Background: The reporting of machine learning (ML) prognostic and diagnostic modeling studies is often inadequate, making it difficult to understand and replicate such studies. To address this issue, multiple consensus and expert reporting guidelines for ML studies have been published. However, these guidelines cover different parts of the analytics lifecycle, and individually, none of them provide a complete set of reporting requirements.
Objective: We aimed to consolidate the ML reporting guidelines and checklists in the literature to provide reporting items for prognostic and diagnostic ML in in-silico and shadow mode studies.
Methods: We conducted a literature search that identified 192 unique peer-reviewed English articles that provide guidance and checklists for reporting ML studies. The articles were screened by their title and abstract against a set of 9 inclusion and exclusion criteria. Articles that were filtered through had their quality evaluated by 2 raters using a 9-point checklist constructed from guideline development good practices. The average κ was 0.71 across all quality criteria. The resulting 17 high-quality source papers were defined as having a quality score equal to or higher than the median. The reporting items in these 17 articles were consolidated and screened against a set of 6 inclusion and exclusion criteria. The resulting reporting items were sent to an external group of 11 ML experts for review and updated accordingly. The updated checklist was used to assess the reporting in 6 recent modeling papers in JMIR AI. Feedback from the external review and initial validation efforts was used to improve the reporting items.
Results: In total, 37 reporting items were identified and grouped into 5 categories based on the stage of the ML project: defining the study details, defining and collecting the data, modeling methodology, model evaluation, and explainability. None of the 17 source articles covered all the reporting items. The study details and data description reporting items were the most common in the source literature, with explainability and methodology guidance (ie, data preparation and model training) having the least coverage. For instance, a median of 75% of the data description reporting items appeared in each of the 17 high-quality source guidelines, but only a median of 33% of the data explainability reporting items appeared. The highest-quality source articles tended to have more items on reporting study details. Other categories of reporting items were not related to the source article quality. We converted the reporting items into a checklist to support more complete reporting.
Conclusions: Our findings supported the need for a set of consolidated reporting items, given that existing high-quality guidelines and checklists do not individually provide complete coverage. The consolidated set of reporting items is expected to improve the quality and reproducibility of ML modeling studies.
Prognostic and diagnostic studies that train and apply machine learning (ML) models on health data often fail to adhere to minimal reporting standards [, ], with inadequate details on model development and evaluation, and fail to fully cover sources of bias [ - ]. Transparent reporting on the development and application of such models is believed to improve reliability, fairness, and usefulness as well as ethical, legal, and regulatory oversight [ ].
There are many reporting guidelines and checklists that have been developed for health research . Although a recent evaluation of the use of reporting guidelines by peer reviewers was not able to reach a conclusion on their use and utility [ ], one other study found a positive association between reviewer ratings of adherence to reporting guidelines and favorable editorial decisions [ ]. Another study reported a significant positive correlation between adherence to reporting guidelines and citations and between adherence to reporting guidelines and publication in higher impact factor journals [ ]. Furthermore, there is evidence that the completeness and quality of reporting of research studies is associated with the use of reporting guidelines [ - ].
However, ML modeling studies do not often use reporting guidelines developed for statistical models , and reporting deficiencies are being seen in contemporary ML modeling articles [ ]. To address this issue, multiple reporting guidelines specific to ML studies have been developed [ , , - ]. In general, reporting guideline “inflation” can lead to confusion among authors and peer reviewers regarding the appropriate ones to use [ ]. These ML study reporting guidelines overlap but are not the same, with each covering a subset of what can be considered good reporting practice [ ]. They each focus on subsets of a typical analytics workflow without being comprehensive. Some guidelines may be nonspecific to health care (eg, DC-Check [ ]), and others may omit important aspects of ML modeling methodology (eg, the absence of guidance on model tuning and optimization [ , ]). The existence of multiple guidelines may hinder the adoption of good reporting guidelines in general, as this makes it difficult for researchers to determine the most suitable set of guidelines to use for a particular study and for journal editors to consistently prescribe reporting requirements for authors [ ].
In this paper, we consolidated items from current ML reporting guidelines and checklists into a single set. We limited our scope to in-silico studies and those where an ML model is running in shadow mode, as these are necessary first steps in developing ML models that are useful in practice . Given that our items consolidate material from previously published consensus and expert guidelines, they can be used by authors as a checklist to ensure adequate reporting of their studies, by peer reviewers to confirm that important details are included in manuscripts, and by journal editors to ensure that good reporting practices are applied and applied consistently across articles and journals.
The objective of this study was to identify high-quality ML reporting guidelines and checklists from the literature and consolidate them into a set of reporting items. The approach we followed was informed by recommended practices for conducting scoping reviews [, ] and developing reporting guidelines [ , ].
The literature search focused on articles that contained a checklist, a flow diagram, or structured text developed to guide authors on the minimum level of detail to include in research papers reporting findings or a specific aspect of research .
We executed a very broad search query in January 2023 and updated it in June 2023 on PubMed to maximize the capture of published reporting guidelines. The query searched for all articles that contained the term “reporting guideline” with either one of the terms “machine learning” or “artificial intelligence,” that is, “reporting guidelines” AND (“machine learning” OR “artificial intelligence”), limited to English articles published in or after the year 2000. We retrieved 73 articles from the search.
The EQUATOR Network  database was searched for articles on “machine learning” and “artificial intelligence” separately. We also adopted an expert-driven approach by curating recent review articles that presented reporting guidelines, reproducibility guidelines, reviews of guidelines, or critique articles on ML practices in medical or biological studies. Recursively, we also reviewed their respective references that reported informative items of the strengths or weaknesses of ML-based studies in medicine. This enabled us to identify an additional 137 articles.
After removing duplicates, there were 192 articles remaining.
Article Inclusion and Exclusion Criteria
The titles and abstracts of the 192 identified articles were reviewed by one of the authors (WK) and screened according to the inclusion and exclusion criteria presented in.
As illustrated in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) diagram  in , the selection criteria resulted in 27 articles. These were then subjected to a quality assessment, as described in the following section.
Inclusion and exclusion criteria.
- The article was peer reviewed in journals or conferences (ie, no preprints or technical reports were included) unless the article was initially expert curated or recommended.
- The article met the definition of a reporting guideline. We generalized the definition in the study by Schlussel et al [ ] to include articles that were not focused on medical research because there are machine learning reporting guidelines that are domain agnostic that still contain useful guidance.
- The reporting guideline article must be specific to machine learning models (ie, guidelines that were specific to statistical prognostic or diagnostic models were excluded).
- The reporting guideline must be new, an update, or an extension.
- Articles that were exclusive to certain types of data, such as images, unstructured text, and genomic sequences, were excluded. The default type of data assumed in this consolidated guideline is structured data. Although most health data are unstructured [ ] and have significant value when analyzed [ ], an examination of all articles (excluding editorials) published in JMIR AI at the time of writing indicated that 23% involved the analysis of structured data, 11% involved the analysis of images, and 35% involved text. Therefore, to the extent that these numbers are reflective of the current published research in medical ML, the focus on structured data is still relevant to at least one-fifth of that body of work, especially given that reporting guidelines and checklists are targeted at improving the reporting of published articles. Articles covering structured data and other additional data modalities were included (per the item inclusion and exclusion criteria described in the Item Inclusion and Exclusion Criteria section, the reporting items that were not on structured data in these multimodal guideline articles were removed).
- Articles that review or evaluate existing reporting guidelines.
- Articles that call for or make the case for reporting guidelines or better reporting guidelines.
- Articles that describe or report on the use of ML in a particular specialty.
- Articles that describe methods for developing reporting guidelines.
We constructed a checklist to evaluate the quality of reporting guidelines and checklists based on the recommendations in the literature on good practices for guideline development [, , , ]. We only considered criteria that were relevant for our purposes (eg, whether a reporting guideline had a website was not considered a quality criterion that was relevant for our purposes). The quality assessment criteria are listed in .
|Quality criteria||Value, κ (95% CI)||Score, mean (SD)|
|1. The need for guidance and reporting checklist is clearly defined||1a||1.00 (0.00)|
|2. There was a literature review (either in the article or cited) to identify previous relevant guidance and requirements||1a||0.98 (0.10)|
|3. The methods for that previous literature review have been described||0.68 (0.67 to 0.69)||0.37 (0.45)|
|4. Inclusion criteria for the reporting items in the preliminary list were defined||−0.054 (−0.52 to −.05)b||0.94 (0.16)|
|5. A Delphi exercise was performed to define and narrow down the initial reporting item list||0.91 (0.9 to 0.91)||0.28 (0.45)|
|6. The Delphi group was representative (includes, eg, academics, journal editors, policy makers, industry, funders, patients, regulators, REBc members, medical writing professionals, librarians)||0.91 (0.9 to 0.91)||0.28 (0.45)|
|7. A face-to-face meeting was conducted to reach consensus on the items and their definitions among the expert group (virtual meetings that enabled discussions of items were also considered acceptable)||0.53 (0.51 to 0.54)d||0.81 (0.34)|
|8. The checklist was pilot tested||0.4 (0.39 to 0.41)d||0.44 (0.42)|
|9. The checklist and its development methodology were published in a peer-reviewed journal||1a||0.98 (0.10)|
aPerfect agreement between the raters.
bA known behavior of κ is that when the expected agreement is already high, the κ value can be quite low , which is the case here. Alternative statistics have been proposed [ ], but they have less interpretation guidance. Therefore, we reported the proportion of positive agreement (equal to 0.94) and the proportion of negative agreement (equal to 0), as suggested in these circumstances [ ]. The 2 raters had an almost perfect positive agreement.
cREB: Research Ethics Board.
dA third rater (second coauthor of this paper: KEE) reconciled the differences between the 2 raters to obtain a final score on this criterion.
Two independent reviewers, one coauthor (WK) and an independent reviewer, separately evaluated each of the identified 27 articles. The κ statistic was used to evaluate the interrater agreement of the score for each quality criterion, and the results are shown in. The average κ across all criteria was 0.71. Values equal to and above 0.61 are considered to be moderate [ ].
The use of a Delphi method to narrow down the reporting items was not used very often (criteria 5 and 6), and the literature review that was performed was not always clearly documented (criterion 3). Many guideline development efforts have used a form of expert meeting to review the reporting items (criterion 7), although we did not require these to be face to face. Pilot testing of reporting guidelines was performed in less than half the time (criterion 8).
We considered an article to be of high quality if at least 61% of the responses were “yes” when each criterion score was averaged across the 2 raters (after reconciliation, where relevant). This threshold also coincides with the median of the quality score. This threshold resulted in 17 articles that were assigned a quality score≥5.5. The scores for each of the 27 articles and whether they scored high or low are shown in.
As a sensitivity analysis, if a higher threshold score of 7 is used (77% “yes” responses on the quality criteria; this is the mean value), this would have resulted in the exclusion of only 1 item from our final list. Therefore, there was little sensitivity in the 61% to 77% threshold range.
|Label||Article title||Reference||Year||Quality score (overall mean 6, SD 1.66; median 5.5, IQR 2)||Quality assessment|
|A01||Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI||||2022||8.5||High|
|A02||Machine Learning Methods in Health Economics and Outcomes Research—The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force||||2022||4.5||Low|
|A03||Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines)||||2022||5.5||High|
|A04||DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems||||2022||5||Low|
|A05||Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease||||2022||4|
|A06||DOME: recommendations for supervised machine learning validation in biology||||2021||5.5||High|
|A07||Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal||||2021||5||Low|
|A08||Low adherence to existing model reporting guidelines by commonly used clinical prediction models||||2021||7||High|
|A09||The need to separate the wheat from the chaff in medical informatics||||2021||4.5||Low|
|A10||Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare||||2021||5.5||High|
|A11||Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension||||2020||9||High|
|A12||Best practices for authors of healthcare-related artificial intelligence manuscripts||||2020||3.5||Low|
|A13||Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension||||2020||9||High|
|A14||Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness||||2020||5||Low|
|A15||Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist||||2020||5||Low|
|A16||Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council||||2020||5.5||High|
|A17||MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care||||2020||4||Low|
|A18aa||PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies||||2019||9||High|
|A18ba||PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration||||2019||9||High|
|A19||Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View||||2016||7||High|
|A20||Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement||||2015||7||High|
|A21||Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist||||2014||6.5||High|
|A22||Towards better clinical prediction models: seven steps for development and an ABCD for validation||||2014||5.5||High|
|A23||Improving Reproducibility in Machine Learning Research||||2020||6||High|
|A24||The AIMe registry for artificial intelligence in biomedical research||||2021||6||High|
|A25||Recommendations for reporting machine learning analyses in clinical research||||2020||5||Low|
|A26||CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII||||2023||8||High|
|A27||Artificial intelligence in dental research: Checklist for authors, reviewers, readers||||2021||8.5||High|
aThere are 27 articles in total with A18a and A18b presenting the same content.
Item Inclusion and Exclusion Criteria
Reporting items were identified in the 17 articles that made it through the quality assessment. Items in these articles were excluded if they met any of the following criteria:
- The items were not within our defined scope of in-silico and shadow mode studies. For example, the items covered ML model implementation and deployment details, such as how models affected clinical pathways and the requirements for the training and education of clinicians. These may have appeared in articles that discuss ML model evaluation in practice and model deployment exclusively or as part of the entire model development and implementation life cycle.
- They did not pertain to structured data. For example, the items pertained to images or text. This sometimes occurred in articles that covered multiple data modalities.
- The items described detailed technical reproducibility requirements, such as guidelines for organizing and documenting code, preparation of virtual machines and Docker containers, and documentation of computational environments were excluded.
- The items pertained to the methodology used for the collection of the training data used in the study. The data sets used in model training may have been collected using different prospective or retrospective designs that may have been controlled. There are additional reporting requirements specific to these designs, such as randomized controlled trials, which would complement the guidelines described in this paper [ , , , ].
- Items that were specific to the reporting of theoretical results and mathematical proofs pertaining to ML models were excluded.
- The reporting guidelines presented in this paper are intended to reflect best practices today rather than ideal practices that would be challenging for contemporary researchers to meet (eg, because the methodological issue remains in the formative stages of exploration and development). Any reporting items that were deemed formative were excluded.
Note that the abovementioned exclusions pertain to items and not articles. For example, guideline articles such as DECIDE-AI (Developmental and Exploratory Clinical Investigations of Decision support systems driven by Artificial Intelligence), CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence), and SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence) discuss the use of clinical trial designs to evaluate ML models. The specific reporting items that pertain to the conduct of clinical trials were not considered, but other items that were specific to the modeling task were included in our review.
This set of items was converted into a checklist that can be used to ensure that all relevant information has been reported.
Expert Review of Reporting Items or the Checklist
The resultant checklist was sent to 11 members of the JMIR AI editorial board for an independent review. They were asked to comment on the clarity of the definitions and levels of granularity of the reporting items. They were also invited to identify any significant omissions that were not covered by the consolidated list and any challenges that they foresee in its application. The feedback from the editorial board was incorporated into a revision of the reporting items and checklist in this paper.
Six prognostic or diagnostic ML studies that were published in JMIR AI were identified, and the resulting items were used to assess whether the authors reported the specific details. The perspective of this assessment was as a reviewer and was not intended to score the papers on adherence to our reporting items. This was performed by one of the authors (KEE). The result of that effort was used to further revise the item definitions, item descriptions, and checklist to ensure that they can be applied in practice.
Presenting the Consolidated Reporting Items or Checklist
During the presentation of the consolidated reporting items and checklist, a number of general principles were followed in their presentation to ensure that they would be practically useful and within our scope:
- Some of the source articles that we draw from provide methodology recommendations (ie, explanation and elaboration of the items). We limited our focus to the information that needs to be reported only while keeping methodology recommendations to a minimum (eg, to illustrate a type of reporting). Methodology guidance is outside the scope of this paper.
- Given that this is a consolidation, new items were not introduced. Therefore, all the items are derived from existing recommendations to ensure consistency with the literature and the objectives of the study.
- Our consolidated reporting items and checklist were categorized into 5 groups according to an analytics workflow consistent with standard process models for data mining [ ], including describing the study details and problem being addressed, the data, the modeling approach, performance evaluation, and model interpretation.
We identified 37 reporting items from 17 high-quality reporting guideline articles. The articles were published between 2014 and 2023, and most articles were published in the last 3 years.
The percentage of articles that covered each of the categories is shown in. We see that the median percentage of “data description” articles that appeared in source articles was 75%, but only a median of 33% of the “model explainability” items appeared in a source article. This table indicates the coverage of each category in the ML reporting guidelines literature and illustrates the importance of developing a consolidated set of reporting items across this body of work.
|Description||Values, median (range; %)a||Values, mean (SD; %)a|
|Data description||75 (50-100)||74 (14)|
|Study details||70 (10-100)||63 (29)|
|Model evaluation||67 (17-83)||63 (17)|
|Methodology||40 (20-90)||44 (18)|
|Model explainability||33 (0-100)||39 (32)|
aThese values are rounded to the nearest integer.
Although the items assume that the observations pertain to patients, they can also pertain to providers or administrators, depending on the study. Furthermore, the following order of categories follows a theoretical workflow sequence for a study; however, this may differ from how the information is actually presented in an article or a report.
The reporting items and checklist are described in terms of the information that needs to be documented in a study report. They are defined assuming a single ML model that is being developed and evaluated, but in practice, multiple models may be part of the same study, and the items should be generalized accordingly.
In subsequent sections, we present reporting items grouped into the 5 categories.
Category 1: Study Details
A mapping of the category 1 items to the articles is presented in. Here, we can see that the article with the highest coverage for this category is A13, with all items included (100%), and articles A23 and A06 have the smallest coverage where only 1 item from this category is mentioned (1/10, 10%). In addition, item 1.1 is mentioned the most in 88% (15/17) of the source articles and item 1.6 is mentioned the least in only 41% (7/17) of the source articles. Article A13 had a high-quality score, whereas articles A23 and A06 had quality scores that were lower and closer to the median threshold.
|Articles||Quality score||Reporting items||Total (n=10 items), n (%)|
|1.1 (n=15, 88%)||1.9 (n=13, 76%)||1.8 (n=12, 71%)||1.3 (n=11, 65%)||1.5 (n=11, 65%)||1.2 (n=10, 59%)||1.4 (n=10, 59%)||1.7 (n=10, 59%)||1.10 (n=8, 47%)||1.6 (n=7, 41%)|
aThe articles are ordered vertically by the percentage of items that are covered per article. The items are ordered horizontally by the percentage of items that are covered by a particular article. The quality score for each article is also shown in the second column.
A description of the items in this category is presented in subsequent sections.
Item 1.1: The Medical and Clinical Task of Interest
The focus is on tasks that can be characterized as diagnostic, where they estimate the presence of disease or condition or prognostic, which forecasts the occurrence of a specific future event.
Item 1.2: The Research Question
The outcomes of interest should be defined. Present factors and insights into what is involved in determining the outcome or in estimating the risk of the end point in the context of the medical and clinical task described earlier . This helps to clarify the relevance, importance, challenges, and contributions of the proposed analysis.
Item 1.3: Current Medical and Clinical Practice
To effectively propose a diagnostic and prognostic model, the current practice and standard of care at the relevant institution or community in general should be understood. To this extent, describe how diagnosis and prognosis are currently established, at what stage of disease, and toward what end point.
Item 1.4: The Known Predictors and Confounders to What Is Being Predicted or Diagnosed
Predictors should be specified with justifications (eg, from the literature). Comorbidities, interventions, and administered treatments are a few of many possible confounders that may be involved in diagnosis or prognosis. Understanding the confounders will enhance the validity of the study and clarify its limitations.
It is also important to ensure that none of the covariates are a proxy for the outcome and would not be available at the time of decision-making, as that would negatively impact the value of the model. For example, if a covariate is an indicator of prescribing a drug indicated for a disease and the model is predicting whether a patient has the disease, then if the covariate is known, we would know the outcome (ie, the model is likely not useful).
Item 1.5: The Overall Study Design
The training, validation, and test data may have been collected through, for example, observational methods, case-control studies, cohort studies, and population studies. They may also be prospective or retrospective studies. Key details of the study design that resulted in the data should be described.
Item 1.6: The Medical Institutional Settings
The setting could be, for example, a hospital, nursing home, or epidemiological center, where the modeling study is conducted, where the ML model will be used (usually these are the same, but not always), and where the data have been or are being collected.
Item 1.7: The Target Patient Population
This is the population that the model is intended to generalize to.
Item 1.8: The Intended Use of the ML Model
Explain the intended use of the ML model as part of the clinical pathway. Describe its purpose and its respective users (eg, medical staff, technicians, patients, and the public). If applicable, clarify how it may be integrated into practice [, ]. Set out the expertise expected of intended users of the ML model.
Item 1.9: Existing Model Performance Benchmarks for This Task
Describe preexisting evidence of using ML methods applied to the medical and clinical task described earlier. If available, summarize the existing model performance (such as the area under the receiver operating characteristic curve results) to establish a benchmark performance for comparison. Where the ground truth is required to interpret the evaluation results, how that ground truth was defined needs to be described.
Item 1.10: Ethical and Other Regulatory Approvals Obtained
Standard reporting requirements include the consent of participants, approvals of ethical agencies, regulatory compliance statements and declarations, certifications required, funding, and conflicts of interest [, , , , ].
Category 2: The Data
There is a need to assess the quality and representativeness of data in the context of the study problem, as relevant to the question and outcomes described in category 1. A mapping of the category 2 items to the articles is presented in.
|Articles||Quality score||Reporting items||Total (n=8 items), n (%)|
|2.4 (n=16, 94%)||2.7 (n=16, 94%)||2.5 (n=15, 88%)||2.6 (n=14, 82%)||2.8 (n=14, 82%)||2.1 (n=11, 65%)||2.2 (n=11, 65%)||2.3 (n=4, 24%)|
aThe articles are ordered vertically by the percentage of items that are covered per article. The items are ordered horizontally by the percentage of items that are covered by a particular article. The quality score for each article is also shown in the second column.
Item 2.1: Inclusion and Exclusion Criteria for the Patient Cohort
This item details the patient inclusion and exclusion criteria, along with any treatments administered. Additional details on how the cohort is representative of the population would be provided here.
Item 2.2: Methods of Data Collection
Data collection methods can be characterized as retrospective or prospective and by the timeliness and frequency of collection as per the study design (eg, cross-sectional at a single time point or longitudinal at multiple time points). Where relevant, characterize the patient burden of data collection and any data collection challenges. For example, tissue biopsy collection is considered more intrusive than drawing blood samples from a clinical point of view. Intuitively, such issues will impact the availability of data and will influence the reproducibility of results.
Item 2.3: Bias Introduced Due to the Method of Data Collection Used
Potential error or bias may be introduced by the method of collection because clinical practice can lead to undesired bias. For instance, the collection of tissue biopsies from cancer patients can be highly associated with the presence of tumors because a biopsy is collected only when the patient is highly suspected of having a tumor. Conversely, obtaining a biopsy of a tumor in an advanced stage of cancer may trigger undesired tumor growth. Consequently, a higher or lower degree of association between sample collection and the presence of tumors can potentially be observed.
Item 2.4: Data Characteristics
Data characteristics include the number of records or data points (this number may differ from individuals included in the study if there are multiple records per individual, as in a longitudinal data set); dimension (the number of features); type (categorical, ordinal, continuous, and text); level of missingness for each feature (by class for categorical variables); range of respective values; cardinality (for categorical features); units; and relevant mappings and encodings. Where observations were manually classified or labeled, the number of annotators should be specified.
In addition, the authors should report empirical characteristics (or descriptive statistics) on patient data (including demographics and end point outcome characteristics). This can include proportions, central tendency, and variation.
Item 2.5: Methods of Data Transformations and Preprocessing Applied
Transformations include those to normalize or standardize features, for the calculation of derived features, for the use of discretization and cutoffs, and for any embedding layers constructed to handle high-cardinality variables. These should be described including the order in which the transformations are applied where relevant. It should be clarified whether data-informed transformations (eg, dichotomization around a median cutoff) were performed on the training data only, as otherwise data leakage may affect model evaluation results.
Item 2.6: Known Quality Issues With the Data
Quality issues may be indicated by the level of missingness, known systematic or random biases, and interobservation dependencies. Manual data collection steps can introduce quality issues such as mislabeling and poor interobserver agreement for manually coded variables. Quality may also be relative to the analytical method used. For example, if there is an assumption of a normal distribution by a particular analytic technique and the data are heavily skewed, then arguably the data have poor quality for this particular study. Where relevant, quality metrics should be provided to quantify such data problems.
Item 2.7: Sample Size Calculation
To achieve specific performance and stability, the size of the training data set must be sufficiently large. The method of calculating the sample size along with any assumptions made to support the calculation should be described.
Item 2.8: Data Availability
Funding agencies are increasingly requiring that data sets be shared more broadly [- ]. Information about data availability would be part of a study’s data management and data sharing plan for newly generated data sets [ ]. For data that already exist in repositories or from specific data custodians (such as national statistical agencies), information on how to access or request the data should be provided.
Category 3: Methodology
Reporting items in this category are concerned with methodological strategies used during the development of an ML model. A mapping of the category 3 items to the articles is presented in.
|Articles||Quality score||Reporting items||Total (n=10 items), n (%)|
|3.8 (n=14, 82%)||3.1 (n=13, 76%)||3.9 (n=12, 71%)||3.10 (n=11, 65%)||3.2 (n=6, 35%)||3.5 (n=5, 29%)||3.7 (n=5, 29%)||3.3 (n=3, 18%)||3.4 (n=3, 18%)||3.6 (n=3, 18%)|
aThe articles are ordered vertically by the percentage of items that are covered per article. The items are ordered horizontally by the percentage of items that are covered by a particular article. The quality score for each article is also shown in the second column.
Reporting on Activities Performed Before Training the ML Model
The items in the subsequent sections pertain to activities that would be performed during a data preprocessing stage. A particular general concern is data leakage, in which spurious relationships are induced between the covariates and the outcomes.
Item 3.1: Strategies for Handling Missing Data
An ML algorithm may or may not tolerate missingness. Methods for dealing with missingness may remove data records that have missing data (complete case analysis); however, this reduces the sample size and may introduce bias. Alternatively, the detrimental effect of missing data can be minimized by selecting features that avoid it or by imputing the missing values needed for the analysis . Each approach has advantages and disadvantages [ - ]. Reporting the strategies used in the study will enable the assessment of how missingness impacts the representativeness of concepts in the data.
Item 3.2: Strategies for Addressing Class Imbalance
Class imbalance is often common in medical data, and in many cases, it can be severe (ie, the minority class is <10% of the data ). Reporting the presence and magnitude of class imbalance and how and when it was dealt with is important to allow for the proper evaluation of the study methodology and results.
Item 3.3: Strategies for Reducing Dimensionality of Data
If used in the study, describe methods that optimize the number of features, such as principal component analysis, and feature selection. Feature selection should be performed on the training data, and the resulting features should be reused in the test data .
Item 3.4: Strategies for Handling Outliers
Sources of outliers in the data include input errors, corruption, and true outlier measurements. Many methods have been proposed to detect and deal with outliers. Some ML algorithms can deal with outliers by assigning lower weights to them, but sometimes, outliers are removed from the training data. These strategies should be reported.
Item 3.5: Strategies for Data Augmentation
In some cases, synthetic data are generated to increase the size of the available data for reasons ranging from class balancing, increasing data diversity, to intentionally adding noise—possibly to counter overfitting. If used, it is necessary to discuss the objectives, rationale, methods, and impact on the ML algorithm used. Furthermore, reporting the potential impact of augmentation on prediction performance and interpretation is recommended.
Item 3.6: Strategies for Model Pretraining
Transfer learning involves transferring knowledge from training the model on another data set and then continuing to train the resulting model on the data at hand to solve the new problem. For instance, a model may be pretrained on a completely different data set, or it can be pretrained on different training data collected as part of this study. In either situation, report on the rationale, methodology, and similarity between data sets and results.
Reporting on Model Training
The following items pertain to activities involved in the training of an ML model.
Item 3.7: The Rationale for Selecting the ML Algorithm
The rationale for selecting the algorithms should be presented, especially when compared with alternatives. Discuss the rationale and justification for why the ML model is useful for solving the problem at hand rather than a traditional statistical model (eg, why is a logistic regression model not sufficient). This may be justified by the results from previous studies.
The choice of algorithms can also be based on how well their requirements are met. For example, an artificial neural network will require a substantial number of training examples and may not be suited for problems with limited data.
Item 3.8: The Method of Evaluating Model Performance During Training
To avoid optimism in model error estimates, appropriate partitioning, cross-validation, or bootstrapping methods can be used. The parameters (eg, proportion of train or test split, number of bootstrap iterations, or number of folds) need to be reported.
Item 3.9: The Method Used for Hyperparameter Tuning
Define the range of possible values evaluated for all hyperparameters, present the method used to select or optimize hyperparameter values (eg, grid search or Bayesian optimization), and describe any cross-validation strategy used during the tuning phase. In addition, define the optimization metric used for model tuning (eg, log loss and mean squared error). It is important to describe the behavior of the performance metrics and to clarify and motivate their use and interpretability in the context of the study.
Item 3.10: Model’s Output Adjustments
Models can still be adjusted after training but before testing. For example, a classification threshold can be adjusted and the classification scores may be calibrated. Report on whether such modifications took place and describe how and why they were made. For example, if the model was designed to produce a pseudoprobability of classification, then calibration may be necessary to ensure proper probability estimates (eg, when using ensemble learning or balancing otherwise imbalanced data). In this case, details on the method of calibration will support the understanding of the results.
Category 4: Model Evaluation
This category is focused on ML model performance evaluation and its reporting. A mapping of the category 4 items to the articles is presented in.
|Articles||Quality score||Reporting items||Total (n=6 items), n (%)|
|4.3 (n=16, 94%)||4.5 (n=16, 94%)||4.1 (n=13, 76%)||4.4 (n=10, 59%)||4.2 (n=7, 41%)||4.6 (n=2, 12%)|
Item 4.1: Performance Metrics Used to Evaluate the Model
Contextually appropriate performance metrics and their parameters should be used and reported. For example, a decision threshold needs to be reported for classification performance metrics that are dependent on a threshold, such as when a probability is predicted and sensitivity is computed from that. Alternatively, the Brier score can be used for evaluating the goodness of estimated probabilities. Multiple metrics should be reported to provide a more complete understanding of model performance under different use scenarios.
Item 4.2: The Cost or Consequence of Errors
Discuss the consequential effect of potential errors made by an ML model. We encourage the use of specific performance metrics designed to support the discussion. For instance, the false-negative rate of a diagnostic model may result in a missed diagnosis, which may be detrimental for some patients. Similarly, the false-positive rate of a prognostic model may lead to subjecting some patients to potentially intrusive, let alone unsafe, interventions unnecessarily. Cost curves  offer a comprehensive analysis for optimizing the operating conditions of a classifier and can also be used for regression models.
Item 4.3: The Results of Internal Validation
The results of the error estimation evaluations on all of the metrics should be reported. The results should be reported on the training and test data sets, where applicable. If multiple modeling techniques or models are being compared, appropriate statistical comparisons should be reported.
Item 4.4: The Final Model Hyperparameters
The hyperparameters of the final model that will be used in practice should be reported, even if they are the default values of the analytics software used.
Item 4.5: Model Evaluation on an External Data Set
An external data set can be a hold-out from the original sample used in the study, although that would be a weak form of external validation. A data set from the same facility at a later point in time or from a different facility or facilities would provide stronger external validation. The performance metrics from internal and external validation should be compared.
Item 4.6: Characteristics Relevant for Detecting Data Shift and Drift
Data distributions change over time that may negatively affect model performance in the future or in different settings . To be able to detect data shifts and drifts, a baseline characterization of the training population and decision-making processes is needed. Items 1.3 (current practices) and 2.4 (data characteristics) may be sufficient, with the evaluation results interpreted within that context. However, any additional data and process details that may be relevant for future use and deployment should be provided. For example, the performance of a model may be dependent on a particular pattern of care or specific referral criteria at a facility, and this should be specified if the dependence is known a priori.
Category 5: Model Explainability
Reporting on items in this category aims to substantiate the ease or difficulty in which a human is able to comprehend how the proposed model produces its output [, ]. This will demonstrate the comprehensibility or the lack thereof of factors that drive the decision-making process in the proposed model and can potentially enhance the model’s credibility and trustworthiness. A mapping of the category 5 items to the articles is presented in .
|Articles||Quality score||Reporting items||Total (n=3 items), n (%)|
|5.3 (n=8, 47%)||5.1 (n=6, 35%)||5.2 (n=6, 35%)|
Item 5.1: The Most Important Features and How They Relate to the Outcomes
An important aspect of explainability for prognostic and diagnostic tools is feature importance. Describe the methods used to determine feature importance and document how the final ranking of features and their scores (where relevant) are determined. Furthermore, discuss the relative or absolute impact of each feature on the outcomes and, if possible, the functional form of this relationship. Common methods include Shapley Additive Explanations and partial dependence plots.
Item 5.2: Plausibility of Model Outputs
The resulting model outputs need to be clinically plausible according to domain experts and consistent with the current understanding. This can be demonstrated with reference to existing literature or by validation from domain experts. Explanations for deviations should be provided.
Item 5.3: Interpretation of the Model’s Results by an End User
Reporting how knowledge is communicated will help define the complexity of the interaction between domain experts and the ML model, which in turn will promote the acceptance of the proposed model. For example, when predicting whether a patient will develop complications in the first 48 hours after lung resection surgery, the model can be made to present calibrated probabilities along with a simple calculation of CIs, which are commonly used and understood by surgeons to assess risk.
Coverage of Reporting Items
We examined the coverage of the reporting items in each article in each of the reporting categories.presents the proportions of reporting items discussed in each article. The last bar on the right shows the median proportions of proposed items that were discussed in the articles for each category. Although the consolidated items have a higher focus on describing the “study details” (median 70%) and “data description” (median 75%), reporting on model explainability and details of methodology are lacking with a median percentage of 33% and 40%, respectively, being covered in those articles.
We also saw that some source articles, such as A06, A08, A23, and A24, did not cover all 5 categories that we defined. This demonstrates that the coverage of reporting items and categories is not uniform across source articles.
We also examined how often reporting items in this paper were discussed in the 17 high-quality articles.shows the list of reporting items and the number of articles that discuss them. We can see that some reporting items shown toward the top are rarely discussed and, not surprisingly, belong to the methodology category. Handling missing data and model performance evaluation during training are the most common methodology items.
The bottom of the figure with the items appearing in most articles is dominated by data description items. Sample size calculations and providing information on data characteristics are the most common in that category. Internal and external model evaluation items also appeared in almost all the articles. This is not surprising, because that is a basic requirement for in-silico studies.
shows the rank correlation between article coverage and article quality score for the 17 articles. We can see that there is a strong and significant positive relationship only for the category “study details.” This suggests that the highest-quality source papers focused more on defining reporting items for the study details compared with articles with quality scores closer to the median. Coverage of the reporting items was not associated with article quality for the other categories, although the negative association for the “methodology” and “model evaluation” categories suggests that the highest-quality source papers focused less on those items.
|Category||Spearman correlation, ρ||P value|
Multiple guidelines have been developed for ML prognostic and diagnostic modeling studies [, , , , , , - , , , ]. However, having multiple and overlapping guidelines can be confusing for researchers, journal editors, and reviewers, as it is less obvious which guidelines should be adhered to, and this may create friction in the adoption of good reporting practices. Such a state of affairs is not desirable, as there is evidence that the application of reporting guidance has benefits in higher-quality research and positive editorial decisions and increased citations.
In this paper, we developed a consolidated list of 37 reporting items for ML prognostic and diagnostic modeling studies conducted in silico or in shadow mode. The consolidated items were obtained from 17 high-quality source articles and represented consensus and expert guidance to ensure that these types of ML studies are adequately reported upon. The items were reviewed by independent experts and were applied in a review of a sample of JMIR AI articles, both of which informed further refinements and clarifications.
The results insupport the need for our consolidated reporting items and checklist, since none of the source articles covered all 37 items presented. Some of our reporting items were covered in only 2 source articles, with the highest coverage being reporting items that appeared in 16 of the 17 source articles.
It was found that descriptions of the data and reporting study details were the most common categories in the source literature, whereas model explainability and methodological guidance (ie, data preparation descriptions and descriptions of model training) had the least coverage in the ML reporting guidelines literature overall. Using our checklist can help ensure that explainability and methodological considerations are documented appropriately. However, current explainability methods have come under criticism in terms of their value and the guarantees that they can provide .
The items can be used as a checklist (see the checklist in), where the authors can indicate whether they have reported the relevant information. If it is reported, then the location in the article can be provided. If an item is not applicable, some reasoning can be provided. The use of a checklist will ensure consistency and that authors consider all reporting items.
Satisfying the reporting items does not necessarily mean that prognostic and diagnostic ML modeling study articles will increase in length. Some of the information may already be published elsewhere; therefore, the reporting items can be satisfied by citing other work. Any additional burden on authors will arguably be offset by the noted benefits to authors and readers, as well as by enhancing the transparency and reproducibility of ML studies.
Although we used a broad search of the literature to identify and select papers for our consolidated guidelines and checklist, we acknowledge that some may have been missed. However, any impact from missed papers is likely to be limited. In addition, a recent systematic review of reporting guidelines for prediction models that included ML within its scope did not identify any articles that were not within our scope .
Because this was a consolidation effort, whatever omissions exist in the literature, for example, will also be evident in our consolidated items. A design decision in this study was not to add new items that were not directly derived from the 17 source articles to ensure that we do not introduce a new source of bias in the results.
While we accounted for interrater reliability in the assessment of source article quality, the process of consolidating guidelines does involve subjectivity in the application of the inclusion and exclusion criteria. The external review by the JMIR AI editorial board helped ensure face validity to ameliorate some individual subjectivity.
The scope of our reporting items has been on structured data. ML modeling for other data types, such as text and images, was outside the scope of this study. Although this limitation excludes many studies, a large number of studies are still covered by our reporting guidelines and checklist. As noted earlier, approximately one-fifth of the noneditorial papers published in JMIR AI were on structured data. Future work should extend these consolidated reporting items and checklist to other data modalities (such as text and images).
Another scoping decision for the reporting items in this paper is that they focused only on in-silico and shadow mode studies. These are necessary first steps in developing and validating ML models. Future research can extend this work and cover the deployment and monitoring of prognostic and diagnostic ML models into practice.
This paper does not provide an explanation and elaboration for each reporting item. This information and references to examples are available in the source articles, as indicated in the mapping tables, and were therefore not repeated here.
The checklist that is provided in this paper () is intended to be used by authors to ensure that all relevant information for prognostic and diagnostic studies is reported. Extending the reporting checklist to an evaluation instrument would require additional effort to develop scoring items and scoring schemes [ , ]. This would allow the community to assess and track changes in reporting quality over time and evaluate study quality in meta-analysis projects.
The authors thank Samer El Kababji, Lamin Juwara, and Jeff Gilchrist for reviewing earlier versions of this paper; Elizabeth Jonker, who performed the quality ratings; and David Moher, who provided very helpful methodology suggestions. They also wish to thank members of the JMIR AI editorial board (at the time of writing: Bradley Malin, Munmun de Chaudhury, Jimeng sun, Danica Xiao, Hongfang Liu, Gang Luo, Yuankai Huo, Alessandro Blasimme, Doug Manuel, Janice Branson, and Jean Louise Raisaro) for providing feedback on an earlier version of the reporting items and checklist.
This work was funded by the Precision Child and Youth Mental Health funding initiative at the Children’s Hospital of Eastern Ontario Research Institute, the Canada Research Chairs program through the Canadian Institutes of Health Research, and a Discovery Grant RGPIN-2022-04811 from the Natural Sciences and Engineering Research Council of Canada.
Conflicts of Interest
KEE is a cofounder and has financial interests in Replica Analytics Ltd, a spin-off from his research laboratory that develops synthetic data generation software. All other authors declare no other conflicts of interest.
Author checklist.DOCX File , 64 KB
- Cabitza F, Campagner A. The need to separate the wheat from the chaff in medical informatics: introducing a comprehensive checklist for the (self)-assessment of medical AI studies. Int J Med Inform 2021 Sep;153:104510 [https://hdl.handle.net/10281/324843] [CrossRef] [Medline]
- Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health 2019 Oct;1(6):e271-e297 [https://linkinghub.elsevier.com/retrieve/pii/S2589-7500(19)30123-2] [CrossRef] [Medline]
- Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ, SPIRIT-AICONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. BMJ 2020 Sep 09;370:m3210 [http://www.bmj.com/lookup/pmidlookup?view=long&pmid=32907797] [CrossRef] [Medline]
- Shelmerdine SC, Arthurs OJ, Denniston A, Sebire NJ. Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare. BMJ Health Care Inform 2021 Aug;28(1):e100385 [https://informatics.bmj.com/lookup/pmidlookup?view=long&pmid=34426417] [CrossRef] [Medline]
- Ibrahim H, Liu X, Denniston AK. Reporting guidelines for artificial intelligence in healthcare research. Clin Exp Ophthalmol 2021 Jul;49(5):470-476 [CrossRef] [Medline]
- Crossnohere NL, Elsaid M, Paskett J, Bose-Brill S, Bridges JF. Guidelines for artificial intelligence in medicine: literature review and content analysis of frameworks. J Med Internet Res 2022 Aug 25;24(8):e36823 [https://www.jmir.org/2022/8/e36823/] [CrossRef] [Medline]
- Simera I, Moher D, Hoey J, Schulz KF, Altman DG. A catalogue of reporting guidelines for health research. Eur J Clin Invest 2010 Jan;40(1):35-53 [CrossRef] [Medline]
- Hirst A, Altman DG. Are peer reviewers encouraged to use reporting guidelines? A survey of 116 health research journals. PLoS One 2012 Apr 27;7(4):e35621 [https://dx.plos.org/10.1371/journal.pone.0035621] [CrossRef] [Medline]
- Botos J. Reported use of reporting guidelines among authors, editorial outcomes, and reviewer ratings related to adherence to guidelines and clarity of presentation. Res Integr Peer Rev 2018 Sep 27;3(1):7 [https://researchintegrityjournal.biomedcentral.com/articles/10.1186/s41073-018-0052-4] [CrossRef] [Medline]
- Stevanovic A, Schmitz S, Rossaint R, Schürholz T, Coburn M. CONSORT item reporting quality in the top ten ranked journals of critical care medicine in 2011: a retrospective analysis. PLoS One 2015 May 28;10(5):e0128061 [https://dx.plos.org/10.1371/journal.pone.0128061] [CrossRef] [Medline]
- Turner L, Shamseer L, Altman DG, Schulz KF, Moher D. Does use of the CONSORT statement impact the completeness of reporting of randomised controlled trials published in medical journals? A Cochrane review. Syst Rev 2012 Nov 29;1:60 [https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/2046-4053-1-60] [CrossRef] [Medline]
- Smidt N, Rutjes AW, van der Windt DA, Ostelo RW, Bossuyt PM, Reitsma JB, et al. The quality of diagnostic accuracy studies since the STARD statement: has it improved? Neurology 2006 Sep 12;67(5):792-797 [https://core.ac.uk/reader/15461168?utm_source=linkout] [CrossRef] [Medline]
- Wynne KE, Simpson BJ, Berman L, Rangel SJ, Grosfeld JL, Moss RL. Results of a longitudinal study of rigorous manuscript submission guidelines designed to improve the quality of clinical research reporting in a peer-reviewed surgical journal. J Pediatr Surg 2011 Jan;46(1):131-137 [CrossRef] [Medline]
- Hopewell S, Dutton S, Yu LM, Chan AW, Altman DG. The quality of reports of randomised trials in 2000 and 2006: comparative study of articles indexed in PubMed. BMJ 2010 Mar 23;340(mar23 1):c723 [https://europepmc.org/abstract/MED/20332510] [CrossRef] [Medline]
- Prady SL, Richmond SJ, Morton VM, Macpherson H. A systematic evaluation of the impact of STRICTA and CONSORT recommendations on quality of reporting for acupuncture trials. PLoS One 2008 Feb 13;3(2):e1577 [https://dx.plos.org/10.1371/journal.pone.0001577] [CrossRef] [Medline]
- Moher D, Jones A, Lepage L, CONSORT Group (Consolidated Standards for Reporting of Trials). Use of the CONSORT statement and quality of reports of randomized trials: a comparative before-and-after evaluation. JAMA 2001 Apr 18;285(15):1992-1995 [CrossRef] [Medline]
- Plint AC, Moher D, Morrison A, Schulz K, Altman DG, Hill C, et al. Does the CONSORT checklist improve the quality of reports of randomised controlled trials? A systematic review. Med J Aust 2006 Sep 04;185(5):263-267 [CrossRef] [Medline]
- Andaur Navarro CL, Damen JA, Takada T, Nijman SW, Dhiman P, Ma J, et al. Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review. BMC Med Res Methodol 2022 Jan 13;22(1):12 [https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01469-6] [CrossRef] [Medline]
- El Emam K, Klement W, Malin B. Reporting and methodological observations on prognostic and diagnostic machine learning studies. JMIR AI. Preprint posted online April 28, 2023 2023 [https://ai.jmir.org/2023/1/e47995/] [CrossRef]
- Lu JH, Callahan A, Patel BS, Morse KE, Dash D, Pfeffer MA, et al. Assessment of adherence to reporting guidelines by commonly used clinical prediction models from a single vendor: a systematic review. JAMA Netw Open 2022 Aug 01;5(8):e2227779 [https://europepmc.org/abstract/MED/35984654] [CrossRef] [Medline]
- Olczak J, Pavlopoulos J, Prijs J, Ijpma FF, Doornberg JN, Lundström C, et al. Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal. Acta Orthop 2021 Oct;92(5):513-525 [https://www.tandfonline.com/doi/full/10.1080/17453674.2021.1918389] [CrossRef] [Medline]
- Padula WV, Kreif N, Vanness DJ, Adamson B, Rueda JD, Felizzi F, et al. Machine learning methods in health economics and outcomes research-the PALISADE checklist: a good practices report of an ISPOR task force. Value Health 2022 Jul;25(7):1063-1080 [https://linkinghub.elsevier.com/retrieve/pii/S1098-3015(22)00191-7] [CrossRef] [Medline]
- Loftus TJ, Tighe PJ, Ozrazgat-Baslanti T, Davis JP, Ruppert MM, Ren Y, et al. Ideal algorithms in healthcare: explainable, dynamic, precise, autonomous, fair, and reproducible. PLOS Digit Health 2022;1(1):e0000006 [https://europepmc.org/abstract/MED/36532301] [CrossRef] [Medline]
- Weaver CG, Basmadjian RB, Williamson T, McBrien K, Sajobi T, Boyne D, et al. Reporting of model performance and statistical methods in studies that use machine learning to develop clinical prediction models: protocol for a systematic review. JMIR Res Protoc 2022 Mar 03;11(3):e30956 [https://www.researchprotocols.org/2022/3/e30956/] [CrossRef] [Medline]
- Kotecha D, Asselbergs FW, Achenbach S, Anker SD, Atar D, Baigent C, Innovative Medicines Initiative BigData@Heart Consortium, European Society of Cardiology, and CODE-EHR International Consensus Group. CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research. Lancet Digit Health 2022 Oct;4(10):e757-e764 [https://boris.unibe.ch/id/eprint/172658] [CrossRef] [Medline]
- Seedat N, Imrie F, van der Schaar M. DC-Check: a data-centric AI checklist to guide the development of reliable machine learning systems. arXiv. Preprint posted online November 9, 2022 2022 [https://arxiv.org/abs/2211.05764] [CrossRef]
- Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, DECIDE-AI expert group. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ 2022 May 18;377:e070904 [http://www.bmj.com/lookup/pmidlookup?view=long&pmid=35584845] [CrossRef] [Medline]
- Moher D. Reporting guidelines: doing better for readers. BMC Med 2018 Dec 14;16(1):233 [https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-018-1226-0] [CrossRef] [Medline]
- Peters MD, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc 2015 Sep;13(3):141-146 [CrossRef] [Medline]
- Peters MD, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth 2020 Oct;18(10):2119-2126 [CrossRef] [Medline]
- Moher D, Schulz KF, Simera I, Altman DG. Guidance for developers of health research reporting guidelines. PLoS Med 2010 Feb 16;7(2):e1000217 [https://dx.plos.org/10.1371/journal.pmed.1000217] [CrossRef] [Medline]
- Schlussel MM, Sharp MK, de Beyer JA, Kirtley S, Logullo P, Dhiman P, et al. Reporting guidelines used varying methodology to develop recommendations. J Clin Epidemiol (Forthcoming) 2023 Mar 24;159:246-256 [https://linkinghub.elsevier.com/retrieve/pii/S0895-4356(23)00069-0] [CrossRef] [Medline]
- Simera I, Moher D, Hoey J, Schulz KF, Altman DG. The EQUATOR Network and reporting guidelines: helping to achieve high standards in reporting health research studies. Maturitas 2009 May 20;63(1):4-6 [CrossRef] [Medline]
- Kong HJ. Managing unstructured big data in healthcare system. Healthc Inform Res 2019 Jan;25(1):1-2 [https://europepmc.org/abstract/MED/30788175] [CrossRef] [Medline]
- Tayefi M, Ngo P, Chomutare T, Dalianis H, Salvi E, Budrionis A, et al. Challenges and opportunities beyond structured data in analysis of electronic health records. Wiley Interdiscip Rev Comput Stat 2021 Feb 14;13(6):e1549 [https://wires.onlinelibrary.wiley.com/doi/full/10.1002/wics.1549] [CrossRef]
- Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021 Mar 29;372:n71 [http://www.bmj.com/lookup/pmidlookup?view=long&pmid=33782057] [CrossRef] [Medline]
- Appraisal of guidelines for research and evaluation (AGREE) II instrument. National Collaborating Centre for Methods and Tools. 2017. URL: https://www.nccmt.ca/knowledge-repositories/search/100 [accessed 2023-06-12]
- Arundel C, James S, Northgraves M, Booth A. Study reporting guidelines: how valid are they? Contemp Clin Trials Commun 2019 Mar 11;14:100343 [https://linkinghub.elsevier.com/retrieve/pii/S2451-8654(18)30203-5] [CrossRef] [Medline]
- Feinstein AR, Cicchetti DV. High agreement but low kappa: I. The problems of two paradoxes. J Clin Epidemiol 1990;43(6):543-549 [CrossRef] [Medline]
- Shankar V, Bangdiwala SI. Observer agreement paradoxes in 2x2 tables: comparison of agreement measures. BMC Med Res Methodol 2014 Aug 28;14(1):100 [https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-100] [CrossRef] [Medline]
- Cicchetti DV, Feinstein AR. High agreement but low kappa: II. Resolving the paradoxes. J Clin Epidemiol 1990;43(6):551-558 [CrossRef] [Medline]
- McHugh ML. Interrater reliability: the kappa statistic. Biochem Med 2012;22(3):276-282 [CrossRef]
- Jha AK, Doolan D, Grandt D, Scott T, Bates DW. The use of health information technology in seven nations. Int J Med Inform 2008 Dec;77(12):848-854 [CrossRef] [Medline]
- van Smeden M, Heinze G, Van Calster B, Asselbergs FW, Vardas PE, Bruining N, et al. Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease. Eur Heart J 2022 Aug 14;43(31):2921-2930 [https://europepmc.org/abstract/MED/35639667] [CrossRef] [Medline]
- Walsh I, Fishman D, Garcia-Gasulla D, Titma T, Pollastri G, ELIXIR Machine Learning Focus Group, et al. DOME: recommendations for supervised machine learning validation in biology. Nat Methods 2021 Oct;18(10):1122-1127 [https://air.unimi.it/handle/2434/860286] [CrossRef] [Medline]
- Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK, SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 2020 Sep 09;26(9):1364-1374 [https://europepmc.org/abstract/MED/32908283] [CrossRef] [Medline]
- Kakarmath S, Esteva A, Arnaout R, Harvey H, Kumar S, Muse E, et al. Best practices for authors of healthcare-related artificial intelligence manuscripts. NPJ Digit Med 2020 Oct 16;3(1):134 [https://doi.org/10.1038/s41746-020-00336-w] [CrossRef] [Medline]
- Vollmer S, Mateen BA, Bohner G, Király FJ, Ghani R, Jonsson P, et al. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ 2020 Mar 20;368:l6927 [http://www.bmj.com/lookup/pmidlookup?view=long&pmid=32198138] [CrossRef] [Medline]
- Norgeot B, Quer G, Beaulieu-Jones BK, Torkamani A, Dias R, Gianfrancesco M, et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med 2020 Sep;26(9):1320-1324 [https://europepmc.org/abstract/MED/32908275] [CrossRef] [Medline]
- Sengupta PP, Shrestha S, Berthon B, Messas E, Donal E, Tison GH, et al. Proposed requirements for cardiovascular imaging-related machine learning evaluation (PRIME): a checklist: reviewed by the American College of Cardiology Healthcare Innovation Council. JACC Cardiovasc Imaging 2020 Sep;13(9):2017-2035 [https://linkinghub.elsevier.com/retrieve/pii/S1936-878X(20)30636-7] [CrossRef] [Medline]
- Hernandez-Boussard T, Bozkurt S, Ioannidis JP, Shah NH. MINIMAR (MINimum Information for Medical AI Reporting): developing reporting standards for artificial intelligence in health care. J Am Med Inform Assoc 2020 Dec 09;27(12):2011-2015 [https://europepmc.org/abstract/MED/32594179] [CrossRef] [Medline]
- Wolff RF, Moons KG, Riley RD, Whiting PF, Westwood M, Collins GS, PROBAST Group†. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 2019 Jan 01;170(1):51-58 [https://www.acpjournals.org/doi/abs/10.7326/M18-1376?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub 0pubmed] [CrossRef] [Medline]
- Moons KG, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med 2019 Jan 01;170(1):W1-33 [CrossRef]
- Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res 2016 Dec 16;18(12):e323 [https://www.jmir.org/2016/12/e323/] [CrossRef] [Medline]
- Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD). Ann Intern Med 2015 May 19;162(10):735-736 [CrossRef]
- Moons KG, de Groot JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med 2014 Oct 14;11(10):e1001744 [https://dx.plos.org/10.1371/journal.pmed.1001744] [CrossRef] [Medline]
- Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J 2014 Aug 01;35(29):1925-1931 [https://europepmc.org/abstract/MED/24898551] [CrossRef] [Medline]
- Pineau J, Vincent-Lamarre P, Sinha K, Larivière V, Beygelzimer A, d'Alché-Buc F, et al. Improving reproducibility in machine learning research (a report from the NeurIPS 2019 reproducibility program). arXiv. Preprint posted online March 27, 2020 2020 [https://arxiv.org/abs/2003.12206] [CrossRef]
- Matschinske J, Alcaraz N, Benis A, Golebiewski M, Grimm DG, Heumos L, et al. The AIMe registry for artificial intelligence in biomedical research. Nat Methods 2021 Oct;18(10):1128-1131 [https://hdl.handle.net/2268/291731] [CrossRef] [Medline]
- Stevens LM, Mortazavi BJ, Deo RC, Curtis L, Kao DP. Recommendations for reporting machine learning analyses in clinical research. Circ Cardiovasc Qual Outcomes 2020 Oct;13(10):e006556 [https://www.ahajournals.org/doi/abs/10.1161/CIRCOUTCOMES.120.006556?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub 0pubmed] [CrossRef] [Medline]
- Kocak B, Baessler B, Bakas S, Cuocolo R, Fedorov A, Maier-Hein L, et al. CheckList for EvaluAtion of radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 2023 May 04;14(1):75 [https://europepmc.org/abstract/MED/37142815] [CrossRef] [Medline]
- Schwendicke F, Singh T, Lee JH, Gaudin R, Chaurasia A, Wiegand T, IADR e-oral health network and the ITU WHO focus group AI for Health. Artificial intelligence in dental research: checklist for authors, reviewers, readers. J Dent 2021 Apr;107:103610 [CrossRef] [Medline]
- McGenity C, Treanor D. Guidelines for clinical trials using artificial intelligence - SPIRIT-AI and CONSORT-AI. J Pathol 2021 Jan;253(1):14-16 [http://liu.diva-portal.org/smash/get/diva2:1504674/FULLTEXT01.pdf] [CrossRef] [Medline]
- Wirth R, Hipp J. CRISP-DM: towards a standard process model for data mining. In: Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining. 1998 Presented at: KDD '98; August 27-31, 1998; New York, NY
- Ethics and governance of artificial intelligence for health: WHO guidance. World Health Organization. 2021 Jun 28. URL: https://www.who.int/publications/i/item/9789240029200 [accessed 2022-08-25]
- Data, software and materials management and sharing policy. Wellcome. 2017. URL: https://wellcome.ac.uk/funding/managing-grant/policy-data-software-materials-management-and-sharing [accessed 2017-09-12]
- Final NIH statement on sharing research data. National Institutes of Health. 2003. URL: https://grants.nih.gov/grants/guide/notice-files/NOT-OD-03-032.html [accessed 2020-06-29]
- Jorgenson LA, Wolinetz CD, Collins FS. Incentivizing a new culture of data stewardship: the NIH policy for data management and sharing. JAMA 2021 Dec 14;326(22):2259-2260 [CrossRef] [Medline]
- Gelman A, Hill J. Missing-data imputation. In: Gelman A, Hill J, editors. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge, MA: Cambridge University Press; 2006.
- Efron B. Missing data, imputation, and the bootstrap. J Am Stat Assoc 2012 Feb 27;89(426):463-475 [https://www.tandfonline.com/doi/abs/10.1080/01621459.1994.10476768] [CrossRef]
- Jadhav A, Pramod D, Ramanathan K. Comparison of performance of data imputation methods for numeric dataset. Appl Artif Intell 2019;33(10):913-933 [https://www.tandfonline.com/doi/full/10.1080/08839514.2019.1637138] [CrossRef]
- Zhao Y, Long Q. Variable selection in the presence of missing data: imputation-based methods. Wiley Interdiscip Rev Comput Stat 2017 Sep;9(5):e1402 [https://europepmc.org/abstract/MED/29085552] [CrossRef] [Medline]
- Leevy JL, Khoshgoftaar TM, Bauder RA, Seliya N. A survey on addressing high-class imbalance in big data. J Big Data 2018 Nov 1;5(1):42 [https://journalofbigdata.springeropen.com/articles/10.1186/s40537-018-0151-6] [CrossRef]
- Smialowski P, Frishman D, Kramer S. Pitfalls of supervised feature selection. Bioinformatics 2010 Feb 01;26(3):440-443 [https://europepmc.org/abstract/MED/19880370] [CrossRef] [Medline]
- Drummond C, Holte RC. Cost curves: an improved method for visualizing classifier performance. Mach Learn 2006 May 8;65(1):95-130 [https://link.springer.com/article/10.1007/s10994-006-8199-5] [CrossRef]
- Hand DJ. Rejoinder: classifier technology and the illusion of progress. Statist Sci 2006 Feb;21(1):30-34 [https://projecteuclid.org/journals/statistical-science/volume-21/issue-1/Rejoinder-Classifier-Technology-and-the-Illusion-of-Progress/10.1214/088342306000000079.full] [CrossRef]
- Belle V, Papantonis I. Principles and practice of explainable machine learning. Front Big Data 2021 Jul 1;4:688969 [https://europepmc.org/abstract/MED/34278297] [CrossRef] [Medline]
- Kempt H, Heilinger JC, Nagel SK. Relative explainability and double standards in medical decision-making. Ethics Inf Technol 2022 Apr 05;24(2):20 [https://link.springer.com/article/10.1007/s10676-022-09646-x] [CrossRef]
- Lu JH, Callahan A, Patel BS, Morse KE, Dash D, Shah NH. Low adherence to existing model reporting guidelines by commonly used clinical prediction models. medRxiv. Preprint posted online July 23, 2021 2021 [https://www.medrxiv.org/content/10.1101/2021.07.21.21260282v1] [CrossRef]
- Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health 2021 Nov;3(11):e745-e750 [https://linkinghub.elsevier.com/retrieve/pii/S2589-7500(21)00208-9] [CrossRef] [Medline]
- Logullo P, MacCarthy A, Kirtley S, Collins GS. Reporting guideline checklists are not quality evaluation forms: they are guidance for writing. Health Sci Rep 2020 Jun 03;3(2):e165 [https://europepmc.org/abstract/MED/32373717] [CrossRef] [Medline]
- Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, Cochrane Bias Methods Group, Cochrane Statistical Methods Group. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ 2011 Oct 18;343(oct18 2):d5928 [https://boris.unibe.ch/id/eprint/7356] [CrossRef] [Medline]
|CONSORT-AI: Consolidated Standards of Reporting Trials–Artificial Intelligence|
|DECIDE-AI: Developmental and Exploratory Clinical Investigations of Decision support systems driven by Artificial Intelligence|
|ML: machine learning|
|PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses|
|SPIRIT-AI: Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence|
Edited by G Eysenbach, T Leung; submitted 05.05.23; peer-reviewed by L Edwards, P McCulloch; comments to author 02.06.23; revised version received 11.07.23; accepted 31.07.23; published 31.08.23Copyright
©William Klement, Khaled El Emam. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.08.2023.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.