Published on in Vol 22 , No 6 (2020) :June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15154, first published .
Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians

Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians

Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians

Journals

  1. Soong T, Ho C. Artificial Intelligence in Medical OSCEs: Reflections and Future Developments. Advances in Medical Education and Practice 2021;Volume 12:167 View
  2. Esteva A, Chou K, Yeung S, Naik N, Madani A, Mottaghi A, Liu Y, Topol E, Dean J, Socher R. Deep learning-enabled medical computer vision. npj Digital Medicine 2021;4(1) View
  3. Sandhu S, Lin A, Brajer N, Sperling J, Ratliff W, Bedoya A, Balu S, O'Brien C, Sendak M. Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study. Journal of Medical Internet Research 2020;22(11):e22421 View
  4. Laudanski K, Shea G, DiMeglio M, Restrepo M, Solomon C. What Can COVID-19 Teach Us about Using AI in Pandemics?. Healthcare 2020;8(4):527 View
  5. Lee C, Samad M, Hofer I, Cannesson M, Baldi P. Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality. npj Digital Medicine 2021;4(1) View
  6. He Q, Du F, Simonse L. A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth. JMIR Medical Informatics 2021;9(4):e23238 View
  7. Choudhury A, Elkefi S, Asan O. Impact of Gender on Doctor-Patient Communication and Emotion: Exploratory Analysis. SSRN Electronic Journal 2020 View
  8. Lonsdale D, Lipman J. Global personalization of antibiotic therapy in critically ill patients. Expert Review of Precision Medicine and Drug Development 2021;6(2):87 View
  9. Li Z, Kitajima K, Hirata K, Togo R, Takenaka J, Miyoshi Y, Kudo K, Ogawa T, Haseyama M. Preliminary study of AI-assisted diagnosis using FDG-PET/CT for axillary lymph node metastasis in patients with breast cancer. EJNMMI Research 2021;11(1) View
  10. Higgins T. Not All Databases Are Created Equal*. Critical Care Medicine 2020;48(12):1891 View
  11. Chaddad A, Kucharczyk M, Cheddad A, Clarke S, Hassan L, Ding S, Rathore S, Zhang M, Katib Y, Bahoric B, Abikhzer G, Probst S, Niazi T. Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review. Cancers 2021;13(3):552 View
  12. Duncker D, Ding W, Etheridge S, Noseworthy P, Veltmann C, Yao X, Bunch T, Gupta D. Smart Wearables for Cardiac Monitoring—Real-World Use beyond Atrial Fibrillation. Sensors 2021;21(7):2539 View
  13. Mehta N, Born K, Fine B. How artificial intelligence can help us ‘Choose Wisely’. Bioelectronic Medicine 2021;7(1) View
  14. Roski J, Maier E, Vigilante K, Kane E, Matheny M. Enhancing trust in AI through industry self-governance. Journal of the American Medical Informatics Association 2021;28(7):1582 View
  15. Asan O, Choudhury A. Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review. JMIR Human Factors 2021;8(2):e28236 View
  16. Voigt I, Inojosa H, Dillenseger A, Haase R, Akgün K, Ziemssen T. Digital Twins for Multiple Sclerosis. Frontiers in Immunology 2021;12 View
  17. Barak-Corren Y, Agarwal I, Michelson K, Lyons T, Neuman M, Lipsett S, Kimia A, Eisenberg M, Capraro A, Levy J, Hudgins J, Reis B, Fine A. Prediction of patient disposition: comparison of computer and human approaches and a proposed synthesis. Journal of the American Medical Informatics Association 2021;28(8):1736 View
  18. Petzold A, Albrecht P, Balcer L, Bekkers E, Brandt A, Calabresi P, Deborah O, Graves J, Green A, Keane P, Nij Bijvank J, Sander J, Paul F, Saidha S, Villoslada P, Wagner S, Yeh E, Aktas O, Antel J, Asgari N, Audo I, Avasarala J, Avril D, Bagnato F, Banwell B, Bar‐Or A, Behbehani R, Manterola A, Bennett J, Benson L, Bernard J, Bremond‐Gignac D, Britze J, Burton J, Calkwood J, Carroll W, Chandratheva A, Cohen J, Comi G, Cordano C, Costa S, Costello F, Courtney A, Cruz‐Herranz A, Cutter G, Crabb D, Delott L, De Seze J, Diem R, Dollfuss H, El Ayoubi N, Fasser C, Finke C, Fischer D, Fitzgerald K, Fonseca P, Frederiksen J, Frohman E, Frohman T, Fujihara K, Cuellar I, Galetta S, Garcia‐Martin E, Giovannoni G, Glebauskiene B, Suárez I, Jensen, G, Hamann S, Hartung H, Havla J, Hemmer B, Huang S, Imitola J, Jasinskas V, Jiang H, Kafieh R, Kappos L, Kardon R, Keegan D, Kildebeck E, Kim U, Klistorner S, Knier B, Kolbe S, Korn T, Krupp L, Lagrèze W, Leocani L, Levin N, Liskova P, Preiningerova J, Lorenz B, May E, Miller D, Mikolajczak J, Saïd S, Montalban X, Morrow M, Mowry E, Murta J, Navas C, Nolan R, Nowomiejska K, Oertel F, Oh J, Oreja‐Guevara C, Orssaud C, Osborne B, Outteryck O, Paiva C, Palace J, Papadopoulou A, Patsopoulos N, Preiningerova J, Pontikos N, Preising M, Prince J, Reich D, Rejdak R, Ringelstein M, Rodriguez de Antonio L, Sahel J, Sanchez‐Dalmau B, Sastre‐Garriga J, Schippling S, Schuman J, Shindler K, Shin R, Shuey N, Soelberg K, Specovius S, Suppiej A, Thompson A, Toosy A, Torres R, Touitou V, Trauzettel‐Klosinski S, van der Walt A, Vermersch P, Vidal‐Jordana A, Waldman A, Waters C, Wheeler R, White O, Wilhelm H, Winges K, Wiegerinck N, Wiehe L, Wisnewski T, Wong S, Würfel J, Yaghi S, You Y, Yu Z, Yu‐Wai‐Man P, Žemaitien≐ R, Zimmermann H. Artificial intelligence extension of the OSCAR‐IB criteria. Annals of Clinical and Translational Neurology 2021;8(7):1528 View
  19. Chaddad A, Katib Y, Hassan L. Future artificial intelligence tools and perspectives in medicine. Current Opinion in Urology 2021;31(4):371 View
  20. Ozaydin B, Berner E, Cimino J. Appropriate use of machine learning in healthcare. Intelligence-Based Medicine 2021;5:100041 View
  21. Kwong J, McLoughlin L, Haider M, Goldenberg M, Erdman L, Rickard M, Lorenzo A, Hung A, Farcas M, Goldenberg L, Nguan C, Braga L, Mamdani M, Goldenberg A, Kulkarni G. Standardized Reporting of Machine Learning Applications in Urology: The STREAM-URO Framework. European Urology Focus 2021;7(4):672 View
  22. Joloudari J, Saadatfar H, GhasemiGol M, Alizadehsani R, Sani Z, Hasanzadeh F, Hassannataj E, Sharifrazi D, Mansor Z. FCM-DNN: diagnosing coronary artery disease by deep accuracy fuzzy C-means clustering model. Mathematical Biosciences and Engineering 2022;19(4):3609 View
  23. Koutsouleris N, Hauser T, Skvortsova V, De Choudhury M. From promise to practice: towards the realisation of AI-informed mental health care. The Lancet Digital Health 2022;4(11):e829 View
  24. Howell P, Aryal A, Wu C. Web-Based Patient Recommender Systems for Preventive Care: Protocol for Empirical Research Propositions. JMIR Research Protocols 2023;12:e43316 View
  25. Knop M, Weber S, Mueller M, Niehaves B. Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence–Enabled Clinical Decision Support Systems: Literature Review. JMIR Human Factors 2022;9(1):e28639 View
  26. Eshel R, Bellolio F, Boggust A, Shapiro N, Mullan A, Heaton H, Madsen B, Homme J, Iliff B, Sunga K, Wangsgard C, Vanmeter D, Cabrera D. Comparison of clinical note quality between an automated digital intake tool and the standard note in the emergency department. The American Journal of Emergency Medicine 2023;63:79 View
  27. Winter P, Carusi A. Professional expectations and patient expectations concerning the development of Artificial Intelligence (AI) for the early diagnosis of Pulmonary Hypertension (PH). Journal of Responsible Technology 2022;12:100052 View
  28. Jermutus E, Kneale D, Thomas J, Michie S. Influences on User Trust in Healthcare Artificial Intelligence: A Systematic Review. Wellcome Open Research 2022;7:65 View
  29. Hindocha S, Badea C. Moral exemplars for the virtuous machine: the clinician’s role in ethical artificial intelligence for healthcare. AI and Ethics 2022;2(1):167 View
  30. Hanis T, Islam M, Musa K. Diagnostic Accuracy of Machine Learning Models on Mammography in Breast Cancer Classification: A Meta-Analysis. Diagnostics 2022;12(7):1643 View
  31. Petersson L, Larsson I, Nygren J, Nilsen P, Neher M, Reed J, Tyskbo D, Svedberg P. Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Services Research 2022;22(1) View
  32. Winter P, Carusi A. (De)troubling transparency: artificial intelligence (AI) for clinical applications. Medical Humanities 2023;49(1):17 View
  33. Russell S, Kumar A. Providing Care: Intrinsic Human–Machine Teams and Data. Entropy 2022;24(10):1369 View
  34. Plana D, Shung D, Grimshaw A, Saraf A, Sung J, Kann B. Randomized Clinical Trials of Machine Learning Interventions in Health Care. JAMA Network Open 2022;5(9):e2233946 View
  35. Doyen S, Dadario N. 12 Plagues of AI in Healthcare: A Practical Guide to Current Issues With Using Machine Learning in a Medical Context. Frontiers in Digital Health 2022;4 View
  36. Graziani M, Dutkiewicz L, Calvaresi D, Amorim J, Yordanova K, Vered M, Nair R, Abreu P, Blanke T, Pulignano V, Prior J, Lauwaert L, Reijers W, Depeursinge A, Andrearczyk V, Müller H. A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences. Artificial Intelligence Review 2023;56(4):3473 View
  37. Jiang J, Kahai S, Yang M. Who needs explanation and when? Juggling explainable AI and user epistemic uncertainty. International Journal of Human-Computer Studies 2022;165:102839 View
  38. Garcia K, Mishler S, Xiao Y, Wang C, Hu B, Still J, Chen J. Drivers’ Understanding of Artificial Intelligence in Automated Driving Systems: A Study of a Malicious Stop Sign. Journal of Cognitive Engineering and Decision Making 2022;16(4):237 View
  39. Bove R, Schleimer E, Sukhanov P, Gilson M, Law S, Barnecut A, Miller B, Hauser S, Sanders S, Rankin K. Building a Precision Medicine Delivery Platform for Clinics: The University of California, San Francisco, BRIDGE Experience. Journal of Medical Internet Research 2022;24(2):e34560 View
  40. Xu D, Xu Z. Bibliometric analysis of decision-making in healthcare management from 1998 to 2021. International Journal of Healthcare Management 2022:1 View
  41. Alaqra A, Kane B, Fischer-Hübner S. Machine Learning–Based Analysis of Encrypted Medical Data in the Cloud: Qualitative Study of Expert Stakeholders’ Perspectives. JMIR Human Factors 2021;8(3):e21810 View
  42. Svedberg P, Reed J, Nilsen P, Barlow J, Macrae C, Nygren J. Toward Successful Implementation of Artificial Intelligence in Health Care Practice: Protocol for a Research Program. JMIR Research Protocols 2022;11(3):e34920 View
  43. Scott I. Using information technology to reduce diagnostic error: still a bridge too far?. Internal Medicine Journal 2022;52(6):908 View
  44. Higgins O, Short B, Chalup S, Wilson R. Artificial intelligence ( AI ) and machine learning ( ML ) based decision support systems in mental health: An integrative review. International Journal of Mental Health Nursing 2023 View
  45. Fujimori R, Liu K, Soeno S, Naraba H, Ogura K, Hara K, Sonoo T, Ogura T, Nakamura K, Goto T. Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence–Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation. JMIR Formative Research 2022;6(6):e36501 View
  46. Bilal Unver M, Asan O. Role of Trust in AI-Driven Healthcare Systems: Discussion from the Perspective of Patient Safety. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2022;11(1):129 View
  47. Richardson J, Curtis S, Smith C, Pacyna J, Zhu X, Barry B, Sharp R. A framework for examining patient attitudes regarding applications of artificial intelligence in healthcare. DIGITAL HEALTH 2022;8:205520762210890 View
  48. Stacy J, Kim R, Barrett C, Sekar B, Simon S, Banaei-Kashani F, Rosenberg M. Qualitative Evaluation of an Artificial Intelligence–Based Clinical Decision Support System to Guide Rhythm Management of Atrial Fibrillation: Survey Study. JMIR Formative Research 2022;6(8):e36443 View
  49. Peters U. Explainable AI lacks regulative reasons: why AI and human decision-making are not equally opaque. AI and Ethics 2022 View
  50. Rushlow D, Croghan I, Inselman J, Thacher T, Friedman P, Yao X, Pellikka P, Lopez-Jimenez F, Bernard M, Barry B, Attia I, Misra A, Foss R, Molling P, Rosas S, Noseworthy P. Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care.. Mayo Clinic Proceedings 2022;97(11):2076 View
  51. Ojha U, Ayathamattam J, Okonkwo K, Ogunmwonyi I. Recent Updates and Technological Developments in Evaluating Cardiac Syncope in the Emergency Department. Current Cardiology Reviews 2022;18(6) View
  52. Meyer J, Khademi A, Têtu B, Han W, Nippak P, Remisch D. Impact of artificial intelligence on pathologists’ decisions: an experiment. Journal of the American Medical Informatics Association 2022;29(10):1688 View
  53. Withall J, Schwartz J, Usseglio J, Cato K. A Scoping Review of Integrated Medical Devices and Clinical Decision Support in the Acute Care Setting. Applied Clinical Informatics 2022;13(05):1223 View
  54. Jackson S, Panteli N. Trust or mistrust in algorithmic grading? An embedded agency perspective. International Journal of Information Management 2023;69:102555 View
  55. Sax D, Sturmer L, Mark D, Rana J, Reed M. Barriers and Opportunities Regarding Implementation of a Machine Learning-Based Acute Heart Failure Risk Stratification Tool in the Emergency Department. Diagnostics 2022;12(10):2463 View
  56. Roy S, Meena T, Lim S. Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine. Diagnostics 2022;12(10):2549 View
  57. Wenderott K, Gambashidze N, Weigl M. Integration of Artificial Intelligence Into Sociotechnical Work Systems—Effects of Artificial Intelligence Solutions in Medical Imaging on Clinical Efficiency: Protocol for a Systematic Literature Review. JMIR Research Protocols 2022;11(12):e40485 View
  58. Phan H, Mikkelsen K. Automatic sleep staging of EEG signals: recent development, challenges, and future directions. Physiological Measurement 2022;43(4):04TR01 View
  59. Fisher S, Rosella L. Priorities for successful use of artificial intelligence by public health organizations: a literature review. BMC Public Health 2022;22(1) View
  60. Phan H, Mikkelsen K, Chen O, Koch P, Mertins A, De Vos M. SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification. IEEE Transactions on Biomedical Engineering 2022;69(8):2456 View
  61. Győrffy Z, Békási S, Döbrössy B, Bognár V, Radó N, Morva E, Zsigri S, Tari P, Girasek E, Lim S. Exploratory attitude survey of homeless persons regarding telecare services in shelters providing mid- and long-term accommodation: The importance of trust. PLOS ONE 2022;17(1):e0261145 View
  62. Liu K, Li Y, Cui R. Design of a Computable Approximate Reasoning Logic System for AI. Mathematics 2022;10(9):1447 View
  63. . A 3‐D approach to personalised nutrition. Food Science and Technology 2022;36(2):34 View
  64. Asher C, Puyol-Antón E, Rizvi M, Ruijsink B, Chiribiri A, Razavi R, Carr-White G. The Role of AI in Characterizing the DCM Phenotype. Frontiers in Cardiovascular Medicine 2021;8 View
  65. Saßmannshausen T, Burggräf P, Hassenzahl M, Wagner J. Human trust in otherware – a systematic literature review bringing all antecedents together. Ergonomics 2022:1 View
  66. Kela N, Eytam E, Katz A. Supporting Management of Noncommunicable Diseases With Mobile Health (mHealth) Apps: Experimental Study. JMIR Human Factors 2022;9(1):e28697 View
  67. Ismatullaev U, Kim S. Review of the Factors Affecting Acceptance of AI-Infused Systems. Human Factors: The Journal of the Human Factors and Ergonomics Society 2022:001872082110647 View
  68. Zhang X, Xie Z, Xiang Y, Baig I, Kozman M, Stender C, Giancardo L, Tao C. Issues in Melanoma Detection: Semisupervised Deep Learning Algorithm Development via a Combination of Human and Artificial Intelligence. JMIR Dermatology 2022;5(4):e39113 View
  69. Buck C, Doctor E, Hennrich J, Jöhnk J, Eymann T. General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study. Journal of Medical Internet Research 2022;24(1):e28916 View
  70. Lukyanenko R, Maass W, Storey V. Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities. Electronic Markets 2022;32(4):1993 View
  71. Shan Y, Ji M, Xie W, Lam K, Chow C. Public Trust in Artificial Intelligence Applications in Mental Health Care: Topic Modeling Analysis. JMIR Human Factors 2022;9(4):e38799 View
  72. Parra R, Ojeda V, Vázquez Noguera J, García-Torres M, Mello-Román J, Villalba C, Facon J, Divina F, Cardozo O, Castillo V, Matto I. A Trust-Based Methodology to Evaluate Deep Learning Models for Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images. Diagnostics 2021;11(11):1951 View
  73. Jang Y, Choi S, Kim H. Development and validation of an instrument to measure undergraduate students’ attitudes toward the ethics of artificial intelligence (AT-EAI) and analysis of its difference by gender and experience of AI education. Education and Information Technologies 2022;27(8):11635 View
  74. Morande S. Enhancing psychosomatic health using artificial intelligence-based treatment protocol: A data science-driven approach. International Journal of Information Management Data Insights 2022;2(2):100124 View
  75. Sebastian G, George A, Jackson Jr G. Persuading Patients Using Rhetoric to Improve Artificial Intelligence Adoption: Experimental Study. Journal of Medical Internet Research 2023;25:e41430 View
  76. CHEN X, LEUNG Y, SHEN J. Artificial intelligence and its application for cardiovascular diseases in Chinese medicine. Digital Chinese Medicine 2022;5(4):367 View
  77. Liu S, Chen W, Su C, Pan K. Convolutional neural Network-based detection of deep vein thrombosis in a low limb with light reflection rheography. Measurement 2022;189:110457 View
  78. Tripathi S, Augustin A, Dako F, Kim E. Turing test-inspired method for analysis of biases prevalent in artificial intelligence-based medical imaging. AI and Ethics 2022 View
  79. Hulsen T, Friedecký D, Renz H, Melis E, Vermeersch P, Fernandez-Calle P. From big data to better patient outcomes. Clinical Chemistry and Laboratory Medicine (CCLM) 2023;61(4):580 View
  80. Triantafyllidis A, Kondylakis H, Katehakis D, Kouroubali A, Koumakis L, Marias K, Alexiadis A, Votis K, Tzovaras D. Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review. JMIR mHealth and uHealth 2022;10(4):e32344 View
  81. Goździkiewicz N, Zwolińska D, Polak-Jonkisz D. The Use of Artificial Intelligence Algorithms in the Diagnosis of Urinary Tract Infections—A Literature Review. Journal of Clinical Medicine 2022;11(10):2734 View
  82. Morrison J, Casey B, Sochet A, Dudas R, Rehman M, Goldenberg N, Ahumada L, Dees P. Performance Characteristics of a Machine-Learning Tool to Predict 7-Day Hospital Readmissions. Hospital Pediatrics 2022;12(9):824 View
  83. Ben-Shabat N, Sharvit G, Meimis B, Ben Joya D, Sloma A, Kiderman D, Shabat A, Tsur A, Watad A, Amital H. Assessing data gathering of chatbot based symptom checkers - a clinical vignettes study. International Journal of Medical Informatics 2022;168:104897 View
  84. van de Sande D, Van Genderen M, Smit J, Huiskens J, Visser J, Veen R, van Unen E, BA O, Gommers D, Bommel J. Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter. BMJ Health Care Inform 2022;29(1):e100495 View
  85. Raza A, Tran K, Koehl L, Li S. AnoFed: Adaptive anomaly detection for digital health using transformer-based federated learning and support vector data description. Engineering Applications of Artificial Intelligence 2023;121:106051 View
  86. Hosny A, Bitterman D, Guthier C, Qian J, Roberts H, Perni S, Saraf A, Peng L, Pashtan I, Ye Z, Kann B, Kozono D, Christiani D, Catalano P, Aerts H, Mak R. Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study. The Lancet Digital Health 2022;4(9):e657 View
  87. Sajno E, Bartolotta S, Tuena C, Cipresso P, Pedroli E, Riva G. Machine learning in biosignals processing for mental health: A narrative review. Frontiers in Psychology 2023;13 View
  88. TAYLOR L, NONG P, PLATT J. Fifty Years of Trust Research in Health Care: A Synthetic Review. The Milbank Quarterly 2023;101(1):126 View
  89. Braun E, Singh S, Penlesky A, Strong E, Holt J, Fletcher K, Stadler M, Nattinger A, Crotty B. Nursing implications of an early warning system implemented to reduce adverse events: a qualitative study. BMJ Quality & Safety 2022;31(10):716 View
  90. Denissen S, Chén O, De Mey J, De Vos M, Van Schependom J, Sima D, Nagels G. Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis. Journal of Personalized Medicine 2021;11(12):1349 View
  91. Marotta A. When AI Is Wrong: Addressing Liability Challenges in Women’s Healthcare. Journal of Computer Information Systems 2022;62(6):1310 View
  92. Hah H, Goldin D. How Clinicians Perceive Artificial Intelligence–Assisted Technologies in Diagnostic Decision Making: Mixed Methods Approach. Journal of Medical Internet Research 2021;23(12):e33540 View
  93. Yang K, Nambudiri V. Anticipating Ambulatory Automation: Potential Applications of Administrative and Clinical Automation in Outpatient Healthcare Delivery. Applied Clinical Informatics 2021;12(05):1157 View
  94. Pandya S, Thakur A, Saxena S, Jassal N, Patel C, Modi K, Shah P, Joshi R, Gonge S, Kadam K, Kadam P. A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions. Sensors 2021;21(23):7786 View
  95. Banerjee M, Chiew D, Patel K, Johns I, Chappell D, Linton N, Cole G, Francis D, Szram J, Ross J, Zaman S. The impact of artificial intelligence on clinical education: perceptions of postgraduate trainee doctors in London (UK) and recommendations for trainers. BMC Medical Education 2021;21(1) View
  96. Zhang G, Raina A, Brownell E, Cagan J. Artificial Intelligence Impersonating a Human: The Impact of Design Facilitator Identity on Human Designers. Journal of Mechanical Design 2023;145(5) View
  97. Adler-Milstein J, Chen J, Dhaliwal G. Next-Generation Artificial Intelligence for Diagnosis. JAMA 2021;326(24):2467 View
  98. Stenwig E, Salvi G, Rossi P, Skjærvold N. Comparative analysis of explainable machine learning prediction models for hospital mortality. BMC Medical Research Methodology 2022;22(1) View
  99. Choi S, Jang Y, Kim H. Influence of Pedagogical Beliefs and Perceived Trust on Teachers’ Acceptance of Educational Artificial Intelligence Tools. International Journal of Human–Computer Interaction 2023;39(4):910 View
  100. Choudhury A, Asan O. Impact of accountability, training, and human factors on the use of artificial intelligence in healthcare: Exploring the perceptions of healthcare practitioners in the US. Human Factors in Healthcare 2022;2:100021 View
  101. Rizvi R, Emani S, Rocha H, de Aquino C, Garabedian P, Rui A, Arruda C, Sands-Lincoln M, Rozenblum R, Felix W, Jackson G, Juacaba S, Bates D. Physicians' Perceptions and Expectations of an Artificial Intelligence-Based Clinical Decision Support System in Cancer Care in an Underserved Setting. ACI Open 2022;06(02):e66 View
  102. Kumar P, Chauhan S, Awasthi L. Artificial Intelligence in Healthcare: Review, Ethics, Trust Challenges & Future Research Directions. Engineering Applications of Artificial Intelligence 2023;120:105894 View
  103. Link E, Baumann E, Klimmt C. Understanding the importance of trust in patients’ coping with uncertainty via health information-seeking behaviors. Communications 2022;0(0) View
  104. Terry A, Kueper J, Beleno R, Brown J, Cejic S, Dang J, Leger D, McKay S, Meredith L, Pinto A, Ryan B, Stewart M, Zwarenstein M, Lizotte D. Is primary health care ready for artificial intelligence? What do primary health care stakeholders say?. BMC Medical Informatics and Decision Making 2022;22(1) View
  105. Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review. Journal of Medical Internet Research 2022;24(1):e32215 View
  106. Choudhury A, Asan O, Medow J. Effect of risk, expectancy, and trust on clinicians’ intent to use an artificial intelligence system -- Blood Utilization Calculator. Applied Ergonomics 2022;101:103708 View
  107. Schoenherr J. Folkmedical Technologies and the Sociotechnical Systems of Healthcare. IEEE Technology and Society Magazine 2022;41(3):38 View
  108. Wysocki O, Davies J, Vigo M, Armstrong A, Landers D, Lee R, Freitas A. Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making. Artificial Intelligence 2023;316:103839 View
  109. Jang Y, Choi S, Jung H, Kim H. Practical early prediction of students’ performance using machine learning and eXplainable AI. Education and Information Technologies 2022;27(9):12855 View
  110. Schepman A, Rodway P. The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory Validation and Associations with Personality, Corporate Distrust, and General Trust. International Journal of Human–Computer Interaction 2022:1 View
  111. Glick A, Clayton M, Angelov N, Chang J. Impact of explainable artificial intelligence assistance on clinical decision-making of novice dental clinicians. JAMIA Open 2022;5(2) View
  112. Finck T, Li H, Schlaeger S, Grundl L, Sollmann N, Bender B, Bürkle E, Zimmer C, Kirschke J, Menze B, Mühlau M, Wiestler B. Uncertainty-Aware and Lesion-Specific Image Synthesis in Multiple Sclerosis Magnetic Resonance Imaging: A Multicentric Validation Study. Frontiers in Neuroscience 2022;16 View
  113. Liu X, He X, Wang M, Shen H. What influences patients' continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics. Technology in Society 2022;70:101996 View
  114. Abdalla M, Fine B. Hurdles to Artificial Intelligence Deployment: Noise in Schemas and “Gold” Labels. Radiology: Artificial Intelligence 2023;5(2) View
  115. Michelson K, Klugman C, Kho A, Gerke S. Ethical Considerations Related to Using Machine Learning-Based Prediction of Mortality in the Pediatric Intensive Care Unit. The Journal of Pediatrics 2022;247:125 View
  116. Hanif A, Beqiri S, Keane P, Campbell J. Applications of interpretability in deep learning models for ophthalmology. Current Opinion in Ophthalmology 2021;32(5):452 View
  117. Rojas J, Teran M, Umscheid C. Clinician Trust in Artificial Intelligence. Critical Care Clinics 2023 View
  118. Moazemi S, Vahdati S, Li J, Kalkhoff S, Castano L, Dewitz B, Bibo R, Sabouniaghdam P, Tootooni M, Bundschuh R, Lichtenberg A, Aubin H, Schmid F. Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review. Frontiers in Medicine 2023;10 View
  119. Ben-Shabat N, Sharvit G, Meimis B, Ben Joya D, Sloma A, Kiderman D, Shabat A, Tsur A, Watad A, Amital H. Assessing Data Gathering Quality of Chatbot Based Symptom Checkers - a Clinical Vignettes Study. SSRN Electronic Journal 2022 View
  120. Unver M. Governing fiduciary relationships or building up a governance model for trust in AI? Review of healthcare as a socio-technical system. International Review of Law, Computers & Technology 2023:1 View
  121. Rafiq M, Mazzocato P, Guttmann C, Spaak J, Savage C. Predictive Analytics Support for Complex Chronic Medical Conditions: An Experience-Based Co-Design Study of Physician Managers’ Needs and Preferences. SSRN Electronic Journal 2022 View
  122. Badal K, Lee C, Esserman L. Guiding principles for the responsible development of artificial intelligence tools for healthcare. Communications Medicine 2023;3(1) View
  123. Constantin A, Atkinson M, Bernabeu M, Buckmaster F, Dhillon B, McTrusty A, Strang N, Williams R. Optometrists’ Perspectives regarding Artificial Intelligence Assistance and contributing Retinal Images to a Repository: a Pilot Study (Preprint). JMIR Human Factors 2022 View
  124. robinson r, Liday C, Lee S, Willams I, Wright M, An D, Nguyen E. Artificial intelligence in healthcare: Understanding patient information needs and designing comprehensible transparency (Preprint). JMIR AI 2023 View
  125. Ciccarelli M, Giallauria F, Carrizzo A, Visco V, Silverio A, Cesaro A, Calabrò P, De Luca N, Mancusi C, Masarone D, Pacileo G, Tourkmani N, Vigorito C, Vecchione C. Artificial intelligence in cardiovascular prevention: new ways will open new doors. Journal of Cardiovascular Medicine 2023;24(Supplement 2):e106 View
  126. Tong W, Wu S, Cheng M, Huang H, Liang J, Li C, Guo H, He D, Liu Y, Xiao H, Hu H, Ruan S, Li M, Lu M, Wang W. Integration of Artificial Intelligence Decision Aids to Reduce Workload and Enhance Efficiency in Thyroid Nodule Management. JAMA Network Open 2023;6(5):e2313674 View
  127. Massey C, Asokan A, Tietbohl C, Morris M, Ramakrishnan V. Otolaryngologist perceptions of AI-based sinus CT interpretation. American Journal of Otolaryngology 2023:103932 View

Books/Policy Documents

  1. Diaz-Flores E, Meyer T, Giorkallos A. Smart Biolabs of the Future. View
  2. Azzali I, Cilia N, De Stefano C, Fontanella F, Giacobini M, Vanneschi L. Applications of Evolutionary Computation. View
  3. Whitehead S, Petryk S, Shakib V, Gonzalez J, Darrell T, Rohrbach A, Rohrbach M. Computer Vision – ECCV 2022. View
  4. Dykstra S, White J, Gavrilova M. Handbook of Artificial Intelligence in Healthcare. View
  5. Korngiebel D, Solomonides A, Goodman K. Intelligent Systems in Medicine and Health. View
  6. Nesterenko K, Lewis R. Foundations of Intelligent Systems. View
  7. Tuncer S, Ramirez A. HCI International 2022 – Late Breaking Papers: Interacting with eXtended Reality and Artificial Intelligence. View
  8. Ganapathy K. Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence. View
  9. D. J, A. P. Encyclopedia of Data Science and Machine Learning. View
  10. Rueckert D, Knolle M, Duchateau N, Razavi R, Kaissis G. AI and Big Data in Cardiology. View
  11. Rao Bhavaraju S. Artificial Intelligence in Medicine and Surgery - An Exploration of Current Trends, Potential Opportunities, and Evolving Threats [Working Title]. View