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Citing this Article

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Published on 08.03.17 in Vol 19, No 3 (2017): March

This paper is in the following e-collection/theme issue:

Works citing "A Learning Health Care System Using Computer-Aided Diagnosis"

Amos Cahan, James J Cimino

J Med Internet Res 2017 (Mar 08); 19(3):e54 

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.6663):

(note that this is only a small subset of citations)

  1. Garg G, Juneja M. A survey of denoising techniques for multi-parametric prostate MRI. Multimedia Tools and Applications 2019;78(10):12689
    CrossRef
  2. Rider NL, Miao D, Dodds M, Modell V, Modell F, Quinn J, Schwarzwald H, Orange JS. Calculation of a Primary Immunodeficiency “Risk Vital Sign” via Population-Wide Analysis of Claims Data to Aid in Clinical Decision Support. Frontiers in Pediatrics 2019;7
    CrossRef
  3. Schwitzguebel AJ, Jeckelmann C, Gavinio R, Levallois C, Benaïm C, Spechbach H. Differential Diagnosis Assessment in Ambulatory Care With an Automated Medical History–Taking Device: Pilot Randomized Controlled Trial. JMIR Medical Informatics 2019;7(4):e14044
    CrossRef
  4. Jani BD, Pell JP, McGagh D, Liyanage H, Kelly D, de Lusignan S, Weatherburn CJ, Burns R, Sullivan FM, Mair FS. Recording COVID-19 consultations: review of symptoms, risk factors, and proposed SNOMED CT terms. BJGP Open 2020;4(4):bjgpopen20X101125
    CrossRef
  5. Ferdousi R, Arab‐Zozani M, Tahamtan I, Rezaei‐Hachesu P, Dehghani M. Attitudes of nurses towards clinical information systems: a systematic review and meta‐analysis. International Nursing Review 2021;68(1):59
    CrossRef
  6. . Pharmacovigilance in Crisis: Drug Safety at a Crossroads. Clinical Therapeutics 2018;40(5):790
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  7. Keitel K, D'Acremont V. Electronic clinical decision algorithms for the integrated primary care management of febrile children in low-resource settings: review of existing tools. Clinical Microbiology and Infection 2018;24(8):845
    CrossRef
  8. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nature Reviews Cancer 2018;18(8):500
    CrossRef
  9. Ahmad W, Ahmad A, Iqbal A, Hamayun M, Hussain A, Rehman G, Khan S, Khan UU, Khan D, Huang L. Intelligent hepatitis diagnosis using adaptive neuro-fuzzy inference system and information gain method. Soft Computing 2019;23(21):10931
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  10. Verheij RA, Curcin V, Delaney BC, McGilchrist MM. Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse. Journal of Medical Internet Research 2018;20(5):e185
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  11. Panigrahi L, Verma K, Singh BK. Ultrasound image segmentation using a novel multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution. Expert Systems with Applications 2019;115:486
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  12. . Decision Support to Enhance Automated Laboratory Testing by Leveraging Analytical Capabilities. Clinics in Laboratory Medicine 2019;39(2):259
    CrossRef
  13. Ronicke S, Hirsch MC, Türk E, Larionov K, Tientcheu D, Wagner AD. Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study. Orphanet Journal of Rare Diseases 2019;14(1)
    CrossRef
  14. Rodgers A, Trinchieri A, Ather MH, Buchholz N. Vision for the future on urolithiasis: research, management, education and training—some personal views. Urolithiasis 2019;47(5):401
    CrossRef
  15. Lake IR, Colón-González FJ, Barker GC, Morbey RA, Smith GE, Elliot AJ. Machine learning to refine decision making within a syndromic surveillance service. BMC Public Health 2019;19(1)
    CrossRef
  16. Kuiper G, Meijer OLM, Langereis EJ, Wijburg FA. Failure to shorten the diagnostic delay in two ultra-orphan diseases (mucopolysaccharidosis types I and III): potential causes and implications. Orphanet Journal of Rare Diseases 2018;13(1)
    CrossRef
  17. Yanase J, Triantaphyllou E. The seven key challenges for the future of computer-aided diagnosis in medicine. International Journal of Medical Informatics 2019;129:413
    CrossRef
  18. Kondylakis H, Axenie C, (Kiran) Bastola D, Katehakis DG, Kouroubali A, Kurz D, Larburu N, Macía I, Maguire R, Maramis C, Marias K, Morrow P, Muro N, Núñez-Benjumea FJ, Rampun A, Rivera-Romero O, Scotney B, Signorelli G, Wang H, Tsiknakis M, Zwiggelaar R. Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study. Journal of Medical Internet Research 2020;22(12):e22034
    CrossRef
  19. Harada Y, Katsukura S, Kawamura R, Shimizu T. Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study. International Journal of Environmental Research and Public Health 2021;18(4):2086
    CrossRef
  20. Srinivasu PN, SivaSai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ. Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors 2021;21(8):2852
    CrossRef
  21. Juneja M, Kaur Saini S, Kaul S, Acharjee R, Thakur N, Jindal P. Denoising of magnetic resonance imaging using Bayes shrinkage based fused wavelet transform and autoencoder based deep learning approach. Biomedical Signal Processing and Control 2021;69:102844
    CrossRef
  22. Matsuoka A, Miike T, Yamazaki H, Higuchi M, Komaki M, Shinada K, Nakayama K, Sakurai R, Asahi M, Yoshitake K, Narumi S, Koba M, Sugioka T, Sakamoto Y, Pietrantonio F. Usefulness of a medical interview support application for residents: A pilot study. PLOS ONE 2022;17(9):e0274159
    CrossRef
  23. Lin C, Lee Y, Wu F, Lin S, Hsu C, Lee C, Tsai D, Fang W. The Application of Projection Word Embeddings on Medical Records Scoring System. Healthcare 2021;9(10):1298
    CrossRef
  24. Juneja M, Saini SK, Acharjee R, Kaul S, Thakur N, Jindal P. PC‐SNet for automated detection of prostate cancer in multiparametric‐magnetic resonance imaging. International Journal of Imaging Systems and Technology 2022;32(6):1861
    CrossRef
  25. Shaygan A, Daim T. Technology management maturity assessment model in healthcare research centers. Technovation 2023;120:102444
    CrossRef
  26. Arun Bhavsar K, Singla J, D. Al-Otaibi Y, Song O, Bin Zikriya Y, Kashif Bashir A. Medical Diagnosis Using Machine Learning: A Statistical Review. Computers, Materials & Continua 2021;67(1):107
    CrossRef
  27. Aldhyani THH, Verma A, Al-Adhaileh MH, Koundal D. Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural Network. Diagnostics 2022;12(9):2048
    CrossRef
  28. Kanazawa A, Fujibayashi K, Watanabe Y, Kushiro S, Yanagisawa N, Fukataki Y, Kitamura S, Hayashi W, Nagao M, Nishizaki Y, Inomata T, Arikawa-Hirasawa E, Naito T. Evaluation of a Medical Interview-Assistance System Using Artificial Intelligence for Resident Physicians Interviewing Simulated Patients: A Crossover, Randomized, Controlled Trial. International Journal of Environmental Research and Public Health 2023;20(12):6176
    CrossRef
  29. S. Alshuhri M, Al-Musawi SG, Al-Alwany AA, Uinarni H, Rasulova I, Rodrigues P, Alkhafaji AT, Alshanberi AM, Alawadi AH, Abbas AH. Artificial intelligence in cancer diagnosis: Opportunities and challenges. Pathology - Research and Practice 2024;253:154996
    CrossRef
  30. Zou J, Arshad MR. Detection of whole body bone fractures based on improved YOLOv7. Biomedical Signal Processing and Control 2024;91:105995
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/jmir.6663):

  1. . Handbook of Global Health. 2021. Chapter 70-1:1
    CrossRef
  2. Ghosh A, Parui S, Samanta D, Mukhopadhyay J, Chakravorty N. Modern Techniques in Biosensors. 2021. Chapter 5:111
    CrossRef
  3. . Clinical Neurotechnology meets Artificial Intelligence. 2021. Chapter 12:155
    CrossRef
  4. Andropova P, Cheremisin D, Meldo A. Proceedings of International Scientific Conference on Telecommunications, Computing and Control. 2021. Chapter 24:277
    CrossRef
  5. . Handbook of Global Health. 2021. Chapter 70:1667
    CrossRef