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

A Learning Health Care System Using Computer-Aided Diagnosis

A Learning Health Care System Using Computer-Aided Diagnosis

A Learning Health Care System Using Computer-Aided Diagnosis

Authors of this article:

Amos Cahan1 Author Orcid Image ;   James J Cimino2 Author Orcid Image


  1. Garg G, Juneja M. A survey of denoising techniques for multi-parametric prostate MRI. Multimedia Tools and Applications 2019;78(10):12689 View
  2. Rider N, Miao D, Dodds M, Modell V, Modell F, Quinn J, Schwarzwald H, Orange J. 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 View
  3. Schwitzguebel A, 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 View
  4. Jani B, Pell J, McGagh D, Liyanage H, Kelly D, de Lusignan S, Weatherburn C, Burns R, Sullivan F, Mair F. Recording COVID-19 consultations: review of symptoms, risk factors, and proposed SNOMED CT terms. BJGP Open 2020;4(4):bjgpopen20X101125 View
  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 View
  6. Price J. Pharmacovigilance in Crisis: Drug Safety at a Crossroads. Clinical Therapeutics 2018;40(5):790 View
  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 View
  8. Hosny A, Parmar C, Quackenbush J, Schwartz L, Aerts H. Artificial intelligence in radiology. Nature Reviews Cancer 2018;18(8):500 View
  9. Ahmad W, Ahmad A, Iqbal A, Hamayun M, Hussain A, Rehman G, Khan S, Khan U, Khan D, Huang L. Intelligent hepatitis diagnosis using adaptive neuro-fuzzy inference system and information gain method. Soft Computing 2019;23(21):10931 View
  10. Verheij R, Curcin V, Delaney B, McGilchrist M. Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse. Journal of Medical Internet Research 2018;20(5):e185 View
  11. Panigrahi L, Verma K, Singh B. 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 View
  12. Tuthill J. Decision Support to Enhance Automated Laboratory Testing by Leveraging Analytical Capabilities. Clinics in Laboratory Medicine 2019;39(2):259 View
  13. Ronicke S, Hirsch M, Türk E, Larionov K, Tientcheu D, Wagner A. 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) View
  14. Rodgers A, Trinchieri A, Ather M, Buchholz N. Vision for the future on urolithiasis: research, management, education and training—some personal views. Urolithiasis 2019;47(5):401 View
  15. Lake I, Colón-González F, Barker G, Morbey R, Smith G, Elliot A. Machine learning to refine decision making within a syndromic surveillance service. BMC Public Health 2019;19(1) View
  16. Kuiper G, Meijer O, Langereis E, Wijburg F. 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) View
  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 View
  18. Kondylakis H, Axenie C, (Kiran) Bastola D, Katehakis D, Kouroubali A, Kurz D, Larburu N, Macía I, Maguire R, Maramis C, Marias K, Morrow P, Muro N, Núñez-Benjumea F, 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 View
  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 View
  20. Srinivasu P, SivaSai J, Ijaz M, Bhoi A, Kim W, Kang J. Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors 2021;21(8):2852 View
  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 View
  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 View
  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 View
  24. Juneja M, Saini S, 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 View
  25. Shaygan A, Daim T. Technology management maturity assessment model in healthcare research centers. Technovation 2023;120:102444 View
  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 View
  27. Aldhyani T, Verma A, Al-Adhaileh M, Koundal D. Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural Network. Diagnostics 2022;12(9):2048 View
  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 View
  29. S. Alshuhri M, Al-Musawi S, Al-Alwany A, Uinarni H, Rasulova I, Rodrigues P, Alkhafaji A, Alshanberi A, Alawadi A, Abbas A. Artificial intelligence in cancer diagnosis: Opportunities and challenges. Pathology - Research and Practice 2024;253:154996 View
  30. Zou J, Arshad M. Detection of whole body bone fractures based on improved YOLOv7. Biomedical Signal Processing and Control 2024;91:105995 View
  31. Juneja M, Saini S, Chanana C, Jindal P. MRI-CropNet for Automated Cropping of Prostate Cancer in Magnetic Resonance Imaging. Wireless Personal Communications 2024;136(2):1183 View

Books/Policy Documents

  1. Flahault A. Handbook of Global Health. View
  2. Ghosh A, Parui S, Samanta D, Mukhopadhyay J, Chakravorty N. Modern Techniques in Biosensors. View
  3. Jox R. Clinical Neurotechnology meets Artificial Intelligence. View
  4. Andropova P, Cheremisin D, Meldo A. Proceedings of International Scientific Conference on Telecommunications, Computing and Control. View
  5. Flahault A. Handbook of Global Health. View