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

Decision Support for Health Professionals (1191) Digital Health Reviews (1291) Cost-Effectiveness and Economics (252) Machine Learning (1471)
Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33703, first published .
Evaluation of the Clinical, Technical, and Financial Aspects of Cost-Effectiveness Analysis of Artificial Intelligence in Medicine: Scoping Review and Framework of Analysis

Evaluation of the Clinical, Technical, and Financial Aspects of Cost-Effectiveness Analysis of Artificial Intelligence in Medicine: Scoping Review and Framework of Analysis

Evaluation of the Clinical, Technical, and Financial Aspects of Cost-Effectiveness Analysis of Artificial Intelligence in Medicine: Scoping Review and Framework of Analysis

Authors of this article:

Jesus Gomez Rossi1 Author Orcid Image ;   Ben Feldberg1 Author Orcid Image ;   Joachim Krois1 Author Orcid Image ;   Falk Schwendicke1 Author Orcid Image
Article Authors Cited by (10) Tweetations (4) Metrics

Journals

  1. Nakagawa K, Moukheiber L, Celi L, Patel M, Mahmood F, Gondim D, Hogarth M, Levenson R. AI in Pathology: What could possibly go wrong?. Seminars in Diagnostic Pathology 2023;40(2):100 View
  2. Hung K, Yeung A, Bornstein M, Schwendicke F. Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging. Dentomaxillofacial Radiology 2023;52(1) View
  3. Bajaj S, Khunte M, Moily N, Payabvash S, Wintermark M, Gandhi D, Malhotra A. Value Proposition of FDA-Approved Artificial Intelligence Algorithms for Neuroimaging. Journal of the American College of Radiology 2023;20(12):1241 View
  4. Lünse S, Wisotzky E, Beckmann S, Paasch C, Hunger R, Mantke R. Technological advancements in surgical laparoscopy considering artificial intelligence: a survey among surgeons in Germany. Langenbeck's Archives of Surgery 2023;408(1) View
  5. Bignami E, Celoria S, Bellini V. Surgical outcomes and patient-centred perioperative programs. Journal of Clinical Monitoring and Computing 2023;37(6):1641 View
  6. Hurvitz N, Ilan Y. The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from “Nice to Have” to Mandatory Systems. Clinics and Practice 2023;13(4):994 View
  7. Adigwe O, Onavbavba G, Sanyaolu S. Exploring the matrix: knowledge, perceptions and prospects of artificial intelligence and machine learning in Nigerian healthcare. Frontiers in Artificial Intelligence 2024;6 View
  8. Williams M, Weir-McCall J, Baldassarre L, De Cecco C, Choi A, Dey D, Dweck M, Isgum I, Kolossvary M, Leipsic J, Lin A, Lu M, Motwani M, Nieman K, Shaw L, van Assen M, Nicol E. Artificial Intelligence and Machine Learning for Cardiovascular Computed Tomography (CCT): A White Paper of the Society of Cardiovascular Computed Tomography (SCCT). Journal of Cardiovascular Computed Tomography 2024;18(6):519 View
  9. Darvishmohammadi T, Özkal A, Özkal A. Artificial Intelligence in Medicine: Opportunities and Challenges. Black Sea Journal of Engineering and Science 2024;7(5):1092 View

Books/Policy Documents

  1. Chang A, Limon A. Intelligence-Based Cardiology and Cardiac Surgery. View