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

Viewpoints and Perspectives (619) Chatbots and Conversational Agents (740) Generative Language Models Including ChatGPT (708) Research Instruments, Questionnaires, and Tools (738) New Methods (1134) Natural Language Processing (917) Artificial Intelligence (2129) Adoption and Change Management of eHealth Systems (894)

Published on in Vol 25 (2023)

This is a member publication of Bodleian Libraries (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51603, first published .
How Can the Clinical Aptitude of AI Assistants Be Assayed?

How Can the Clinical Aptitude of AI Assistants Be Assayed?

How Can the Clinical Aptitude of AI Assistants Be Assayed?

Authors of this article:

Arun James Thirunavukarasu1, 2 Author Orcid Image
Article Authors Cited by (10) Tweetations (4) Metrics

Journals

  1. Thirunavukarasu A, Elangovan K, Gutierrez L, Hassan R, Li Y, Tan T, Cheng H, Teo Z, Lim G, Ting D. Clinical performance of automated machine learning: A systematic review. Annals of the Academy of Medicine, Singapore 2024;53(3 - Correct DOI):187 View
  2. Thirunavukarasu A, Elangovan K, Gutierrez L, Hassan R, Li Y, Tan T, Cheng H, Teo Z, Lim G, Ting D. Clinical performance of automated machine learning: A systematic review. Annals of the Academy of Medicine, Singapore 2024;53(3):187 View
  3. Thirunavukarasu A, Mahmood S, Malem A, Foster W, Sanghera R, Hassan R, Zhou S, Wong S, Wong Y, Chong Y, Shakeel A, Chang Y, Tan B, Jain N, Tan T, Rauz S, Ting D, Ting D, Luo M. Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study. PLOS Digital Health 2024;3(4):e0000341 View
  4. Anbumani S, Ahunbay E. Toward the Clinically Effective Evaluation of Artificial Intelligence–Generated Responses. JCO Clinical Cancer Informatics 2024;(8) View
  5. Toal M, Hill C, Quinn M, O'Neill C, Maxwell A. Large Language Models’ Clinical Decision-Making on When to Perform a Kidney Biopsy: Comparative Study. Journal of Medical Internet Research 2025;27:e73603 View
  6. Dennstädt F, Schmerder M, Riggenbach E, Mose L, Bryjova K, Bachmann N, Mackeprang P, Ahmadsei M, Sinovcic D, Windisch P, Zwahlen D, Rogers S, Riesterer O, Maffei M, Gkika E, Haddad H, Peeken J, Putora P, Glatzer M, Putz F, Hoefler D, Christ S, Filchenko I, Hastings J, Gaio R, Chiang L, Aebersold D, Cihoric N. Comparative Evaluation of a Medical Large Language Model in Answering Real-World Radiation Oncology Questions: Multicenter Observational Study. Journal of Medical Internet Research 2025;27:e69752 View
  7. Teo Z, Thirunavukarasu A, Elangovan K, Cheng H, Moova P, Soetikno B, Nielsen C, Pollreisz A, Ting D, Morris R, Shah N, Langlotz C, Ting D. Generative artificial intelligence in medicine. Nature Medicine 2025;31(10):3270 View
  8. Giguere A, Auclair-Rochon D, Robin M, Augustine L, Ayre J, McCaffery K. Comparing traditional and AI-enhanced strategies for developing patient decision aids: a multiple case study. BMJ Evidence-Based Medicine 2025:bmjebm-2025-113675 View
  9. Rocha H, Chong Y, Thirunavukarasu A, Wong Y, Wong S, Chang Y, Azzopardi M, Tan B, Song A, Malem A, Jain N, Zhou S, Tan T, Rauz S, Ang M, Mehta J, Ting D, Ting D. Performance of Foundation Models vs Physicians in Textual and Multimodal Ophthalmological Questions. JAMA Ophthalmology 2025 View

Books/Policy Documents

  1. Pondel M, Chomiak-Orsa I, Sobińska M, Grzelak W, Kotwica A, Małowiecki A, Łuczak K, Greńczuk A, Busch P, Chudán D, Berka P. Emerging Challenges in Intelligent Management Information Systems. View