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

Viewpoints and Perspectives (619) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (87) Viewpoint and Opinions on Technology and Innovation in Public Health (67) Viewpoints and Perspectives in AI (16)

Published on in Vol 27 (2025)

This is a member publication of Bibsam Consortium

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/75849, first published .
Harm Reduction Strategies for Thoughtful Use of Large Language Models in the Medical Domain: Perspectives for Patients and Clinicians

Harm Reduction Strategies for Thoughtful Use of Large Language Models in the Medical Domain: Perspectives for Patients and Clinicians

Harm Reduction Strategies for Thoughtful Use of Large Language Models in the Medical Domain: Perspectives for Patients and Clinicians

Authors of this article:

Birger Moëll1 Author Orcid Image ;   Fredrik Sand Aronsson2, 3 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

Journals

  1. Moëll B, Farestam F, Beskow J. Swedish Medical LLM Benchmark: development and evaluation of a framework for assessing large language models in the Swedish medical domain. Frontiers in Artificial Intelligence 2025;8 View
  2. Reshetnikov R, Tyrov I, Vasilev Y, Shumskaya Y, Vladzymyrskyy A, Akhmedzyanova D, Bezhenova K, Varyukhina M, Sokolova M, Blokhin I, Voytenko D, Mynko O, Kodenko M, Omelyanskaya O. Assessing the quality of large generative models for basic healthcare applications. Medical Doctor and Information Technologies 2025;(3):64 View
  3. Vladic N, Nopp S, Pabinger I, Ageno W, Connors J, Eichinger S, Ay C. Large language models vs thrombosis experts: a comparative study on patient education and clinical decision-making in venous thromboembolism. Journal of Thrombosis and Haemostasis 2025 View