ArticleJournal of medical Internet research2025
Harm Reduction Strategies for Thoughtful Use of Large Language Models in the Medical Domain: Perspectives for Patients and Clinicians.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Large language models in healthcare: applications, evaluation frameworks, and governance pathways - a scoping review and multidimensional framework.Frontiers in digital health · 2026Pooled it
- A Patient Simulation Framework for Risk Assessment of Conversational Health Care AI: Development and Evaluation Study.JMIR AI · 2026Article
- Article
- Evaluating Large Language Models in Clinical Audiology (AUDIOLOGYBENCH): Benchmark Development and Validation Study.Journal of medical Internet research · 2026Article
- Automating the Management of Extraspinal Findings in Magnetic Resonance Imaging Spine Studies Using a Privacy-Preserving Large Language Model: Retrospective Validation Study.Journal of medical Internet research · 2026Article
- Safety-Oriented Evaluation of Large Language Models in Health Care: Guideline-Informed Systematic Review.Online journal of public health informatics · 2026Review
- Review
- Evaluating the safety of large language models in healthcare and dentistry: adversarial testing approaches.BDJ open · 2026Review
- Personal Health Large Language Models and the Negotiation of Medical Authority in Clinical Care: Opportunities, Risks, and Governance.Journal of medical Internet research · 2026Article
- Leveraging simulation to provide a practical framework for estimating the novel scope of risk of large language models in healthcare.BMJ mental health · 2026Article
- "MELMA" in otolaryngology: Medical evaluation of large language model answers. Clinician-rated scoring (MELMA-Q) and web-based auditing (MELMA-W) novel tools for AI assessment.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Performance of Large Language Models on the Brazilian National Medical Education Examination: Comparative Benchmark Study.JMIR medical education · 2026Article
- Differences in Safety Risks Across Languages in Health-Relevant Queries: Vulnerability Analysis of Large Language Model Responses.JMIR formative research · 2026Article
- Article
- Evaluating large language models for pharmacotherapy simulations: a mixed-methods study.NPJ digital medicine · 2026Article
- Implementation, Acceptability, and Actions After Using an AI Workplace Health Kiosk in a Low-Resource Public School Workplace Setting: Cross-Sectional Pilot Study.JMIR formative research · 2026Article
- Medical large language models and systems in the clinical application of spinal diseases: Current status, challenges, and future prospects.Journal of orthopaedic translation · 2026Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unlabelled: The integration of large language models (LLMs) into health care presents significant risks to patients and clinicians, inadequately addressed by current guidance. This paper adapts harm reduction principles from public health to medical LLMs, proposing a structured framework for mitigating these domain-specific risks while maximizing ethical utility. We outline tailored strategies for patients, emphasizing critical health literacy and output verification, and for clinicians, enforcing "human-in-the-loop" validation and bias-aware workflows. Key innovations include developing thoughtful use protocols that position LLMs as assistive tools requiring mandatory verification, establishing actionable institutional policies with risk-stratified deployment guidelines and patient disclaimers, and critically analyzing underaddressed regulatory, equity, and safety challenges. This research moves beyond theory to offer a practical roadmap, enabling stakeholders to ethically harness LLMs, balance innovation with accountability, and preserve core medical values: patient safety, equity, and trust in high-stakes health care settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.