ReviewEBioMedicine2023
Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine.
Review in EBioMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 137 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
137 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large Language Models for Mental Health Applications: Systematic Review.JMIR mental health · 2024Pooled it
- Embracing Digital as a Paradigm Shift in Medical Affairs.Pharmaceutical medicine · 2026Review
- Ethical and Regulatory Challenges of Generative Artificial Intelligence in Healthcare: A Chinese Perspective.Journal of clinical nursing · 2026Article
- Evaluating Large Language Models in Clinical Audiology (AUDIOLOGYBENCH): Benchmark Development and Validation Study.Journal of medical Internet research · 2026Article
- Local Deployment of Open-Weight Language Models in Dermatology: Viewpoint on Privacy, Equity, and Practical Implementation.JMIR dermatology · 2026Article
- Predicting perceived learning effectiveness from generative artificial intelligence in higher education: a hierarchical regression analysis of psychological, behavioral, and cognitive factors.Cognitive processing · 2026Article
- Mapping the generative era: a bibliometric and specialty-focused analysis of large language models in healthcare.Visual computing for industry, biomedicine, and art · 2026Article
- Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review.JMIR cancer · 2026Article
- An Acceptance Criteria Framework for Determining the Implementation Fit of Custom Large Language Models in Public Health Interventions.Journal of medical Internet research · 2026Article
- Rethinking AI in youth mental health: promise, perils, and ethical integration.International journal of mental health systems · 2026Article
- Performance, safety, and limitations of multimodal large language models in wound image assessment.Scientific reports · 2026Article
- Development and Implementation of an AI System for Generating Clinical Urine Drug Test Sign-Outs.JAMA network open · 2026Article
- Early Implications for Solid Organ Transplantation With the Use of Artificial Intelligence From a Bibliometric Perspective.Mayo Clinic proceedings. Digital health · 2026Review
- ac4C modification sites prediction in human mRNA: a complete review.Briefings in bioinformatics · 2026Review
- Potential of large language models for rapid clinical information support: evidence from acute kidney injury knowledge testing.Scientific reports · 2026Article
- Accuracy of ChatGPT and DeepSeek in answering clinical questions from the 2025 Society for Cardiovascular Angiography & Interventions/Heart Rhythm Society left atrial appendage occlusion guidelines.The Journal of international medical research · 2026Article
- Mass Media Narratives of Psychiatric Adverse Events Associated With Generative AI Chatbots: Rapid Scoping Review.JMIR mental health · 2026Article
- Evaluation of Information Generated by ChatGPT on Preventing Peripheral Venous Catheter-Related Infections.Nursing open · 2026Article
- Recent Advances in AI and GenAI for Health Informatics.Healthcare (Basel, Switzerland) · 2026Review
- Ethical Imperatives for Retrieval-Augmented Generation in Clinical Nursing: Viewpoint on Responsible AI Use.JMIR medical informatics · 2026Article
77 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large Language Models (LLMs) are a key component of generative artificial intelligence (AI) applications for creating new content including text, imagery, audio, code, and videos in response to textual instructions. Without human oversight, guidance and responsible design and operation, such generative AI applications will remain a party trick with substantial potential for creating and spreading misinformation or harmful and inaccurate content at unprecedented scale. However, if positioned and developed responsibly as companions to humans augmenting but not replacing their role in decision making, knowledge retrieval and other cognitive processes, they could evolve into highly efficient, trustworthy, assistive tools for information management. This perspective describes how such tools could transform data management workflows in healthcare and medicine, explains how the underlying technology works, provides an assessment of risks and limitations, and proposes an ethical, technical, and cultural framework for responsible design, development, and deployment. It seeks to incentivise users, developers, providers, and regulators of generative AI that utilises LLMs to collectively prepare for the transformational role this technology could play in evidence-based sectors.
Indexed as
Identifiers
What OpenQuestion holds
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.