ReviewAnnual review of biomedical data science2025
Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare.
Review in Annual review of biomedical data science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Large Language Model Performance on Multistep Clinical Cases: Comparative Study Across Question and Case Levels.JMIR medical education · 2026Article
- CPS-Net (Collaborating Physicians in Silico Network): A Decentralized Multi-Agent Transformer Framework for Specialty-Aware Disease Prediction.Journal of medical systems · 2026Article
- Accuracy, Self-Reported Confidence, and Overconfidence of Large Language Models in Endodontics: An Evaluation Using National Specialty Examination Questions.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Tutorial: guidance on the use of large language models for medical research.Nature protocols · 2026Review
- Memorization in large language models in medicine prevalence characteristics and implications.Nature communications · 2026Article
- Evaluating the Applicability of Advanced Large Language Models in Laboratory Medicine Test Questions: A Comparative Performance Study.Advances in medical education and practice · 2026Article
- AI-generated medical realism and hantavirus misinformation: a new public health threat?Frontiers in digital health · 2026Article
- Exploring and Comparing the Use of Large Language Models in Supporting Osteoporosis Health Consultations.Clinical interventions in aging · 2025Article
Corrections and comments
- Update of
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) have gained significant attention in the medical domain for their human-level capabilities, leading to increased efforts to explore their potential in various healthcare applications. However, despite such a promising future, there are multiple challenges and obstacles that remain for their real-world uses in practical settings. This work discusses key challenges for LLMs in medical applications from four unique aspects: operational vulnerabilities, ethical and social considerations, performance and assessment difficulties, and legal and regulatory compliance. Addressing these challenges is crucial for leveraging LLMs to their full potential and ensuring their responsible integration into healthcare.
Indexed as
Identifiers
40198845What 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.