ArticleNPJ digital medicine2024
Probabilistic medical predictions of large language models.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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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
22 citing papers in PubMed.
- Improving End-of-Life Screening in the Emergency Department With Collaborative Artificial Intelligence.Annals of emergency medicine · 2026Article
- Multimodal large language models for bladder tumor detection in cystoscopy: a retrospective benchmarking study.World journal of urology · 2026Article
- A Multidisciplinary Team-Based Large Language Model Framework for Predicting Postoperative Neurological Complications in Acute Type A Aortic Dissection: Model Development and Validation Study.Journal of medical Internet research · 2026Article
- Review
- Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study.JMIR mental health · 2026Observational
- Clinical large language model centered on electronic medical records.NPJ digital medicine · 2026Article
- BRIDGE: benchmarking large language models for understanding real-world clinical practice texts.Nature biomedical engineering · 2026Article
- Evaluating the Ability of Multimodal Artificial Intelligence to Identify Endodontic Instruments: A Comparative Study of ChatGPT-4o and Gemini 3 Flash.Journal of clinical medicine · 2026Article
- A real-world feasibility evaluation of LLM-based clinical prediction: emergency department return visit admission across two academic medical centers.Research square · 2026Article
- Participatory Digital Twins for Chronic Care: From Predictive Models to Shared Sensemaking.Journal of participatory medicine · 2026Article
- Blinded two-phase evaluation of large language models in complex cardiac surgery: task-specific performance and human-AI collaboration.Frontiers in digital health · 2026Article
- Performance and limitations of four large language models in genetic counseling for thalassemia.Frontiers in digital health · 2026Article
- Confident but unsupported: auditing large language models against supplied evidence boundaries in drug-induced liver injury assessment.Frontiers in public health · 2026Article
- Enhancing the prediction of hospital discharge disposition with extraction-based language model classification.npj health systems · 2026Article
- Enhancing Developmental Language Disorder Identification with Artificial Intelligence: Development of an Explainable Screening App Using Real and Synthetic Data.Journal of autism and developmental disorders · 2025Article
- MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling.NPJ digital medicine · 2025Article
- Evaluating the reliability of large language models for clinical data extraction in bladder cancer prognosis.Scientific reports · 2025Article
- Implications of integrating large language models into clinical decision making.Communications medicine · 2025Review
- AI-generated neurology consultation summaries improve efficiency and reduce documentation burden in the emergency department.Scientific reports · 2025Article
- A statistical framework for evaluating the repeatability and reproducibility of large language models.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Large Language Models (LLMs) have shown promise in clinical applications through prompt engineering, allowing flexible clinical predictions. However, they struggle to produce reliable prediction probabilities, which are crucial for transparency and decision-making. While explicit prompts can lead LLMs to generate probability estimates, their numerical reasoning limitations raise concerns about reliability. We compared explicit probabilities from text generation to implicit probabilities derived from the likelihood of predicting the correct label token. Across six advanced open-source LLMs and five medical datasets, explicit probabilities consistently underperformed implicit probabilities in discrimination, precision, and recall. This discrepancy is more pronounced with smaller LLMs and imbalanced datasets, highlighting the need for cautious interpretation, improved probability estimation methods, and further research for clinical use of LLMs.
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.