ReviewNPJ digital medicine2025
A perspective for adapting generalist AI to specialized medical AI applications and their challenges.
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Precision Grounding: augmenting large language models with evidence-based databases for trustworthy genetic variant summarization.International journal of medical informatics · 2026Article
- Performance of Large Language Models on the Brazilian National Medical Education Examination: Comparative Benchmark Study.JMIR medical education · 2026Article
- Disease Risk Prediction Using Structured EHR Data: Can Generalist Large Language Models Match Specialized Clinical Foundation Models? A Comparative Evaluation with Fine-Tuning.medRxiv : the preprint server for health sciences · 2026Article
- Important Ethical, Technical, and Epidemiological Considerations in an AI Tool Production (ETEPAI): Scoping Review.JMIR AI · 2026Review
- Integrating Fine-Tuning and Retrieval-Augmented Generation for Healthcare AI Systems: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- Holistic AI in medicine; improved performance and explainability.NPJ digital medicine · 2026Article
- Evaluating AI adoption challenges in healthcare using a Multi-Criteria Decision-Making approach: implications for predictive risk analytics.Frontiers in artificial intelligence · 2026Article
- Toward clinical integration of generative AI in mental health: personalization, multimodality and inter-entity experience.Frontiers in public health · 2026Article
- AI in biocuration: challenges, opportunities, and a roadmap for sustainable integration.Bioinformatics advances · 2026Article
- A foundation model for human-AI collaboration in medical literature mining.Nature communications · 2025Article
- Artificial intelligence-assisted analysis of musculoskeletal imaging-A narrative review of the current state of machine learning models.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
We introduce a framework to adapt large language models for medicine: (1) Modeling: breaking down medical workflows into manageable steps; (2) Optimization: optimizing model performance via advanced adaptations; and (3) System engineering: developing agent or chain systems. Furthermore, we describe varied use cases, such as clinical trial design, clinical decision support, and medical imaging analysis. Finally, we discuss challenges and considerations for building medical AI with 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.