ArticleNPJ digital medicine2025
A phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs.
Article 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 19 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
19 citing papers in PubMed.
- Artificial Intelligence in Neuromuscular Diseases: Opportunities for a Data-Scarce Field.Neurology and therapy · 2026Review
- Machine Learning, Large Language Models, and Multimodal AI for Diagnosing Pediatric Rare Diseases: Scoping Review.Journal of medical Internet research · 2026Article
- Artificial Intelligence in Clinical Genetics: Current Applications and Challenges.Indian journal of pediatrics · 2026Review
- Artificial intelligence for pediatric rare disease diagnosis: a multimethod study integrating published evidence and clinician interviews.BMC medical informatics and decision making · 2026Article
- A No-Code, Guideline-Based Custom GPT Outperforms Cardiologists in Response Quality for Cardiac Amyloidosis.JACC. Advances · 2026Article
- From presence to provenance: Building substantive infrastructure for China's rare disease ecosystem.Intractable & rare diseases research · 2026Review
- Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.Clinics and practice · 2026Review
- Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care.Patterns (New York, N.Y.) · 2026Review
- Reframing AI for Rare Disease Recognition.Research square · 2026Article
- GEN-KnowRD: Reframing AI for Rare Disease Recognition.medRxiv : the preprint server for health sciences · 2026Article
- Article
- ChatGPT in precision medicine.APL bioengineering · 2026Review
- Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage.NPJ digital medicine · 2026Article
- Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction.NPJ digital medicine · 2026Article
- Moving beyond monogenic disorders in clinical healthcare.Nature biotechnology · 2026Article
- Advancing the frontier of rare disease modeling: a critical appraisal of in silico technologies.NPJ digital medicine · 2025Review
- An intelligent healthcare system for rare disease diagnosis utilizing electronic health records based on a knowledge-guided multimodal transformer framework.BioData mining · 2025Article
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- A large language model improves clinicians' diagnostic performance in complex critical illness cases.Critical care (London, England) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rare diseases, affecting ~350 million people worldwide, pose significant challenges in clinical diagnosis due to the lack of experienced physicians and the complexity of differentiating between numerous rare diseases. To address these challenges, we introduce PhenoBrain, a fully automated artificial intelligence pipeline. PhenoBrain utilizes a BERT-based natural language processing model to extract phenotypes from clinical texts in EHRs and employs five new diagnostic models for differential diagnoses of rare diseases. The AI system was developed and evaluated on diverse, multi-country rare disease datasets, comprising 2271 cases with 431 rare diseases. In 1936 test cases, PhenoBrain achieved an average predicted top-3 recall of 0.513 and a top-10 recall of 0.654, surpassing 13 leading prediction methods. In a human-computer study with 75 cases, PhenoBrain exhibited exceptional performance with a top-3 recall of 0.613 and a top-10 recall of 0.813, surpassing the performance of 50 specialist physicians and large language models like ChatGPT and GPT-4. Combining PhenoBrain's predictions with specialists increased the top-3 recall to 0.768, demonstrating its potential to enhance diagnostic accuracy in clinical workflows.
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.