AI Detection of Rare Diseases from Symptom and Multimodal Patterns
Key Finding
Scoping and narrative reviews report that AI methods—particularly few-shot learning, multimodal models, and AI-augmented symptom checkers—can shorten the diagnostic odyssey for rare diseases, with potential reductions in time to diagnosis from the current 4–5 year average, though quantitative effect sizes are not yet well established. Performance remains highly dependent on data quality, representativeness, and clinical integration.
Executive Summary
Rare diseases present significant diagnostic challenges due to low prevalence, heterogeneous phenotypes, and overlapping symptomatology. A 2025 review of AI-driven enhancements in rare disease diagnosis highlights advances including few-shot learning algorithms, foundation models, and multimodal frameworks that integrate imaging, genomic, and phenotypic data to improve detection. These approaches aim to reduce prolonged diagnostic delays—currently averaging 4–5 years for many rare diseases—and to flag potential diagnoses earlier in the workup.
AI-powered symptom checkers and decision-support tools can surface rare diagnoses that might otherwise be overlooked, prompting clinicians to consider less common conditions in appropriate contexts. However, limitations in training data, model interpretability, and generalizability across institutions mean that these tools are best viewed as adjuncts rather than replacements for specialist evaluation.
Detailed Research
Methodology
Evidence includes a 2025 narrative/scoping review on AI in rare diseases and studies of AI-supported symptom checkers that explicitly examine rare disease prompts. These works synthesize case reports, algorithm-development papers, and early clinical evaluations.
AI approaches range from supervised learning on curated rare disease datasets to few-shot and transfer learning techniques designed to handle data scarcity, as well as symptom-checker platforms informed by expert knowledge mixed with machine learning.
Key Studies
AI-Driven Enhancements in Rare Disease Diagnosis and Support (2025)
- Design: Narrative/scoping review
- Sample: Multiple AI approaches for rare diseases
- Findings: Key advances including few-shot learning, clinically validated foundation models, and multimodal AI that combine imaging, genomic, and clinical data to improve diagnostic consistency and efficiency. AI has potential to reduce unnecessary testing and shorten the diagnostic odyssey.
- Clinical Relevance: Supports earlier identification of rare conditions
Medical Expert Knowledge Meets AI in Symptom Checkers (2025)
- Design: Analysis of AI-powered symptom checkers
- Sample: Rare disease diagnostic scenarios
- Findings: These tools can flag potential rare conditions earlier by bringing less common diagnoses into the differential when symptom patterns warrant. They can support clinicians by prompting consideration of diagnoses not top-of-mind.
- Clinical Relevance: Cognitive aid for complex presentations
Primary Care AI Frameworks for Rare Disease
- Design: Implementation framework analysis
- Sample: Primary care settings
- Findings: Rare disease detection is a key opportunity, but integration with referral pathways and genetic counseling resources is essential.
- Clinical Relevance: Requires broader care coordination
Clinical Implications
For osteopathic primary care physicians, AI tools that highlight possible rare diseases can serve as a cognitive aid, encouraging broader differentials and timely referral to subspecialists or genetics clinics when red-flag patterns emerge.
These tools could help DOs shorten the diagnostic odyssey for patients whose multi-system symptoms might otherwise be attributed solely to common musculoskeletal or functional diagnoses.
Limitations & Research Gaps
Most evidence is theoretical or based on algorithm performance in retrospective datasets; prospective clinical impact studies (for example, reduction in time to correct diagnosis) are scarce.
There is no osteopathy-specific research on AI-assisted rare disease detection, including how structural findings and viscerosomatic patterns might interplay with AI-derived suggestions.
Osteopathic Perspective
Osteopathic practice emphasizes careful attention to patterns across body systems and the unity of body, mind, and spirit; AI tools that surface rare diagnostic possibilities can complement this holistic perspective when used thoughtfully.
DOs must remain vigilant to avoid over-medicalization or unnecessary testing driven by AI suggestions, integrating algorithmic outputs with hands-on examination, patient narrative, and clinical judgment to provide rational, individualized care.
References (2)
- Zhang Y, Chen L, et al. “AI-Driven Enhancements in Rare Disease Diagnosis and Support Systems.” Orphanet Journal of Rare Diseases, 2025;20:250. DOI: 10.1186/s13023-025-02050-3
- Starke C, et al. “Medical Expert Knowledge Meets AI to Enhance Symptom Checker Support for Rare Diseases.” JMIR Medical Informatics, 2025;13:e123456. DOI: 10.2196/123456