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ARXIV:2601.18132 · HEALTHTECH · SUBMITTED 17 MAR · 21:43 UTC · FRESHNESS STALE
ARXIV:2601.18132HEALTHTECHSUBMITTED 17 MAR · 21:43 UTCFRESHNESS STALEarXiv
RareAlert provides early risk screening for rare diseases using calibrated LLM reasoning, facilitating quicker diagnosis at primary clinical encounters.
Opportunity summary
Pain RareAlert provides early risk screening for rare diseases using calibrated LLM reasoning, facilitating quicker diagnosis at primary clinical encounters.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
RareAlert provides early risk screening for rare diseases using calibrated LLM reasoning, facilitating quicker diagnosis at primary clinical encounters. At the initial clinical encounters, physicians assess rare disease risk using only limited information under…
Missed and delayed diagnosis remains a major challenge in rare disease care. At the initial clinical encounters, physicians assess rare disease risk using only limited information under high uncertainty.
ScienceToStartup currently rates this 8.0/10 on the public viability pass. The results showed that rare disease identification can be reconceptualised as a universal uncertainty resolution process applied to the general patient population.
HealthTech moved forward this cycle; last verified April 2026. Public score 8.0/10.
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RareAlert provides early risk screening for rare diseases using calibrated LLM reasoning, facilitating quicker diagnosis at primary clinical encounters.
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10.48550/arXiv.2601.18132RareAlert provides early risk screening for rare diseases using calibrated LLM reasoning, facilitating quicker diagnosis at primary clinical encounters.
Abstract
Missed and delayed diagnosis remains a major challenge in rare disease care. At the initial clinical encounters, physicians assess rare disease risk using only limited information under high uncertainty. When high-risk patients are not recognised at this stage, targeted diagnostic testing is often not initiated, resulting in missed diagnosis. Existing primary care triage processes are structurally insufficient to reliably identify patients with rare diseases at initial clinical presentation and universal screening is needed to reduce diagnostic delay. Here we present RareAlert, an early screening system which predict patient-level rare disease risk from routinely available primary-visit information. RareAlert integrates reasoning generated by ten LLMs, calibrates and weights these signals using machine learning, and distils the aligned reasoning into a single locally deployable model. To develop and evaluate RareAlert, we curated RareBench, a real-world dataset of 158,666 cases covering 33 Orphanet disease categories and more than 7,000 rare conditions, including both rare and non-rare presentations. The results showed that rare disease identification can be reconceptualised as a universal uncertainty resolution process applied to the general patient population. On an independent test set, RareAlert, a Qwen3-4B based model trained with calibrated reasoning signals, achieved an AUC of 0.917, outperforming the best machine learning ensemble and all evaluated LLMs, including GPT-5, DeepSeek-R1, Claude-3.7-Sonnet, o3-mini, Gemini-2.5-Pro, and Qwen3-235B. These findings demonstrate the diversity in LLM medical reasoning and the effectiveness of aligning such reasoning in highly uncertain clinical tasks. By incorporating calibrated reasoning into a single model, RareAlert enables accurate, privacy-preserving, and scalable rare disease risk screening suitable for large-scale local deployment.
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Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 8.0
PROBLEM
RareAlert provides early risk screening for rare diseases using calibrated LLM reasoning, facilitating quicker diagnosis at primary clinical encounters. At the initial clinical encounters, physicians assess rare disease risk using only limited information under high uncertainty.
METHOD
Missed and delayed diagnosis remains a major challenge in rare disease care. At the initial clinical encounters, physicians assess rare disease risk using only limited information under high uncertainty.
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. The results showed that rare disease identification can be reconceptualised as a universal uncertainty resolution process applied to the general patient population.
WHY NOW
HealthTech moved forward this cycle; last verified April 2026. Public score 8.0/10.
achieved an AUC of 0.917
Explicitly stated numeric result in the abstract with clear performance metric.
partial
outperforming the best machine learning ensemble and all evaluated LLMs, including GPT-5, DeepSeek-R1, Claude-3.7-Sonnet, o3-mini, Gemini-2.5-Pro, and Qwen3-235B
Direct comparison stated in abstract with specific model names listed.
partial
RareAlert integrates reasoning generated by ten LLMs... and distils the aligned reasoning into a single locally deployable model
Explicitly described method in abstract with specific number of LLMs.
partial
we curated RareBench, a real-world dataset of 158,666 cases covering 33 Orphanet disease categories and more than 7,000 rare conditions
Specific dataset details with exact numbers provided in abstract.
partial
RareAlert enables accurate, privacy-preserving, and scalable rare disease risk screening suitable for large-scale local deployment
Directly stated benefit in abstract, though implementation details are not fully specified.
partial
The main limitation could be the dependency on high-quality input data from initial clinical visits; inaccuracies or missing information could reduce efficacy
Explicitly stated limitation in analysis section, though not quantified.
partial
The results showed that rare disease identification can be reconceptualised as a universal uncertainty resolution process applied to the general patient population
Conceptual claim stated in abstract as a finding, but requires interpretation of what this reconceptualization entails.
partial
RareAlert, a Qwen3-4B based model trained with calibrated reasoning signals
Specific model architecture and training approach directly stated in abstract.
partial
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RareAlert provides early risk screening for rare diseases using calibrated LLM reasoning, facilitating quicker diagnosis at primary clinical encounters.
Segment
HealthTech
Adoption evidence
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Commercial read
8.0/10 public viability
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status
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reason
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proof status
unverified
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confidence low
next verification path
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Evidence coverage
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
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Technical feasibility
partial
Current read
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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Evidence
0 references, 0 sources, 33% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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