{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_rare-genie","slug":"rare-genie","name":"Rare genie","type":"product","url":"https://raregenie.com","page_url":"https://unfragile.ai/rare-genie","categories":["text-writing"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_rare-genie__cap_0","uri":"capability://data.processing.analysis.symptom.to.disease.pattern.matching.with.rare.disease.database.indexing","name":"symptom-to-disease pattern matching with rare disease database indexing","description":"Analyzes patient-reported symptoms and clinical presentations against a curated database of rare disease phenotypes using semantic matching and statistical pattern recognition. The system likely employs vector embeddings of symptom descriptions and disease manifestations to identify rare conditions that present atypically or with overlapping symptomatology, reducing the diagnostic search space from thousands of potential conditions to a ranked list of differential diagnoses with confidence scores.","intents":["I need to identify which rare diseases match this patient's symptom constellation when standard diagnostic workups have been inconclusive","I want to surface rare disease possibilities that might be overlooked due to atypical presentation or symptom overlap with common conditions","I need to accelerate differential diagnosis by systematically comparing patient data against rare disease phenotype databases rather than relying on clinician memory"],"best_for":["Diagnostic centers and teaching hospitals handling complex undiagnosed cases","Rare disease specialists seeking systematic differential diagnosis support","Primary care physicians encountering unusual symptom clusters"],"limitations":["Pattern matching accuracy depends entirely on training data quality and completeness of rare disease phenotype descriptions — missing or mischaracterized disease presentations will produce false negatives","Cannot replace clinical judgment; symptom-based matching alone cannot confirm diagnosis without confirmatory testing","Bias toward well-documented rare diseases; ultra-rare conditions with limited published case reports will be underrepresented in matching results","Requires structured symptom input; free-text clinical notes may lose diagnostic signal during parsing"],"requires":["Structured patient symptom data (onset, duration, severity, progression)","Access to rare disease phenotype database (internal or licensed)","Clinical validation dataset for model training and calibration","FDA or equivalent regulatory clearance for clinical decision support (not evident from public materials)"],"input_types":["structured symptom checklist","clinical narrative text","lab/imaging findings","patient medical history"],"output_types":["ranked differential diagnosis list with confidence scores","disease phenotype summaries","recommended diagnostic pathways","clinical literature references"],"categories":["data-processing-analysis","medical-diagnostics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rare-genie__cap_1","uri":"capability://data.processing.analysis.medical.history.contextualization.and.temporal.pattern.analysis","name":"medical history contextualization and temporal pattern analysis","description":"Integrates patient medical history, medication records, family history, and prior diagnostic workup results to build temporal context for symptom interpretation. The system likely constructs a patient timeline and identifies temporal correlations between symptom onset, medication changes, and prior test results, enabling detection of disease progression patterns or iatrogenic causes that isolated symptom matching would miss.","intents":["I need to understand how this patient's current symptoms relate to their prior medical events and medication history","I want to identify whether symptoms are consistent with disease progression versus new unrelated conditions","I need to rule out medication side effects or drug interactions as causes of the symptom presentation"],"best_for":["Complex patients with extensive medical histories and multiple prior diagnoses","Cases where diagnostic odyssey has involved multiple specialists and conflicting prior workups","Situations requiring integration of longitudinal patient data across multiple healthcare systems"],"limitations":["Requires complete and accurate medical history; gaps in documentation (especially from outside healthcare systems) will degrade contextual analysis","Cannot access real-time EHR data without direct integration; manual history entry introduces transcription errors and omissions","Temporal pattern analysis assumes accurate dating of events; vague patient recall ('sometime last year') reduces precision","No built-in capability to detect or correct conflicting information across multiple medical records"],"requires":["Structured medical history data (diagnoses, procedures, medications with dates)","Family history information","Prior diagnostic test results and imaging reports","EHR integration or manual data entry interface"],"input_types":["patient medical history timeline","medication list with dates","prior lab and imaging results","family history data"],"output_types":["temporal analysis summary","identified correlations between events","ruled-out differential diagnoses based on history","recommended additional historical data points"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rare-genie__cap_2","uri":"capability://search.retrieval.multi.source.medical.literature.and.case.report.retrieval","name":"multi-source medical literature and case report retrieval","description":"Searches and retrieves relevant medical literature, published case reports, and clinical guidelines related to identified differential diagnoses or symptom patterns. The system likely uses semantic search over indexed medical databases (PubMed, case report repositories, clinical guidelines) to surface relevant evidence, enabling clinicians to review published presentations of rare diseases that match the patient's presentation.","intents":["I need to find published case reports of rare diseases with similar presentations to validate a diagnosis hypothesis","I want to access clinical guidelines and diagnostic criteria for rare conditions identified by the AI","I need to review the latest literature on diagnostic approaches for a specific rare disease"],"best_for":["Clinicians seeking evidence-based validation of rare disease diagnoses","Rare disease specialists building diagnostic confidence through case comparison","Teaching hospitals and academic medical centers with research infrastructure"],"limitations":["Search results depend on indexing coverage; rare diseases with limited published literature will return sparse results","Cannot access paywalled journals or proprietary medical databases without institutional subscriptions","Literature retrieval is retrospective; cannot identify emerging or newly-described rare disease presentations","No built-in critical appraisal of retrieved literature quality or evidence strength"],"requires":["Access to medical literature databases (PubMed, Google Scholar, institutional subscriptions)","Semantic search infrastructure indexed over medical text","Integration with medical knowledge bases or APIs"],"input_types":["disease names or diagnostic hypotheses","symptom patterns","clinical presentation summaries"],"output_types":["ranked list of relevant case reports","clinical guidelines and diagnostic criteria","literature summaries with citations","evidence strength indicators"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rare-genie__cap_3","uri":"capability://planning.reasoning.diagnostic.pathway.recommendation.with.test.sequencing","name":"diagnostic pathway recommendation with test sequencing","description":"Generates recommended diagnostic workflows and test sequencing based on differential diagnoses, patient characteristics, and clinical context. The system likely uses decision tree logic or probabilistic reasoning to suggest which confirmatory tests, imaging studies, or genetic testing should be prioritized based on diagnostic yield, cost-effectiveness, and clinical urgency, reducing unnecessary testing and accelerating diagnosis.","intents":["I need to know which diagnostic tests to order next to confirm or rule out the suspected rare disease","I want to optimize test sequencing to minimize cost and time while maximizing diagnostic yield","I need guidance on whether genetic testing, specialized imaging, or other advanced diagnostics are warranted for this patient"],"best_for":["Diagnostic centers seeking to optimize testing workflows and reduce unnecessary procedures","Clinicians managing patients with limited diagnostic budgets or insurance constraints","Rare disease specialists designing efficient diagnostic algorithms"],"limitations":["Recommendations are only as good as underlying diagnostic evidence; rare diseases with limited diagnostic validation data will produce uncertain recommendations","Cannot account for patient-specific factors like comorbidities, contraindications, or access barriers without explicit input","Test availability varies by institution and geography; recommendations may not be actionable in resource-limited settings","No real-time integration with lab/imaging availability or turnaround times"],"requires":["Diagnostic criteria and test characteristics for rare diseases (sensitivity, specificity, cost)","Clinical decision support knowledge base","Patient demographic and clinical data"],"input_types":["differential diagnosis list","patient clinical presentation","prior test results","institutional resources and constraints"],"output_types":["recommended diagnostic test sequence","test rationale and expected diagnostic yield","cost-benefit analysis of testing options","timeline estimates for diagnosis"],"categories":["planning-reasoning","medical-diagnostics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rare-genie__cap_4","uri":"capability://planning.reasoning.diagnostic.confidence.scoring.and.uncertainty.quantification","name":"diagnostic confidence scoring and uncertainty quantification","description":"Assigns confidence scores and uncertainty estimates to diagnostic recommendations based on data completeness, symptom specificity, and disease prevalence. The system likely uses Bayesian reasoning or probabilistic modeling to quantify diagnostic uncertainty, explicitly flagging cases where additional data is needed or where multiple diagnoses remain plausible, preventing false confidence in inconclusive situations.","intents":["I need to understand how confident the AI is in its diagnostic recommendations and what factors are driving uncertainty","I want to know what additional clinical data would most improve diagnostic confidence","I need to communicate diagnostic uncertainty to the patient and care team appropriately"],"best_for":["Clinicians making high-stakes diagnostic decisions who need explicit uncertainty quantification","Healthcare systems implementing AI decision support with governance requirements for confidence thresholds","Rare disease specialists managing cases with incomplete or conflicting clinical data"],"limitations":["Confidence scores are only as reliable as the underlying probabilistic model; miscalibrated models can produce false confidence or excessive uncertainty","Cannot distinguish between 'insufficient data' and 'data suggests multiple diagnoses equally' without explicit uncertainty decomposition","Confidence scores may not align with clinician intuition or domain expertise, creating trust issues","No built-in mechanism to update confidence as new clinical data becomes available"],"requires":["Probabilistic diagnostic model with calibration data","Quantified uncertainty estimates for disease prevalence and test characteristics","Completeness assessment of input clinical data"],"input_types":["patient clinical data","differential diagnosis list with probabilities","test results and their reliability"],"output_types":["confidence scores for each diagnosis","uncertainty decomposition (data gaps, conflicting evidence, etc.)","recommended data collection to improve confidence","diagnostic probability distributions"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rare-genie__cap_5","uri":"capability://tool.use.integration.institutional.ehr.integration.and.data.normalization","name":"institutional ehr integration and data normalization","description":"Integrates with hospital EHR systems to automatically extract patient data (symptoms, medical history, lab results, imaging reports) and normalizes heterogeneous data formats into standardized clinical data models. The system likely uses HL7/FHIR standards or custom EHR connectors to map institution-specific data schemas into normalized formats, enabling seamless data flow without manual entry.","intents":["I want to automatically pull patient data from our EHR without manual data entry or copy-paste errors","I need to ensure data consistency across our institution's multiple EHR systems or legacy systems","I want to integrate Rare Genie into our existing clinical workflows without disrupting EHR operations"],"best_for":["Large hospital systems with mature EHR infrastructure and IT governance","Multi-site healthcare networks with heterogeneous EHR systems requiring data normalization","Institutions with dedicated health IT teams to manage integrations"],"limitations":["EHR integration complexity varies dramatically by vendor and institution; custom development may be required for legacy systems","Data normalization introduces latency; real-time data availability depends on EHR API performance","HIPAA compliance and data governance requirements add implementation overhead; data residency and encryption must be verified","EHR data quality issues (missing fields, inconsistent coding) propagate into diagnostic analysis; garbage in, garbage out"],"requires":["HL7/FHIR API access or custom EHR connector development","HIPAA-compliant data handling and encryption infrastructure","IT governance and security review processes","EHR vendor cooperation or API documentation"],"input_types":["EHR API endpoints","patient identifiers (MRN, medical record number)","data mapping specifications"],"output_types":["normalized patient data in standard clinical data models","data quality reports and missing field indicators","audit logs of data access and usage"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rare-genie__cap_6","uri":"capability://safety.moderation.bias.detection.and.fairness.monitoring.for.diagnostic.recommendations","name":"bias detection and fairness monitoring for diagnostic recommendations","description":"Monitors diagnostic recommendations for demographic bias (e.g., underdiagnosis in specific populations) and fairness issues that could perpetuate healthcare disparities. The system likely tracks diagnostic accuracy and recommendation patterns across demographic groups, flagging cases where certain populations receive systematically different diagnostic pathways or confidence scores for equivalent clinical presentations.","intents":["I need to ensure our AI diagnostic tool doesn't perpetuate healthcare disparities or miss diagnoses in underrepresented populations","I want to monitor whether the AI recommends different diagnostic pathways for equivalent presentations across demographic groups","I need evidence of fairness and bias mitigation for regulatory compliance and institutional governance"],"best_for":["Healthcare systems with equity and diversity commitments seeking to audit AI tools","Institutions subject to regulatory scrutiny on healthcare disparities","Rare disease networks studying diagnostic disparities across populations"],"limitations":["Bias detection requires large datasets stratified by demographic groups; rare diseases with small patient populations may lack statistical power for fairness analysis","Defining 'fairness' in rare disease diagnosis is complex; equal diagnostic accuracy across groups may not be achievable if disease prevalence differs","Bias monitoring is retrospective; cannot prevent bias in real-time recommendations without explicit fairness constraints","No transparency on what demographic data is collected or how it's used; privacy concerns may limit demographic stratification"],"requires":["Demographic data collection (age, sex, race/ethnicity, socioeconomic status) with privacy protections","Diagnostic outcome tracking across demographic groups","Fairness metrics and thresholds defined by institution","Governance processes for responding to detected bias"],"input_types":["diagnostic recommendations with demographic data","diagnostic outcomes and confirmatory test results","demographic stratification variables"],"output_types":["fairness audit reports by demographic group","bias detection alerts","recommendations for bias mitigation","equity dashboards"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_rare-genie__cap_7","uri":"capability://automation.workflow.clinician.feedback.loop.and.model.retraining.pipeline","name":"clinician feedback loop and model retraining pipeline","description":"Captures clinician feedback on diagnostic recommendations (correct/incorrect diagnoses, useful/not useful suggestions) and feeds this data into model retraining pipelines to continuously improve diagnostic accuracy. The system likely implements active learning to identify high-uncertainty cases where clinician feedback is most valuable, and uses this feedback to update pattern matching models and confidence calibration.","intents":["I want the AI to learn from our institution's diagnostic outcomes and improve recommendations over time","I need to provide feedback on incorrect diagnoses so the system doesn't repeat the same mistakes","I want to contribute to improving rare disease diagnostic AI through our clinical experience"],"best_for":["Large healthcare systems with sufficient diagnostic volume to enable meaningful model retraining","Rare disease centers with specialized expertise willing to contribute to model improvement","Institutions with data science teams to manage retraining pipelines"],"limitations":["Feedback loop introduces data drift risk; institutional-specific patterns may not generalize to other healthcare systems","Retraining requires careful validation to avoid overfitting to local data or incorporating clinician biases","Feedback collection adds workflow burden on clinicians; low feedback rates limit model improvement","No clear governance on who owns retraining decisions or how institutional feedback influences shared models"],"requires":["Feedback collection interface integrated into clinical workflows","Diagnostic outcome tracking and validation","Model retraining infrastructure and validation pipelines","Data governance and model versioning"],"input_types":["clinician feedback on diagnostic recommendations","confirmed diagnoses and diagnostic outcomes","cases where recommendations were incorrect or unhelpful"],"output_types":["updated diagnostic models","improved confidence calibration","feedback analytics and insights","model performance reports"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Structured patient symptom data (onset, duration, severity, progression)","Access to rare disease phenotype database (internal or licensed)","Clinical validation dataset for model training and calibration","FDA or equivalent regulatory clearance for clinical decision support (not evident from public materials)","Structured medical history data (diagnoses, procedures, medications with dates)","Family history information","Prior diagnostic test results and imaging reports","EHR integration or manual data entry interface","Access to medical literature databases (PubMed, Google Scholar, institutional subscriptions)","Semantic search infrastructure indexed over medical text"],"failure_modes":["Pattern matching accuracy depends entirely on training data quality and completeness of rare disease phenotype descriptions — missing or mischaracterized disease presentations will produce false negatives","Cannot replace clinical judgment; symptom-based matching alone cannot confirm diagnosis without confirmatory testing","Bias toward well-documented rare diseases; ultra-rare conditions with limited published case reports will be underrepresented in matching results","Requires structured symptom input; free-text clinical notes may lose diagnostic signal during parsing","Requires complete and accurate medical history; gaps in documentation (especially from outside healthcare systems) will degrade contextual analysis","Cannot access real-time EHR data without direct integration; manual history entry introduces transcription errors and omissions","Temporal pattern analysis assumes accurate dating of events; vague patient recall ('sometime last year') reduces precision","No built-in capability to detect or correct conflicting information across multiple medical records","Search results depend on indexing coverage; rare diseases with limited published literature will return sparse results","Cannot access paywalled journals or proprietary medical databases without institutional subscriptions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:32.438Z","last_scraped_at":"2026-04-05T13:23:42.560Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=rare-genie","compare_url":"https://unfragile.ai/compare?artifact=rare-genie"}},"signature":"58Nb00zyn9mlO9khCGjCV+M6UQY9wEq+QewYh8O2AD5Kk7D+mPrOr4F92f/qKcfTy6LLtOzZGL7WUd8K5k//CQ==","signedAt":"2026-06-21T01:28:50.212Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rare-genie","artifact":"https://unfragile.ai/rare-genie","verify":"https://unfragile.ai/api/v1/verify?slug=rare-genie","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}