{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_glass-health","slug":"glass-health","name":"glass.health","type":"product","url":"https://glass.health","page_url":"https://unfragile.ai/glass-health","categories":["data-analysis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_glass-health__cap_0","uri":"capability://planning.reasoning.clinical.context.aware.differential.diagnosis.generation","name":"clinical-context-aware differential diagnosis generation","description":"Accepts unstructured clinical presentation data (chief complaint, history of present illness, physical exam findings, lab results) and generates ranked differential diagnosis lists using LLM reasoning with embedded medical knowledge. The system processes free-text clinical narratives through prompt engineering that enforces structured diagnostic reasoning, prioritizing conditions by epidemiological likelihood and clinical relevance rather than simple keyword matching. Architecture relies on few-shot prompting with real clinical case examples to guide the LLM toward clinically sound differential generation.","intents":["I need a second opinion on what conditions to consider for this complex patient presentation","I want to ensure I'm not missing rare diagnoses in a time-pressured clinical setting","I need to generate a structured differential diagnosis list from unstructured clinical notes"],"best_for":["Emergency medicine physicians evaluating undifferentiated presentations","Primary care clinicians in resource-limited settings without specialist access","Hospitalists managing complex multi-system cases","Rare disease specialists seeking validation of diagnostic hypotheses"],"limitations":["No access to real-time patient data or EHR integration—requires manual copy-paste of clinical information, introducing transcription errors and incompleteness","LLM hallucination risk for rare conditions with limited training data representation; may generate plausible-sounding but medically inaccurate differentials","No validation against ground-truth diagnoses; lacks published accuracy metrics for sensitivity/specificity across condition categories","Reasoning transparency limited to LLM-generated explanations, which may rationalize incorrect suggestions post-hoc","No ability to weight patient-specific risk factors (age, comorbidities, medications) beyond what's explicitly stated in the input text"],"requires":["Web browser with internet connectivity","Clinical knowledge sufficient to recognize and correct LLM errors","Ability to formulate structured clinical presentations (no automatic EHR data extraction)"],"input_types":["unstructured clinical text (chief complaint, HPI, physical exam, labs)","structured clinical data (vital signs, lab values, imaging findings)"],"output_types":["ranked differential diagnosis list with clinical reasoning","evidence citations linking suggestions to clinical features"],"categories":["planning-reasoning","medical-decision-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_glass-health__cap_1","uri":"capability://text.generation.language.evidence.based.clinical.reasoning.explanation","name":"evidence-based clinical reasoning explanation","description":"For each differential diagnosis suggestion, the system generates a natural-language explanation of the clinical logic connecting the patient's presentation to the suggested condition. This works by prompting the LLM to explicitly state which clinical features (symptoms, signs, labs) support each diagnosis and how they align with epidemiological or pathophysiological patterns. The explanation layer enables clinicians to verify reasoning rather than blindly accepting suggestions, functioning as a transparency mechanism for AI-assisted decision-making.","intents":["I need to understand WHY the AI suggested this diagnosis so I can evaluate if it's clinically sound","I want to verify the AI's reasoning against my own clinical knowledge before acting on suggestions","I need to document the diagnostic reasoning process for medical-legal purposes"],"best_for":["Clinicians with strong diagnostic reasoning skills who want to use AI as a thinking partner rather than an oracle","Teaching hospitals and residency programs using AI to scaffold diagnostic reasoning","Healthcare organizations concerned about liability and requiring explainable AI decisions"],"limitations":["Explanations are LLM-generated and may be plausible-sounding but medically incorrect or incomplete","No formal validation that explanations match actual clinical evidence or guidelines","Clinicians may experience 'automation bias' and accept explanations uncritically if they sound authoritative","Explanations cannot cite specific literature or guidelines; references are approximate and may be inaccurate","No ability to explain why certain high-probability diagnoses were NOT suggested"],"requires":["Clinical expertise to evaluate explanation quality","Access to medical literature or guidelines for independent verification"],"input_types":["differential diagnosis suggestion (from prior capability)"],"output_types":["natural-language explanation of clinical reasoning","mapping of patient features to diagnostic criteria"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_glass-health__cap_2","uri":"capability://planning.reasoning.rare.and.complex.condition.coverage.via.broad.llm.knowledge","name":"rare and complex condition coverage via broad llm knowledge","description":"Leverages the broad training data of large language models to surface rare diagnoses and complex condition combinations that might be overlooked in time-pressured clinical environments. The system works by encoding the patient presentation and allowing the LLM to generate differentials across its entire knowledge base without filtering to 'common' diagnoses. This is particularly effective for zebra cases, atypical presentations of common diseases, and rare genetic or infectious conditions where clinician familiarity is low.","intents":["I'm seeing an unusual presentation and want to ensure I'm not missing a rare diagnosis","I need to consider zebra cases and atypical presentations that might not come to mind immediately","I want to expand my differential beyond the most common diagnoses in my specialty"],"best_for":["Clinicians in resource-limited settings without access to rare disease specialists","Hospitalists and emergency physicians managing undifferentiated cases","Rare disease specialists seeking validation of diagnostic hypotheses","Teaching settings where exposure to rare conditions is limited"],"limitations":["LLM knowledge cutoff means recent disease outbreaks or newly described conditions may not be included","Rare condition suggestions may be statistically improbable given the patient's demographics and presentation, leading to false leads","No epidemiological weighting—a very rare condition may be suggested with equal prominence as a common one","Rare disease suggestions may lack clinical validation and could reflect LLM hallucinations rather than real medical entities","No ability to filter suggestions by geographic prevalence, making suggestions potentially irrelevant in certain regions"],"requires":["Clinical judgment to evaluate the plausibility of rare disease suggestions","Access to medical literature or specialists for verification of rare diagnoses"],"input_types":["clinical presentation (symptoms, signs, labs, imaging)"],"output_types":["differential diagnosis list including rare conditions","brief description of rare conditions and their key features"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_glass-health__cap_3","uri":"capability://data.processing.analysis.unstructured.clinical.text.processing.and.normalization","name":"unstructured clinical text processing and normalization","description":"Accepts free-text clinical narratives (chief complaint, history of present illness, physical exam notes, lab result descriptions) and processes them through the LLM to extract and normalize clinical information into a structured format suitable for diagnostic reasoning. The system uses prompt engineering to guide the LLM to identify key clinical features, temporal relationships, and severity indicators from unstructured text. This enables clinicians to input data in their natural documentation style without requiring structured data entry.","intents":["I want to paste my clinical notes directly without reformatting into structured fields","I need to extract key clinical features from a lengthy, unstructured patient narrative","I want to ensure the AI understands the temporal sequence and severity of symptoms"],"best_for":["Busy clinicians who document in free-text and want minimal friction to use AI tools","Settings with heterogeneous EHR systems where structured data export is difficult","Clinicians who prefer narrative documentation over structured templates"],"limitations":["LLM may misinterpret clinical abbreviations, regional terminology, or shorthand notation","No validation that extracted features match the clinician's intent; errors in interpretation are silent","Negations and conditional statements may be mishandled (e.g., 'no fever' vs. 'fever ruled out')","Temporal relationships may be ambiguous in unstructured text and LLM interpretation may be incorrect","No feedback loop to correct misinterpretations; clinicians must manually verify extracted features"],"requires":["Clinical documentation in English (language support unknown for other languages)","Reasonably clear clinical narrative (severely fragmented or abbreviated notes may not process well)"],"input_types":["unstructured clinical text (free-form narrative)"],"output_types":["structured clinical data (extracted symptoms, signs, labs, temporal sequence)","normalized clinical features suitable for diagnostic reasoning"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_glass-health__cap_4","uri":"capability://tool.use.integration.point.of.care.diagnostic.decision.support.without.ehr.integration","name":"point-of-care diagnostic decision support without ehr integration","description":"Provides diagnostic support at the moment of clinical decision-making through a web interface that requires manual input of clinical data rather than automatic EHR integration. The system is designed for rapid access and minimal setup—clinicians can open the tool, paste or type clinical information, and receive differential diagnoses within seconds. This architecture trades integration friction for deployment simplicity and avoids complex EHR API dependencies.","intents":["I need diagnostic support right now while I'm with the patient, without waiting for EHR integration setup","I want to use this tool across multiple EHR systems without custom integration for each","I need a tool that works in resource-limited settings without complex IT infrastructure"],"best_for":["Clinicians in resource-limited settings with minimal IT infrastructure","Emergency departments and urgent care settings where speed is critical","Clinicians working across multiple healthcare systems with different EHRs","Settings where EHR integration is not feasible due to security or compliance constraints"],"limitations":["Manual data entry creates friction and reduces adoption compared to automatic EHR integration","Transcription errors and incompleteness when manually entering clinical data","No real-time access to patient history, medications, or prior diagnoses","No ability to track diagnostic accuracy or learn from outcomes","No integration with clinical workflows; requires context-switching away from EHR"],"requires":["Web browser with internet connectivity","Ability to manually enter or copy-paste clinical information","No EHR integration required (works independently)"],"input_types":["manual text entry or copy-paste of clinical data"],"output_types":["differential diagnosis suggestions","clinical reasoning explanations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_glass-health__cap_5","uri":"capability://automation.workflow.free.access.diagnostic.support.without.subscription.barriers","name":"free-access diagnostic support without subscription barriers","description":"Provides full access to differential diagnosis generation and clinical reasoning explanations without requiring payment, subscription, or institutional licensing. The business model removes financial barriers to adoption, allowing individual clinicians to experiment with AI-assisted diagnostics regardless of their institution's budget or purchasing decisions. This is implemented through a freemium model where core diagnostic functionality is available without payment.","intents":["I want to try AI-assisted diagnostics without my institution paying for a subscription","I need diagnostic support but my healthcare system hasn't adopted AI tools yet","I want to evaluate whether AI diagnostics would be useful before recommending institutional adoption"],"best_for":["Individual clinicians in resource-limited settings or low-income countries","Clinicians in healthcare systems that haven't yet adopted AI tools","Early adopters and innovators evaluating AI diagnostics","Residents and fellows learning diagnostic reasoning"],"limitations":["Free model may not be sustainable long-term; service could be discontinued or moved to paid-only","No guaranteed uptime or service level agreements for free tier","Potential data privacy concerns with free service (unclear data retention and usage policies)","No institutional support or liability coverage for free users","Limited feature set compared to potential paid tiers (unknown what premium features might exist)"],"requires":["No payment or institutional licensing required","Web browser and internet connectivity"],"input_types":["clinical presentation data"],"output_types":["differential diagnosis suggestions","clinical reasoning explanations"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_glass-health__cap_6","uri":"capability://planning.reasoning.multi.system.clinical.feature.integration.for.holistic.differential.generation","name":"multi-system clinical feature integration for holistic differential generation","description":"Accepts clinical data across multiple organ systems and integrates them into a unified differential diagnosis that considers multi-system involvement and systemic conditions. The system uses LLM reasoning to identify patterns that span multiple systems (e.g., fever + rash + joint pain + eye inflammation → systemic inflammatory condition) rather than generating separate differentials for each system. This enables consideration of connective tissue diseases, vasculitides, infections, and other conditions that present with multi-system involvement.","intents":["I have a patient with findings in multiple organ systems and need to consider systemic conditions","I want to identify patterns that connect seemingly unrelated symptoms across different systems","I need to avoid anchoring on single-system diagnoses when the presentation is actually multi-system"],"best_for":["Internists and hospitalists managing complex multi-system presentations","Rheumatologists evaluating connective tissue diseases","Infectious disease specialists considering systemic infections","Emergency physicians evaluating undifferentiated multi-system presentations"],"limitations":["LLM may miss subtle multi-system patterns or generate spurious connections between unrelated findings","No validation that identified patterns correspond to real clinical entities","Weighting of different systems may not reflect clinical importance (e.g., cardiac findings weighted equally to minor rash)","No ability to consider temporal relationships between system involvement (e.g., which system was affected first)","Rare multi-system conditions may be underrepresented in LLM training data"],"requires":["Clinical data from multiple organ systems","Ability to recognize and evaluate multi-system patterns"],"input_types":["clinical data from multiple systems (constitutional, respiratory, cardiovascular, GI, neuro, rheumatologic, dermatologic, etc.)"],"output_types":["differential diagnosis list emphasizing multi-system conditions","pattern recognition explanations connecting findings across systems"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"low","permissions":["Web browser with internet connectivity","Clinical knowledge sufficient to recognize and correct LLM errors","Ability to formulate structured clinical presentations (no automatic EHR data extraction)","Clinical expertise to evaluate explanation quality","Access to medical literature or guidelines for independent verification","Clinical judgment to evaluate the plausibility of rare disease suggestions","Access to medical literature or specialists for verification of rare diagnoses","Clinical documentation in English (language support unknown for other languages)","Reasonably clear clinical narrative (severely fragmented or abbreviated notes may not process well)","Ability to manually enter or copy-paste clinical information"],"failure_modes":["No access to real-time patient data or EHR integration—requires manual copy-paste of clinical information, introducing transcription errors and incompleteness","LLM hallucination risk for rare conditions with limited training data representation; may generate plausible-sounding but medically inaccurate differentials","No validation against ground-truth diagnoses; lacks published accuracy metrics for sensitivity/specificity across condition categories","Reasoning transparency limited to LLM-generated explanations, which may rationalize incorrect suggestions post-hoc","No ability to weight patient-specific risk factors (age, comorbidities, medications) beyond what's explicitly stated in the input text","Explanations are LLM-generated and may be plausible-sounding but medically incorrect or incomplete","No formal validation that explanations match actual clinical evidence or guidelines","Clinicians may experience 'automation bias' and accept explanations uncritically if they sound authoritative","Explanations cannot cite specific literature or guidelines; references are approximate and may be inaccurate","No ability to explain why certain high-probability diagnoses were NOT suggested","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"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":"2026-05-24T12:16:30.892Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=glass-health","compare_url":"https://unfragile.ai/compare?artifact=glass-health"}},"signature":"HLCLLv4ABsuuWBVXvyo+9Dz0cnFalaTJ0Nb6CEPatT2wAcTWG6YUROEAPwvX9IZgKVdN0WSyrh7PMrhVHfckBw==","signedAt":"2026-06-21T01:54:12.090Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/glass-health","artifact":"https://unfragile.ai/glass-health","verify":"https://unfragile.ai/api/v1/verify?slug=glass-health","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"}}