{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_unlearn-ai","slug":"unlearn-ai","name":"Unlearn.AI","type":"product","url":"https://www.unlearn.ai","page_url":"https://unfragile.ai/unlearn-ai","categories":["research-search"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_unlearn-ai__cap_0","uri":"capability://research.synthetic.control.arm.generation","name":"synthetic-control-arm-generation","description":"Generates statistically valid synthetic patient cohorts to serve as control arms in clinical trials without requiring actual patient recruitment. Uses generative AI to create realistic patient data that matches the characteristics and outcomes of real patient populations.","intents":["I need to reduce the number of real patients required for my control arm","I want to accelerate my clinical trial timeline by avoiding lengthy recruitment phases","I need to lower the cost of running a late-stage clinical trial"],"best_for":["Pharmaceutical companies running Phase 2-3 clinical trials","Biotech firms with limited recruitment budgets","Sponsors seeking faster time-to-market for new drugs"],"limitations":["Regulatory acceptance varies by jurisdiction and indication","Synthetic data quality depends on training dataset representativeness","May not be suitable for rare disease trials with limited historical data","Potential for bias inheritance from source datasets"],"requires":["Historical patient data from similar trials","FDA or regulatory body guidance alignment","Clinical expertise to validate synthetic cohort characteristics","Access to Unlearn.AI platform"],"input_types":["structured patient data (demographics, clinical outcomes, lab values)","trial protocol specifications","inclusion/exclusion criteria"],"output_types":["synthetic patient cohort datasets","statistical validation reports","control arm comparison metrics"],"categories":["research","clinical-trials","data-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_unlearn-ai__cap_1","uri":"capability://research.trial.population.diversity.expansion","name":"trial-population-diversity-expansion","description":"Synthetically generates diverse patient populations across different demographics and geographies to address recruitment constraints in clinical trials. Enables trials to include underrepresented populations without geographic or demographic limitations of traditional recruitment.","intents":["I need to include more diverse patient populations in my trial but face geographic recruitment barriers","I want to reduce disparities in clinical trial representation across demographics","I need to meet regulatory requirements for diverse trial populations without extensive recruitment"],"best_for":["Sponsors aiming to meet FDA diversity guidance requirements","Trials targeting rare diseases with limited geographic patient pools","Companies seeking to improve health equity in drug development"],"limitations":["Synthetic diversity may not capture real-world social determinants of health","Risk of perpetuating historical biases if training data is non-representative","Regulatory bodies may scrutinize diversity claims based on synthetic data"],"requires":["Demographic and outcome data across multiple populations","Clear definition of diversity targets","Validation against real-world population statistics"],"input_types":["demographic characteristics","geographic distribution targets","clinical outcome data by population"],"output_types":["diverse synthetic patient cohorts","demographic distribution reports","diversity metrics and compliance documentation"],"categories":["research","clinical-trials","health-equity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_unlearn-ai__cap_2","uri":"capability://research.trial.cost.reduction.modeling","name":"trial-cost-reduction-modeling","description":"Analyzes and projects cost savings from replacing portions of traditional trial recruitment with synthetic control arms. Provides financial impact modeling showing potential 30-40% cost reductions through reduced participant burden and recruitment expenses.","intents":["I need to understand the financial ROI of using synthetic control arms in my trial","I want to justify the budget for synthetic data technology to my finance team","I need to compare the cost-benefit of synthetic vs. traditional recruitment strategies"],"best_for":["Trial sponsors evaluating budget allocation decisions","Biotech companies with constrained R&D budgets","Finance teams assessing new trial methodologies"],"limitations":["Projections depend on accurate cost baseline data","Actual savings vary by therapeutic area and trial complexity","Does not account for potential regulatory delays or rejections"],"requires":["Historical trial cost data","Recruitment timeline and expense information","Participant retention and dropout rates"],"input_types":["trial budget parameters","recruitment cost estimates","participant retention data"],"output_types":["cost-benefit analysis reports","financial projection models","ROI calculations"],"categories":["research","clinical-trials","financial-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_unlearn-ai__cap_3","uri":"capability://research.digital.twin.patient.simulation","name":"digital-twin-patient-simulation","description":"Creates individual digital twin representations of patients that simulate clinical outcomes and disease progression based on baseline characteristics and treatment exposure. Enables outcome prediction and trial arm comparison without requiring actual patient follow-up.","intents":["I need to predict how different patient subgroups will respond to treatment","I want to simulate trial outcomes before enrolling real patients","I need to understand counterfactual outcomes for control arm comparisons"],"best_for":["Sponsors designing adaptive trial protocols","Companies conducting early-stage trial planning","Researchers studying treatment effect heterogeneity"],"limitations":["Simulation accuracy depends on quality of training data","Cannot capture unexpected adverse events not in training data","May oversimplify complex disease biology","Requires validation against real trial outcomes"],"requires":["Patient baseline characteristics","Historical outcome data","Treatment protocol specifications","Disease progression models"],"input_types":["patient demographics and clinical characteristics","treatment protocols","historical outcome datasets"],"output_types":["simulated patient outcome trajectories","predicted efficacy and safety profiles","treatment response probability estimates"],"categories":["research","clinical-trials","simulation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_unlearn-ai__cap_4","uri":"capability://research.regulatory.compliant.synthetic.data.validation","name":"regulatory-compliant-synthetic-data-validation","description":"Validates synthetic patient data against regulatory standards and FDA guidance for use in clinical trials. Provides documentation and statistical evidence that synthetic control arms meet regulatory requirements for trial submissions.","intents":["I need to ensure my synthetic data meets FDA standards for trial submissions","I want documentation proving my synthetic control arm is statistically valid","I need to demonstrate to regulators that synthetic data doesn't introduce bias"],"best_for":["Sponsors preparing IND or BLA submissions","Companies navigating evolving synthetic data regulations","Regulatory affairs teams evaluating synthetic data acceptability"],"limitations":["Regulatory landscape is still evolving; acceptance not guaranteed","Different jurisdictions have different requirements","Validation may not prevent regulatory questions or requests","Transparency limitations may concern conservative regulators"],"requires":["FDA guidance documents and regulatory frameworks","Statistical validation methodologies","Comparison datasets from real trials","Regulatory expertise"],"input_types":["synthetic patient datasets","real trial data for comparison","regulatory submission requirements"],"output_types":["validation reports","statistical evidence documents","regulatory compliance certifications","submission-ready documentation"],"categories":["research","clinical-trials","regulatory"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_unlearn-ai__cap_5","uri":"capability://research.trial.timeline.acceleration.planning","name":"trial-timeline-acceleration-planning","description":"Develops optimized trial timelines by identifying where synthetic control arms can replace or reduce traditional recruitment phases. Projects realistic timeline reductions based on elimination of recruitment bottlenecks.","intents":["I need to reduce the time from trial initiation to completion","I want to identify which trial phases can use synthetic data to save time","I need to create a realistic accelerated timeline for stakeholder communication"],"best_for":["Trial sponsors under time-to-market pressure","Companies developing treatments for serious conditions","Project managers planning trial logistics"],"limitations":["Timeline acceleration depends on regulatory acceptance","Unexpected recruitment challenges may not be fully eliminated","Regulatory review timelines are not shortened by synthetic data","Actual savings vary significantly by therapeutic area"],"requires":["Detailed trial protocol and timeline","Historical recruitment data","Regulatory submission timelines","Resource availability information"],"input_types":["trial protocol specifications","recruitment strategy details","current timeline estimates"],"output_types":["accelerated timeline projections","phase-by-phase timeline comparisons","critical path analysis","risk mitigation plans"],"categories":["research","clinical-trials","project-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_unlearn-ai__cap_6","uri":"capability://research.participant.burden.reduction.assessment","name":"participant-burden-reduction-assessment","description":"Quantifies and analyzes how synthetic control arms reduce the number of real patients needed and associated participant burden including visit frequency, procedures, and dropout risk. Provides metrics on improved patient experience and retention.","intents":["I want to demonstrate to patients how synthetic data reduces their trial burden","I need to calculate how many fewer patients are needed with synthetic control arms","I want to improve trial retention by reducing participant burden"],"best_for":["Patient recruitment and retention teams","Sponsors focused on patient-centric trial design","Companies addressing trial dropout and burden concerns"],"limitations":["Burden reduction depends on specific trial design","Some trials may still require full participant cohorts regardless","Psychological factors in trial participation not fully captured","Regulatory requirements may mandate minimum real participant numbers"],"requires":["Current trial protocol and visit schedule","Historical dropout and retention data","Patient burden assessment data","Synthetic control arm specifications"],"input_types":["trial protocol details","participant burden metrics","retention and dropout data"],"output_types":["burden reduction quantification reports","participant number reduction projections","retention improvement estimates","patient communication materials"],"categories":["research","clinical-trials","patient-experience"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_unlearn-ai__cap_7","uri":"capability://research.comparative.effectiveness.analysis.with.synthetic.data","name":"comparative-effectiveness-analysis-with-synthetic-data","description":"Performs statistical analysis comparing treatment efficacy and safety between real treatment arms and synthetic control arms. Generates comparative effectiveness metrics and confidence intervals for regulatory submission.","intents":["I need to statistically compare my treatment against a synthetic control arm","I want to generate efficacy and safety metrics for regulatory submission","I need confidence intervals and statistical significance testing for synthetic comparisons"],"best_for":["Biostatisticians analyzing trial results","Sponsors preparing regulatory submissions","Clinical teams interpreting synthetic control arm comparisons"],"limitations":["Statistical validity depends on synthetic data quality","May not capture all real-world confounders","Regulatory bodies may apply different standards to synthetic comparisons","Requires rigorous statistical methodology documentation"],"requires":["Real treatment arm data","Synthetic control arm data","Statistical analysis plan","Biostatistical expertise"],"input_types":["real patient outcome data","synthetic patient outcome data","statistical analysis specifications"],"output_types":["comparative efficacy reports","safety profile comparisons","statistical significance testing results","confidence interval calculations","regulatory-ready analysis tables"],"categories":["research","clinical-trials","biostatistics"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"low","permissions":["Historical patient data from similar trials","FDA or regulatory body guidance alignment","Clinical expertise to validate synthetic cohort characteristics","Access to Unlearn.AI platform","Demographic and outcome data across multiple populations","Clear definition of diversity targets","Validation against real-world population statistics","Historical trial cost data","Recruitment timeline and expense information","Participant retention and dropout rates"],"failure_modes":["Regulatory acceptance varies by jurisdiction and indication","Synthetic data quality depends on training dataset representativeness","May not be suitable for rare disease trials with limited historical data","Potential for bias inheritance from source datasets","Synthetic diversity may not capture real-world social determinants of health","Risk of perpetuating historical biases if training data is non-representative","Regulatory bodies may scrutinize diversity claims based on synthetic data","Projections depend on accurate cost baseline data","Actual savings vary by therapeutic area and trial complexity","Does not account for potential regulatory delays or rejections","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.45,"quality":0.8300000000000001,"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":"2026-05-24T12:16:33.649Z","last_scraped_at":"2026-04-05T13:23:42.533Z","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=unlearn-ai","compare_url":"https://unfragile.ai/compare?artifact=unlearn-ai"}},"signature":"/vydy2qib5GjFXYFxZ1faxbdATUbWljKX28MTiRTU57fC+3NYJZCKD0vAKo4MPXrbeQQLSDBSInMK0PNSgTOAg==","signedAt":"2026-06-21T02:31:14.031Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/unlearn-ai","artifact":"https://unfragile.ai/unlearn-ai","verify":"https://unfragile.ai/api/v1/verify?slug=unlearn-ai","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"}}