[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"trial:NCT06694181":3,"trial-entities:NCT06694181":83,"trial-summary:NCT06694181":89},{"id":4,"nct_id":4,"org_study_id":5,"brief_title":6,"official_title":7,"overall_status":8,"completion_date":9,"status_verified_date":10,"last_update_date":11,"start_date":12,"sponsor_name":13,"lead_sponsor_class":14,"has_dmc":15,"brief_summary":16,"detailed_description":17,"conditions":18,"keywords":23,"study_type":24,"primary_purpose":17,"phases":25,"enrollment_info":26,"interventions":29,"primary_outcomes":35,"secondary_outcomes":48,"sex":49,"minimum_age":50,"maximum_age":17,"healthy_volunteers":15,"eligibility_criteria":51,"std_ages":55,"locations":58,"central_contacts":74,"overall_officials":80,"references":81,"see_also_links":82},"NCT06694181","34124","Safe and Explainable AI","SAFE AND EXPLAINABLE AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT","RECRUITING","2028-11","2026-02","2026-02-25","2025-11-29","Abramson Cancer Center at Penn Medicine","OTHER",false,"While current AI technology is suitable for automating some repetitive clinical tasks, technical challenges remain in solving critical and gainful problems in the domains of patient and disease management. The proposed research seeks to address issues in medical AI, such as integrating medical knowledge effectively, making AI recommendations explainable to clinicians, and establishing safety guarantees.",null,[19,20,21,22],"Artifical Inteligence","Cardiology","Breast Cancer","Sepsis",[],"OBSERVATIONAL",[],{"count":27,"type":28},300000,"ESTIMATED",[30],{"type":14,"name":31,"description":32,"armGroupLabels":33},"AI-PERSONALIZED CLINICAL DECISION SUPPORT","AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT",[20,34,22],"Oncology - Breast Cancer",[36,40,44],{"measure":37,"description":38,"timeFrame":39},"Neurosymbolic Learning Algorithms","Develop and evaluate novel algorithms for training neurosymbolic models. We will develop data- and compute-efficient algorithms for end-to-end training of neurosymbolic models. This task will reduce the burden on clinician experts to provide fine-grained labels on voluminous EHR data.","Prototype and develop new learning algorithms; 18 months. Benchmark and evaluate the learning algorithms; 24 months. Publish research results; 24 months",{"measure":41,"description":42,"timeFrame":43},"Explanation Methods","We will develop new explainable AI techniques that come with verifiable guarantees. These guarantees will enable trust and transparency in AI at a fundamental level.","Prototype and develop new explanation algorithms; 18 months. Derive certified guarantees for explanations; 18 months. Benchmark and evaluate the explanation algorithms; 24 months. Extend certificates to new properties and tasks; 30 months. Publ",{"measure":45,"description":46,"timeFrame":47},"Methods for Safety Guarantees","We will develop new algorithms that can scalably extract complex logical rules governing safety within the data that have statistical guarantees. These techniques will be rooted in statistical analysis and assist users in identifying out of distribution data and detecting anomalies.","Prototype and develop new rule learning algorithms; 30 months. Scale rule learning algorithms to larger data settings; 36 months. Incorporate new primitives to express complex rules; 36 months. Implement rule learning algorithms on baseline tasks",[],"ALL","18 Years",{"inclusion":52,"exclusion":53,"raw_text":54},[],[],"Inclusion Criteria:\n\nCardiology 18 years of age and older, admitted to any of the Penn Medicine hospitals from 2017 to the present. Sepsis 18 years of age at the time of presentation to an emergency department or admission to any Penn Medicine hospital from July 1, 2017, onward will be eligible as this represents the population at risk for acquiring sepsis Oncology 18 years of age and older with a diagnosis of invasive breast cancer (Stage 1-4) in the Penn Cancer registry\n\nExclusion Criteria All prediction models will exclude patients under the age of 18 from their patient data sets.\n\nCardiology Patients whose primary admission diagnosis was cardiac arrest Sepsis Those with pre-existing limitations on life-sustaining therapy will be excluded because their eligibility for sepsis definitions, care received, and outcomes, may be significantly and variably affected by pre-existing limitations on care. Oncology There are no other exclusions.",[56,57],"ADULT","OLDER_ADULT",[59],{"facility":60,"status":8,"city":61,"state":62,"zip":63,"country":64,"contacts":65,"geoPoint":71},"Hospital of the University of Pennsylvania","Philadelphia","Pennsylvania","19104","United States",[66],{"name":67,"role":68,"phone":69,"email":70},"Haideliza Soto Calderon","CONTACT","215-237-4509","haideliza.soto-calderon@pennmedicine.upenn.edu",{"lat":72,"lon":73},39.95238,-75.16362,[75,77],{"name":67,"role":68,"phone":76,"email":70},"215-220-9425",{"name":78,"role":68,"email":79},"Nicholas Bishop","nicholas.bishop@pennmedicine.upenn.edu",[],[],[],{"nct_id":4,"conditions":84,"biomarkers":88},[85,86,87],"Breast Carcinoma","Cardiovascular Disorder","Septicemia",[],{"nct_id":4,"found":90,"summary":91,"prompt_version":101},true,{"design":92,"status":93,"heading":94,"summary":95,"follow_up":96,"word_count":97,"commitments":98,"compensation":99,"drugs_mentioned":100},"This is an observational study that plans to include 300,000 participants. It is not specified if it is randomized or blinded.","completed","Safe and Explainable AI for Cardiology, Breast Cancer, and Sepsis","This study is looking at how to make Artificial Intelligence (AI) safer and easier for doctors to understand when used in patient care. It's an observational study, meaning researchers will look at existing information rather than giving new treatments. The study focuses on AI-PERSONALIZED CLINICAL DECISION SUPPORT, which uses AI to help doctors make personalized decisions for patients. Researchers will be developing new AI learning algorithms and ways to explain AI recommendations, as well as methods to ensure AI safety. They will measure success by how well these new algorithms and explanation methods are developed and evaluated over 18 to 36 months. You could be eligible if you are 18 years or older and have been treated at Penn Medicine hospitals for cardiology, sepsis, or oncology conditions since 2017.","The study involves developing and evaluating AI methods over periods ranging from 18 to 36 months.",129,"Not specified in the trial record.","Not stated in the trial record.",[31],"v2"]