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Safe and Explainable AI
NCT06694181 · Abramson Cancer Center at Penn Medicine
In plain English
Click the button to translate this study into plain language — what it is, who qualifies, and what participation looks like.
Official title
SAFE AND EXPLAINABLE AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT
About this study
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.
Eligibility criteria
Inclusion Criteria:
Cardiology 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
Exclusion Criteria All prediction models will exclude patients under the age of 18 from their patient data sets.
Cardiology 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.
Study design
Enrollment target: 300000 participants
Age groups: adult, older_adult
Timeline
Starts: 2025-11-29
Estimated completion: 2028-11
Last updated: 2026-02-25
Interventions
Other: AI-PERSONALIZED CLINICAL DECISION SUPPORT
Primary outcomes
- • Neurosymbolic Learning Algorithms (Prototype and develop new learning algorithms; 18 months. Benchmark and evaluate the learning algorithms; 24 months. Publish research results; 24 months)
- • Explanation Methods (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)
- • Methods for Safety Guarantees (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)
Sponsor
Abramson Cancer Center at Penn Medicine · other
Contacts & investigators
ContactHaideliza Soto Calderon · contact · haideliza.soto-calderon@pennmedicine.upenn.edu · 215-220-9425
ContactNicholas Bishop · contact · nicholas.bishop@pennmedicine.upenn.edu
All locations (1)
Hospital of the University of PennsylvaniaRecruiting
Philadelphia, Pennsylvania, United States