Safe and Explainable AI

Part of paid clinical trials in Philadelphia, Pennsylvania.

Sponsor
Abramson Cancer Center at Penn Medicine
Study ID
NCT06694181
Status
Recruiting
Apply to this trial

Conditions

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Not accepted

Interventions

  • AI-PERSONALIZED CLINICAL DECISION SUPPORT — OTHER
    AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT

Study Details

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.

Key Dates

Start date
Nov 29, 2025
Status verified
Feb 2026
Primary completion
Nov 30, 2028
Completion
Nov 30, 2028

Study Design

Enrollment
300,000 participants (estimated)

Arms

  • Arm: Cardiology
    The primary objective in this clinical case scenario is to evaluate an ML model utilizing real-time cardiac telemetry, as well as other clinical, demographic, and imaging structured data sources, among hospitalized, intensive care unit (ICU) patients to predict impending inhospital cardiac arrest, identify potentially reversible causes of cardiac arrest, and predict which patients may have impending cardiac arrest due to shockable rhythms i.e. ventricular tachycardia (VT) or ventricular fibrillation (VF).
  • Arm: Oncology - Breast Cancer
    The primary objective in this clinical case scenario is to evaluate an ML model utilizing structured and unstructured data from clinical, demographic, and tumor molecular and germline sequencing, among outpatients with cancer, to predict short-term mortality and/or symptom decline. The model for prediction to treatment response in breast cancer patients will be compared with two prognostic tools: 1) Conversation Connect, a previously validated machine learning mortality prediction tool that has been used at the University of Pennsylvania for routine clinical decision support, and 2) the Elixhauser Comorbidity Index, a comorbidity-based prognostic index used commonly in research and risk-adjustment.
  • Arm: Sepsis
    The primary objective in this clinical case scenario is to develop and evaluate an ML model that utilizes multidmodal clinical data (e.g., structured EHR data such as demographics, laboratory test results, and vital signs; unstructured EHR data including the text of clinical encounter notes and, where available, waveforms from real-time cardiac, hemodynamic, and respiratory monitoring devices) to predict the need for initiation of broad-spectrum antimicrobial therapy for hospitalized patients with sepsis. With a focus on implementable and explainable AI, we will produce well calibrated predictions that are also clinically meaningful at the bedside to aid real-time decision-making about diagnosis and treatment initiation. The model for timely diagnosis and intervention in sepsis will be compared with widely used commercial and open-source sepsis prediction models.

Primary Outcome Measure

Neurosymbolic Learning Algorithms [ Time Frame: Prototype and develop new learning algorithms; 18 months. Benchmark and evaluate the learning algorithms; 24 months. Publish research results; 24 months ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
Hospital of the University of PennsylvaniaPhiladelphiaPennsylvania19104
Haideliza Soto Calderon
[email protected]
215-237-4509

Find similar trials in Philadelphia, PA

By condition
Breast cancer
By specialty
OncologyBreast oncologyWomen's health
By research site
Hospital of the University of Pennsylvania· Philadelphia, PA

Related Studies