Clinical trial · Observational
Safe and Explainable AI
SAFE AND EXPLAINABLE AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT
NCT06694181CI-TRIAL-00104086recruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Artifical Inteligence | — | UNRESOLVED | — |
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
| Cardiology | — | UNRESOLVED | — |
| Sepsis | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI-PERSONALIZED CLINICAL DECISION SUPPORT | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (3)
- label
- Cardiology
- description
- 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).
- interventionNames
- Other: AI-PERSONALIZED CLINICAL DECISION SUPPORT
- label
- Oncology - Breast Cancer
- description
- 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.
- interventionNames
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available
No reference posted for this study.