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NCT Number: NCT06694181

Safe and Explainable AI

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.

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Key information

Conditions

Artifical Inteligence Breast Cancer Breast Diseases Breast Neoplasms Cardiology Infections Inflammation Neoplasms Neoplasms by Site Pathologic Processes Pathological Conditions, Signs and Symptoms Sepsis Skin Diseases Skin and Connective Tissue Diseases Systemic Inflammatory Response Syndrome

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Hospital of the University of Pennsylvania

Philadelphia, Pennsylvania, 19104, United States

Location status: Recruiting

Location contact

Haideliza Soto Calderon

CONTACT

[email protected]

215-237-4509

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

AI-PERSONALIZED CLINICAL DECISION SUPPORT

Other

AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT

Primary outcomes

  1. 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

    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.

  2. Explanation Methods

    Time frame: 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

    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.

  3. Methods for Safety Guarantees

    Time frame: 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

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Haideliza Soto Calderon

CONTACT

[email protected]

215-220-9425

Nicholas Bishop

CONTACT

[email protected]

Sponsors and collaborators

Lead sponsor

Abramson Cancer Center at Penn Medicine

Other

Registry information

Official study title

SAFE AND EXPLAINABLE AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT

Important dates

Study start
2025
Primary completion
2028
Study completion
2028
First posted
Nov 19, 2024
Registry last updated
Feb 25, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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