Safe and Explainable AI
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorAbramson Cancer Center at Penn Medicine
About this trial
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
This trial does not accept healthy volunteersQualifiers
None
Disqualifiers
None
Trial population
The study will look at EMR data from cardiac, oncology, and patients at risk for acquiring sepsis. Each group has clearly defined inclusion-exclusion criteria. For the machine learning model to predict ventricular arrhythmias and in-hospital cardiac arrest, we will construct patient cohorts consisting of adult (i.e., 18 years of age or older) patients admitted to Sickbay-accessible bed within a Penn Medicine hospital from 2020 to the present as well as patients identified as having experienced cardiac arrest using EHR records.
Trial design
Design model
Case-only
Time perspective
Retrospective
Treatments tested in this trial
AI-PERSONALIZED CLINICAL DECISION SUPPORT
Other interventionAI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT
Treatment groups
300,000 Participants
are divided into 3 treatment groupsGroup A: Cardiology1 intervention
Group B: Oncology - Breast Cancer1 intervention
Group C: Sepsis1 intervention
Trial outcomes
Primary outcomes
1
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.
Time frame
Prototype and develop new learning algorithms; 18 months. Benchmark and evaluate the learning algorithms; 24 months. Publish research results; 24 months2
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.
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. Publ3
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.
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 tasksSponsors and contacts
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Source ClinicalTrials.gov