Artifical InteligenceNCT06694181
Safe and Explainable AI
An observational study of Artifical Inteligence and Cardiology, run by Abramson Cancer Center at Penn Medicine at 1 site in United States.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Artifical Inteligence, Cardiology, Breast Cancer
- What is being tested?
- AI-PERSONALIZED CLINICAL DECISION SUPPORT
- How many people
- 300,000 participants
- Where
- 1 siteUnited States
- Study dates
- 29 Nov 2025to Nov 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study 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.
Who may be able to join
- 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.
Who cannot join
None listed.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Nicholas Bishop contact
United States1 site, 1 recruiting
- Recruiting
Hospital of the University of Pennsylvania
Philadelphia, Pennsylvania, 19104
Haideliza Soto Calderon, 215-237-4509, haideliza.soto-calderon@pennmedicine.upenn.edu
About this study
Official title: SAFE AND EXPLAINABLE AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT
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
- Artifical Inteligence, Cardiology, Breast Cancer, Sepsis
- Being tested
- AI-PERSONALIZED CLINICAL DECISION SUPPORT
- Medical subject headings
- Breast Neoplasms, Sepsis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-PERSONALIZED CLINICAL DECISION SUPPORTOther
AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT
| Study group | Role | What they receive |
|---|---|---|
| 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). Receives: Other: AI-PERSONALIZED CLINICAL DECISION SUPPORT | |
| 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. Receives: Other: AI-PERSONALIZED CLINICAL DECISION SUPPORT | |
| 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. Receives: Other: AI-PERSONALIZED CLINICAL DECISION SUPPORT |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Prototype and develop new learning algorithms; 18 months. Benchmark and evaluate the learning algorithms; 24 months. Publish research results; 24 months
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.
Measured: 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
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.
Measured: 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
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Nov 2024
- Study start29 Nov 2025
- Primary completionNov 2028 (estimated)
- Study completionNov 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artifical InteligenceStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 300,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Abramson Cancer Center at Penn Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06694181
- Sponsor's protocol ID
- 34124
- Status last verified
- Feb 2026
- First submitted
- 15 Nov 2024
- Last updated
- 25 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 25 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- SAFE AND EXPLAINABLE AI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT
- ClinicalTrials.gov ID
- NCT06694181
- Acronym / sponsor ID
- 34124
- Conditions
- Artifical Inteligence, Cardiology, Breast Cancer, Sepsis
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 300,000 participants (estimated)
- Lead sponsor
- Abramson Cancer Center at Penn MedicineUniversity, hospital or other
- Study start
- 29 Nov 2025 (actual)
- Primary completion
- Nov 2028 (estimated)
- Study completion
- Nov 2028 (estimated)
- First posted
- 19 Nov 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Cardiology | – | Other: AI-PERSONALIZED CLINICAL DECISION SUPPORTThe 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). |
| Oncology - Breast Cancer | – | Other: AI-PERSONALIZED CLINICAL DECISION SUPPORTThe 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. |
| Sepsis | – | Other: AI-PERSONALIZED CLINICAL DECISION SUPPORTThe 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (0 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-PERSONALIZED CLINICAL DECISION SUPPORTAI-ENABLED DECISION MAKING FOR PERSONALIZED CLINICAL DECISION SUPPORT | Other | Cardiology, Oncology - Breast Cancer, Sepsis |
Primary outcome
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.
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.
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Feb 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Nicholas Bishop contact
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Artifical Inteligence and Cardiology, run by Abramson Cancer Center at Penn Medicine at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 0 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 25 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Haideliza Soto Calderon (215-220-9425, haideliza.soto-calderon@pennmedicine.upenn.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the 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). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Feb 2026.