AI Implementation, Evaluation, and Safety Monitoring
Establishing frameworks for the safe deployment and longitudinal evaluation of AI in hospital and surgical environments, with performance validation and measurable clinical impact.
The AI Learning Lab
A trusted partner for health care providers, health systems, and industry experts
The AI Learning Lab is an applied collaboration hub where interdisciplinary teams bridge cutting-edge research and real-world practice to integrate, validate, and build trustworthy AI healthcare solutions that deliver safer, more equitable patient-centered care.
Working together with RWJBarnabas Health, we engage with health care providers, administrators, analysts, operations teams, decisionmakers, and other partners in New Jersey, nationally, and globally to develop innovative AI-based approaches to enhancing health care.
How We Work
The AI Learning Lab partners with providers and health systems, interdisciplinary experts, and technology vendors to design and evaluate AI solutions that improve health care. Our collaborative work focuses on:
- Co-designing, piloting, and evaluating AI in clinical environments to improve outcomes, safety, and clinician experience
- Bringing together clinical and technical expertise to build and validate AI using the Coalition for Health AI framework
- Evaluating, piloting, and refining AI tools to ensure strong fit, performance, and usability
Core Capacities
We support healthcare in moving from promising concepts to proven impact. The AI Learning Lab supports the full lifecycle of AI and healthcare innovation — translating research into practice while ensuring transparency, performance, and accountability. Our core capacities include:
Explainable AI (XAI) and Clinical Trust
Advancing interpretable models that translate complex algorithmic outputs into transparent, actionable insights to build clinician confidence and support responsible decision-making.
AI-Enhanced Diagnostics and Clinical Oversight
Developing high-precision tools for complex imaging and neuroradiology that pair enhanced diagnostic accuracy with clear, human-understandable explanations for the care team.
Bias Mitigation, Fairness, and Equitable Care
Applying rigorous methodologies to detect and eliminate algorithmic bias, ensuring AI solutions promote health equity and expand access for diverse and underserved populations.
Precision Analytics and Multimodal Integration
Synthesizing diverse data streams to generate accurate, clinically relevant predictions that drive personalized patient care and optimize system-level strategic decisions.
Human–AI Collaboration and Workflow Optimization
Redesigning clinical workflows to harmonize the strengths of human expertise and machine intelligence, ensuring AI integration enhances provider experience and patient outcomes.
Case Studies
Examples from our early portfolio of work demonstrate how we collaborate with partners to translate innovation into meaningful clinical impact:
Epic Deterioration Index
This AI-powered early warning system helps care teams detect subtle signs that hospitalized patients’ conditions may be deteriorating—often hours before they become clinically obvious. Rigorous testing in our partner hospitals showed an 18.6% reduction in in-hospital mortality.
Implementation and Evaluation of AI Devices in Surgery
In partnership with Intuitive, we support the implementation and real-world evaluation of AI-assisted surgical technologies. This work focuses on improving surgical precision, workflow efficiency, and patient safety.
Explainable Neuroradiology Support Tool
This AI-powered neuroradiology tool helps clinicians analyze complex brain imaging while providing clear explanations for its findings. By combining high diagnostic accuracy with strong explainability and interpretability features, the tool enhances clinical confidence, supports better decision-making, and includes built-in safety monitoring.
Partner With Us
The AI Learning Lab partners with organizations seeking responsible, credible pathways to AI innovation. Whether you are evaluating new tools, scaling validated solutions, or exploring how is AI used in healthcare in practice, we offer a collaborative, evidence‑driven approach grounded in real world impact.