Responsible Artificial Intelligence governance in oncology

Abstract

The use of Artificial Intelligence (AI) in healthcare is expanding rapidly, including in oncology. Although generic AI development and implementation frameworks exist in healthcare, no effective governance models have been reported in oncology. Our study reports on a Comprehensive Cancer Center's Responsible AI governance model for clinical, operations, and research programs. We report our one-year AI Governance Committee results with respect to the registration and monitoring of 26 AI models (including large language models), 2 ambient AI pilots, and a review of 33 nomograms. Novel management tools for AI governance are shared, including an overall program model, model information sheet, risk assessment tool, and lifecycle management tool. Two AI model case studies illustrate lessons learned and our "Express Pass" methodology for select models. Open research questions are explored. To the best of our knowledge, this is one of the first published reports on Responsible AI governance at scale in oncology.

Fig. 1. MSK Enterprise AI Program Structure.

Overall AI program structure developed by Memorial Sloan Kettering’s AI Task Force with three programmatic AI domains (Research, Clinical, Operations), supported by governance, partnerships, and underlying infrastructure, skills and processes. MSK = Memorial Sloan Kettering; AI/ML artificial intelligence/machine learning, LLM large language model, HR human resources, EHR electronic health record, ML Ops machine learning operations. Source: Memorial Sloan Kettering.

Fig. 2. iLEAP – MSK’s AI Lifecycle Management Operating Model.

a End-to-end AI model lifecycle management model employed by the AI Governance Committee with three paths to entry into enterprise-wide deployment (research-purple path; in-house development-blue path; 3^rd-party co-development or purchase-orange path). b Decision gate definitions, the outputs of each decision gate, and express path criteria used by the AI Governance Committee for review of each registered model. iLEAP Legal, Ethics, Adoption, Performance, MSK Memorial Sloan Kettering, IX Informatics, Dev Development, MIS Model Information Sheet, App Application, AIGC AI Governance Committee. Source: Memorial Sloan Kettering.

Fig. 3. AI Governance Committee – embedded within existing digital governance.

Positioning of the AI Governance Committee as embedded within overall digital governance structures, along with goals, primary activities, and key collaborations with other related committees needed for successful governance of AI models throughout their lifecycle. IRB Institutional Review Board, DAC Digital Advisory Committee, RAI Responsible Artificial Intelligence, MSK Memorial Sloan Kettering, IT Information Technology. Source: Memorial Sloan Kettering.

Fig. 4. AI models in pipeline by stage gate.

Relative distribution of current AI models through the lifecycle management stage gates, which is dynamic from month to month as models mature through the process. AI artificial intelligence. Source: Memorial Sloan Kettering.

Fig. 5. Example AI Model Progression Through iLEAP – Radiology Case Study.

Real-world example of an acquired FDA-approved radiology AI model from a 3rd-party vendor, and the steps on the path it followed through the lifecycle management stage gates. iLEAP Legal, Ethics, Adoption, Performance; FDA Food and Drug Administration, AIGC AI Governance Committee. Source: Memorial Sloan Kettering.

Fig. 6. AI/ML platform architecture.

High-level description of the technology stack employed by the central AI/ML team to support AI practitioners across MSK. AI/ML artificial intelligence/machine learning, ML machine learning, DL deep learning, LLM large language model, VLM vision language model, EHR electronic health record. Source: Memorial Sloan Kettering Cancer Center.