Richard Frank

Richard Frank, MD PhD

Entrepreneur-in-Residence, DigitalDx Ventures Women in Leadership
Member, CPT Editorial Panel summary-panel-actions.pdf
Chair, AI Working Group & Co-chair, Telehealth WG, AMA digital-medicine-payment-advisory-group
Chair, Bias Policy Working Group, Medical Imaging Technology Alliance [MITA]
Member, Advisory Board, ABAIM [American Board of AI in Medicine]
Chair, Medical Imaging Informatics Section MITA
Chair, Coverage and Research Committee, MITA
Member, AdvaMed CMO Committee, Coverage Reform Committee, Digital Health Joint Committee
National Lung Cancer Round Table (Member, Advanced Imaging) Screening Coverage Decision
Member, FDA Medical Imaging Drugs Advisory Committee, fda-drug-safety-communication-GBCAs

I bring 3 decades’ global success in a) regulatory labeling of, b) public and private coverage and payment for, and c) clinical adoption of, therapeutic and diagnostic drugs and devices including AI. My interests include addressing product-, market-, or delivery system-specific inefficiencies and issues which burden payers and delay or skew patients’ access to the benefits of innovation. I am devoted to the development of and advocacy for clearance to market and payment for healthcare AI, including regulations, standards, coding, and coverage policy [ AI Taxonomy ] by working with medical peers among the stakeholders in each opportunity.

Design and conduct of clinical trials programs from phase I to post-market registries
Leadership of product teams to develop and implement clearance, coverage, & adoption strategy
Persuasive engagement of peers in regulators, payers, professional societies, patient advocacy groups
Expert testimony to Ministries & agencies re; innovation, eg radiation dose, image quality, clinical pathways
Formation and leadership of industry groups to develop and advocate industry policy positions
Development of coalitions and public-private partnerships to grow capacity in healthcare delivery networks
Creation and implementation of a taxonomic framework for reimbursement coding and coverage for AI
Harmonization of descriptors across labeling, coding, and coverage globally for AI, eg via IMDRF

The credibility and network accruing to 3 decades’ pursuit of objectives shared among industry, academia, NGOs, and government agencies has facilitated my opinion leadership in clinical evidentiary standards and related matters of government policy, particularly regarding AI, and result in appointment to external committees such as the NAM workshop on barriers to AI [facilitating-clinical-adoption-of-AI], GAO Report on AI [gao-barriers to AI], and the Pew Charitable Trusts workshop on bias in AI [bias-in-AI-enabled-health-tools].