From Bit to Bedside: A Practical Framework for Artificial Intelligence Product Development in Healthcare. (2nd July 2020)

• Record Type: Journal Article
• Authors: Higgins, David; Madai, Vince I.
• Is Part Of: Advanced intelligent systems. Volume 2:Number 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-02
• Subjects: artificial intelligence -- digital health -- healthcare -- innovation frameworks -- medicine; Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.202000052
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : Artificial intelligence (AI) in healthcare holds great potential to expand access to high‐quality medical care, while reducing systemic costs. Despite hitting headlines regularly and many publications of proofs‐of‐concept, certified products are failing to break through to the clinic. AI in healthcare is a multiparty process with deep knowledge required in multiple individual domains. A lack of understanding of the specific challenges in the domain is the major contributor to the failure to deliver on the big promises. Herein, a "decision perspective" framework for the development of AI‐driven biomedical products from conception to market launch is presented. The framework highlights the risks, objectives, and key results which are typically required to navigate a three‐phase process to market‐launch of a validated medical AI product. Clinical validation, regulatory affairs, data strategy, and algorithmic development are addressed. The development process proposed for AI in healthcare software strongly diverges from modern consumer software development processes. Key time points to guide founders, investors, and key stakeholders throughout the process are highlighted. This framework should be seen as a template for innovation frameworks, which can be used to coordinate team communications and responsibilities toward a viable product development roadmap, thus unlocking the potential of AI in medicine. Abstract : How do you choose where to spend your resources when you are managing a multiparty deep‐tech development process? For Artificial Intelligence (AI) in Healthcare this has been the key blocker on successful development and deployment of products. The framework presented here enables founders, funders and developers alike to more efficiently navigate the path from project inception to market‐place deployment of a medical AI product.