From Bit to Bedside: A Practical Framework for Artificial Intelligence Product Development in Healthcare
From Bit to Bedside: A Practical Framework for Artificial Intelligence Product Development in Healthcare
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DOI：
10.1002/aisy.202000052
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发表时间：
2020-10-01
影响因子：
7.4
通讯作者：
Madai, Vince I.
中科院分区：
文献类型：
--
作者：
Higgins, David;Madai, Vince I.
医疗保健领域的人工智能（AI）在扩大获得高质量医疗保健的机会，同时降低系统成本方面具有巨大潜力。尽管经常成为头条新闻，许多出版物的概念证明，认证产品未能突破到临床。医疗保健中的人工智能是一个多方过程，需要多个领域的深入知识。对该领域的具体挑战缺乏了解是未能兑现重大承诺的主要原因。在此，提出了一个“决策视角”框架，用于开发从概念到市场推出的AI驱动的生物医学产品。该框架强调了风险、目标和关键结果，这些通常是经过验证的医疗AI产品上市的三阶段流程所需的。临床验证，法规事务，数据策略和算法开发得到解决。医疗保健软件中人工智能的开发过程与现代消费者软件开发过程有很大的不同。重点介绍了在整个过程中指导创始人、投资者和关键利益相关者的关键时间点。该框架应被视为创新框架的模板，可用于协调团队沟通和责任，以制定可行的产品开发路线图，从而释放人工智能在医学中的潜力。
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.