The Patient/Industry Trade-off in Medical Artificial Intelligence

TL;DR

This paper discusses the challenges and trade-offs in integrating AI into healthcare, emphasizing the need for balancing patient benefits with industry interests through transparency, collaboration, and prioritizing healthcare outcomes.

Contribution

It identifies key barriers to clinical adoption of AI and proposes strategies to foster industry partnerships that prioritize patient-centered healthcare improvements.

Findings

Lack of clinically relevant metrics hampers AI adoption

Clinical trials and longitudinal studies are often missing

Transparency and industry collaboration are crucial for success

Abstract

Artificial intelligence (AI) in healthcare has led to many promising developments; however, increasingly, AI research is funded by the private sector leading to potential trade-offs between benefits to patients and benefits to industry. Health AI practitioners should prioritize successful adaptation into clinical practice in order to provide meaningful benefits to patients, but translation usually requires collaboration with industry. We discuss three features of AI studies that hamper the integration of AI into clinical practice from the perspective of researchers and clinicians. These include lack of clinically relevant metrics, lack of clinical trials and longitudinal studies to validate results, and lack of patient and physician involvement in the development process. For partnerships between industry and health research to be sustainable, a balance must be established between patient and industry benefit. We propose three approaches for addressing this gap: improved transparency and explainability of AI models, fostering relationships with industry partners that have a reputation for centering patient benefit in their practices, and prioritization of overall healthcare benefits. With these priorities, we can sooner realize meaningful AI technologies used by clinicians where mutua