The "Ecosystem as a Service (EaaS)" approach to advance clinical artificial intelligence (cAI)

Abstract

The application of machine learning and artificial intelligence to clinical settings for prevention, diagnosis, treatment, and the improvement of clinical care have been demonstrably cost-effective. However, current clinical AI (cAI) support tools are predominantly created by non-domain experts and algorithms available in the market have been criticized for the lack of transparency behind their creation. To combat these challenges, the Massachusetts Institute of Technology Critical Data (MIT-CD) consortium, an affiliation of research labs, organizations, and individuals that contribute to research in and around data that has a critical impact on human health, has iteratively developed the "Ecosystem as a Service (EaaS)" approach, providing a transparent education and accountability platform for clinical and technical experts to collaborate and advance cAI. The EaaS approach provides a range of resources, from open-source databases and specialized human resources to networking and collaborative opportunities. While mass deployment of the ecosystem still faces several hurdles, here we discuss our initial implementation efforts. We hope this will promote further exploration and expansion of the EaaS approach, while also informing or realizing policies that will accelerate multinational, multidisciplinary, and multisectoral collaborations in cAI research and development, and provide localized clinical best practices for equitable healthcare access.

Fig 1. “Ecosystem as a service” for cAI.

Overview of the EaaS approach for cAI with its three main components: 1. Global coalition of AI clinicians, 2. Training opportunities, and 3. Networking opportunities, integrated with an individual hospital system.

Fig 2. Stakeholders and Operations of the EaaS approach.

High-level depiction of the stakeholders included in the EaaS approach, its operational components, as well as its iterative nature.