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.
Author summary: The development of effective and scalable algorithms that are executable at the frontlines of clinical care require robust data infrastructure, data sharing and usage capabilities that do not compromise patient confidentiality, and flexibility in creating partnerships across various departments, industries, and sectors. To achieve this in a sustainable and replicable manner, the EaaS approach has three components; 1. A global coalition of AI clinicians accessible via the MIT-CD network to support the translation, customization, innovation, and external validations of algorithms, 2. Training opportunities provided via MIT-CD affiliated institutions to educate clinically informed engineers and data literate clinicians, and introduce them to open source databases, and 3. Networking opportunities and events offered by the MIT-CD consortium to share best practices and offer chances for collaboration on innovative research in multidisciplinary teams.