Table 1.

Mapping physician perspectives of CURATE.AI to Consolidated Framework for Implementation Research (CFIR) domains.

CFIR domain and relevant constructs		Physician insights to facilitate CURATE.AI implementation
Intervention characteristics
	Evidence strength and quality	Establishing satisfactory levels of evidence for the adoption of CURATE.AI
	Relative advantage	Improved drug predictability using CURATE.AI vis-a-vis standard of care
	Adaptability	Accuracy and reproducibility of CURATE.AI
	Complexity	Personalized profiling accomplished through CURATE.AI CURATE.AI’s potential to transform medical practice
Outer setting
	Patient needs and resources	No adverse effects in the use of CURATE. AI Physician’s final say in CURATE.AI-based treatment
Inner setting
	Structural characteristics	Physicians’ access to trial data Physicians’ access to treatment protocols
	Networks and communications culture	Communication and interaction with relevant teams before and during CURATE.AI clinical implementation
	Implementation climate Readiness for implementation	Bringing together expertise to facilitate conversation, familiarity, and ease of implementation
Characteristics of individuals
	Knowledge and beliefs about the intervention	Introducing and familiarizing physicians with the new language of treatment and negotiation idea of machine vs physician
Process
	Planning	Differentiating CURATE.AI through its potential for improved care
	Engaging	Establishing CURATE.AI as a concept of care among physicians Enabling a step-by-step understanding of CURATE.AI
	Executing	Ensuring the presence of systems to access info and data on CURATE.AI Enabling an easy access to the CURATE.AI software
	Reflecting and evaluating	Evaluating the potential of CURATE.AI to be clinically instinctive Understanding ease of use and implementing course corrections Aiming for an integrated use of CURATE.AI in health care