Streamlining medical software development with CARE lifecycle and CARE agent: an AI-driven technology readiness level assessment tool.

Background: Developing medical software requires navigating complex regulatory, ethical, and operational challenges. A comprehensive framework that supports both technical maturity and clinical safety is essential for effective artificial intelligence and machine learning system deployment. This paper introduces the Clinical Artificial Intelligence Readiness Evaluator Lifecycle and the Clinical Artificial Intelligence Readiness Evaluator Agent-a framework and AI-driven tool designed to streamline technology readiness level assessments in medical software development.

Methods: We developed the framework using an iterative process grounded in collaborative stakeholder analysis. Key institutional stakeholders-including clinical informatics experts, data engineers, ethicists, and operational leaders-were engaged to identify and prioritize the regulatory, ethical, and technical requirements unique to clinical AI/ML development. This approach, combined with a thorough review of existing methodologies, informed the creation of a lifecycle model that guides technology maturation from initial concept to full deployment. The AI-driven tool was implemented using a retrieval-augmented generation strategy and evaluated through a synthetic use case (the Diabetes Outcome Predictor). Evaluation metrics included the proportion of correctly addressed assessment questions and the overall time required for automated review, with human adjudication validating the tool's performance.

Results: The findings indicate that the proposed framework effectively captures the complexities of clinical AI development. In the synthetic use case, the AI-driven tool identified that 32.8% of the assessment questions remained unanswered, while human adjudication confirmed discrepancies in 19.4% of these instances. These outcomes suggest that, when fully refined, the automated assessment process can reduce the need for extensive multi-stakeholder involvement, accelerate project timelines, and enhance resource efficiency.

Conclusions: The Clinical Artificial Intelligence Readiness Evaluator Lifecycle and Agent offer a robust and methodologically sound approach for evaluating the maturity of medical AI systems. By integrating stakeholder-driven insights with an AI-based assessment process, this framework lays the groundwork for more streamlined, secure, and effective clinical AI development. Future work will focus on optimizing retrieval strategies and expanding validation across diverse clinical applications.