Objective:

To establish a rigorous, phase-based framework for the responsible development, validation, and deployment of artificial intelligence (AI) in healthcare, modeled after the structured rigor of clinical trials.

Background:

AI promises to transform healthcare delivery, yet its clinical integration remains limited due to fragmented development processes, methodological inconsistencies, and reproducibility challenges. Without standardized protocols and ethical safeguards, AI risks becoming unreliable and inequitable in real-world applications.

Design/Methods:

We propose a four-phase research framework, analogous to drug development pipelines, to guide AI systems from inception to clinical impact. Phase 1 emphasizes data integrity and clinically aligned feature engineering. Phase 2 focuses on model design and internal validation to assess reproducibility and fairness. Phase 3 requires multicenter external validation to evaluate generalizability across diverse populations and settings. Phase 4 outlines post-deployment surveillance, continuous learning, and dynamic governance. Each phase integrates principles of transparency, bias mitigation, and clinical relevance. To illustrate this framework, we present a use case of developing a natural language processing (NLP) model for spinal metastasis detection using radiology reports.

Results:

Our representative scenario demonstrates successful structured navigation through Phases 1-2, with rigorous data cleaning, doc2vec-based feature representation, neural network model optimization, and robust internal validation. Planned Phase 3 efforts involve multicenter evaluation, while Phase 4 includes mechanisms for threshold calibration, feedback loops, and error analysis.

Conclusions: