Abstract Details

Title From Code to Clinic: A Phase-based Framework for AI Research in Healthcare

Topic ��ɫ��, Research, and Methodology

Presentation(s) P1 - Poster Session 1 (8:00 AM-9:00 AM)

Poster/Presentation
Number 15-004

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

The proposed framework offers a reproducible, ethically sound roadmap for clinical AI, ensuring that tools are not only technically performant but also safe, equitable, and trustworthy. We call on clinicians, developers, and regulators to adopt this lifecycle-aware model, aligning AI innovation with the same scientific and ethical rigor demanded of any medical intervention. This is essential for transitioning AI from code to clinic responsibly and sustainably.

Authors/Disclosures
Suhrud Panchawagh, MBBS PRESENTER	Dr. Panchawagh has nothing to disclose.
Vinay Suresh, MBBS	Dr. Suresh has nothing to disclose.
Vivek Sanker, MBBS	Dr. Sanker has nothing to disclose.

��ɫ����

��ɫ����

��ɫ��

��ɫ��