# Prioritizing human-AI collaboration in healthcare: the TRIAD framework for trustworthy governance, real-world, and integrated adaptive deployment

## TL;DR

This paper introduces the TRIAD framework to ensure AI in healthcare works well with clinicians and patients by focusing on trust, real-world value, and adaptive deployment.

## Contribution

The novel contribution is the TRIAD framework, which emphasizes human-AI collaboration through governance, real-world evaluation, and adaptive deployment in clinical settings.

## Key findings

- TRIAD evaluates human-AI teams using metrics like accuracy, safety, and workload in real clinical workflows.
- Deployment involves staged rollouts with continuous monitoring of performance and subgroup impact.
- The framework prioritizes clinical value by aligning governance and evaluation around clinicians and patients.

## Abstract

Artificial intelligence (AI) and big data are reshaping the healthcare landscape. However, clinical value depends on how well systems augment clinicians and fit into routine workflows. To this end, we introduce the TRIAD framework: trustworthy governance, real-world clinical value, and integrated adaptive deployment, to guide the development, validation, and deployment of clinical AI. TRIAD requires explicit data provenance and intended use, fairness auditing, and calibrated uncertainty. This framework evaluates the human-AI team in real workflows using team-level metrics, including accuracy, safety, workload, and patterns of acceptance, editing, and overriding. Deployment proceeds via staged rollouts with preregistered guardrails and continuous monitoring of performance and subgroup impact. TRIAD views intelligence as a property of the human-AI team rather than the AI model alone. Aligning governance, evaluation, and deployment around clinicians and patients enables durable gains in safety, equity, efficiency, and experience, thereby elevating clinical value.