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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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A Comprehensive Risk Analysis Framework for Medical AI: A Mixed-Methods Approach.

Yali Wang¹, Zihao Deng¹, Zhaohua Deng¹

¹School of Management, Huazhong University of Science and Technology, Wuhan, China.

Risk Analysis : an Official Publication of the Society for Risk Analysis

|February 20, 2026

Summary

Patients prioritize data quality and privacy as key risks in medical artificial intelligence (AI). Understanding these AI risks is crucial for safe healthcare transformation.

Keywords:

artificial intelligence choice‐based conjoint analysis healthcare mixed‐methods risk framework

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Area of Science:

Medical Artificial Intelligence
Healthcare Risk Analysis
Patient Safety

Background:

Artificial intelligence (AI) is transforming healthcare, introducing new challenges in medical AI decision-making.
A robust risk analysis framework is needed to address these challenges effectively.

Purpose of the Study:

To develop a comprehensive risk analysis framework for medical AI.
To identify and prioritize patient concerns regarding medical AI risks.

Main Methods:

Mixed-methods approach: Latent Dirichlet Allocation (LDA) topic modeling on news articles, grounded theory analysis of patient interviews, and Choice-Based Conjoint (CBC) analysis of patient surveys.
Analysis of 1618 news articles and semi-structured interviews with 21 patients identified key risk attributes.
Online survey of 396 patients assessed preferences for six identified risk attributes.

Main Results:

Identified eight initial risk attributes, refined to six patient-prioritized concerns: data quality, privacy and security, social bias, system performance, liability attribution, and algorithm inference.
Patient risk prioritization: Data quality (30.20%), privacy and security (29.50%), social bias (19.10%), system performance (13.70%), liability attribution (6.87%), and algorithm inference (0.59%).

Conclusions:

A novel risk analysis framework for medical AI is proposed.
Findings offer practical insights for healthcare policymakers, AI developers, and risk analysts to enhance patient safety and trust in medical AI.