A Human-Centered Approach to Identifying Promises, Risks, & Challenges of Text-to-Image Generative AI in Radiology

2025-07-21

AI Summary

Current text-to-image generative AI in radiology lacks systematic evaluation of clinical utility and human factors integration, risking technological misalignment with real-world needs and potential safety hazards. Method: This study uniquely embeds radiologists, residents, and medical students throughout the generative AI development lifecycle, integrating a text-to-CT synthesis model, qualitative user studies, human-computer interaction analysis, and prompt engineering experiments. Contribution/Results: We identify domain-specific challenges in safety, interpretability, and accountability; delineate high-value educational applications; and propose empirically grounded design principles to mitigate misuse of synthetic medical images. Our work establishes a methodological framework and practical guidelines for responsible, clinically aligned generative AI in radiology.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: User Experience and Usability
• Computer Vision: Computational Photography, Image & Video Synthesis

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

As text-to-image generative models rapidly improve, AI researchers are making significant advances in developing domain-specific models capable of generating complex medical imagery from text prompts. Despite this, these technical advancements have overlooked whether and how medical professionals would benefit from and use text-to-image generative AI (GenAI) in practice. By developing domain-specific GenAI without involving stakeholders, we risk the potential of building models that are either not useful or even more harmful than helpful. In this paper, we adopt a human-centered approach to responsible model development by involving stakeholders in evaluating and reflecting on the promises, risks, and challenges of a novel text-to-CT Scan GenAI model. Through exploratory model prompting activities, we uncover the perspectives of medical students, radiology trainees, and radiologists on the role that text-to-CT Scan GenAI can play across medical education, training, and practice. This human-centered approach additionally enabled us to surface technical challenges and domain-specific risks of generating synthetic medical images. We conclude by reflecting on the implications of medical text-to-image GenAI.

Problem

• Evaluating benefits of text-to-image GenAI in radiology practice
• Identifying risks of domain-specific GenAI without stakeholder input
• Exploring medical professionals' perspectives on synthetic CT scans

Innovation

• Human-centered approach for GenAI development
• Involves stakeholders in evaluating text-to-CT Scan GenAI
• Explores medical professionals' perspectives on GenAI