JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) Is rapidly transforming healthcare, offering potential benefits in diagnosis, treatment, and workflow efficiency. However, limited research explores patient perspectives on AI, especially in its role in diagnosis and communication. This study examines patient perceptions of various AI applications, focusing on the diagnostic process and communication.

Objective:

To examine patient perspectives on AI use in healthcare, particularly in diagnostic processes and communication, identifying key concerns, expectations, and opportunities to guide the development and implementation of AI tools.

Methods:

A co-design focus group workshop was conducted with 17 participants (patients and family members) aged 18-80. The session included interactive activities, discussions, and guideline development exploring five AI scenarios: (1) Patient Portal Messaging, (2) Radiological Imaging, (3) Ambient Digital Scribe, (4) Virtual Human Telehealth Call, (5) Clinical Decision Support for HIV Testing. Thematic analysis was used to analyze transcripts and facilitator notes

Results:

Participants reported varying comfort levels with AI applications, with higher comfort for AI tools with less direct patient interaction, such as ambient digital scribes and radiology image readers, and lower comfort for those with more direct interaction, such as virtual human telehealth calls. Five key themes regarding patient perspectives of AI emerged: (1) Concerns Around Model Development and Validation, (2) Concerns Around AI Systems for Patients and Providers, (3) Expectations Around Disclosure of AI Usage, (4) Excitement and Opportunities for AI to Better Address Patient Needs, (5) Patient Concerns Around Data Protection, Privacy, and Security. Participants emphasized the importance of transparency in AI development validation, preferred AI as a supplementary tool rather than a replacement for human clinicians and stressed the need for clear communication about AI’s role in their care. They also highlighted the potential for AI to enhance patient understanding and engagement while expressing concerns about data security and privacy.

Conclusions:

This study highlights the importance of incorporating patient perspectives in the design and implementation of AI tools in healthcare. Transparency, human oversight, clear communication, and data privacy are crucial for patient trust and acceptance of AI in diagnostic processes. These findings inform strategies for individual clinicians, healthcare organizations, and policymakers to ensure responsible and patient-centered AI deployment in healthcare.