Patient Experiences with AI in Healthcare Settings

Hoseini, Marjan

doi:10.61838/kman.aitech.1.3.3

Cited by 3 publications

References 20 publications

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Patient Perspective on Artificial Intelligence in Healthcare: Insights for Diagnostic Communication and Tool Implementation (Preprint)

Foresman,

Joshua,

Tran

et al. 2024

Preprint

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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.

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Patient Perspective on Artificial Intelligence in Healthcare: Insights for Diagnostic Communication and Tool Implementation (Preprint)

Foresman,

Joshua,

Tran

et al. 2024

Preprint

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Enhancing Patients' E-Health Engagement Through Artificial Intelligence

Chauhan,

Gulati,

Mathur

et al. 2024

Advances in Healthcare Information Systems and Administration

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The burgeoning interest in AI within medical research has sparked significant attention, particularly its integration into healthcare services. Despite advancements in AI-assisted healthcare, its practical implementation and impact on patients have been somewhat constrained. The current study investigated the interplay between patients' knowledge of AI, their perceptions of its usefulness and ease of use, attitudes towards AI systems, and their engagement with e-health platforms. Though AI assisted in terms of Medicare facilities, the applicability from patients' perspective is limited. The present research aimed to measure the relationship between patients' AI knowledge, perceived usefulness, perceived ease of use, attitudes towards AI systems, and e-health engagement. The explanatory research method has been adopted for current research work. The research was conducted on 417 patients using AI-based healthcare apps. The study provides several theoretical and practical implications in terms of utilizing AI in the healthcare sector to ensure patients' well-being and health engagement.

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Emotional Intelligence and AI in Geriatric Nursing

Reis da Silva

2024

Advances in Human and Social Aspects of Technology

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The rise of Artificial Intelligence (AI) in healthcare has led to significant advancements in geriatric nursing, transforming both clinical outcomes and care delivery. Yet, as AI plays an increasing role in patient care, there is growing recognition of the need to balance technological innovation with compassionate, human-centred care. This chapter explores how emotional intelligence (EI) and AI can complement one another to improve the physical and mental health of older adults. The chapter examines the critical role of emotional intelligence in geriatric nursing and discusses how AI can support, rather than replace, the empathetic and emotionally aware care provided by nurses. Through case studies, practical applications, and theoretical analysis, this chapter illustrates how integrating EI and AI can enhance care outcomes while maintaining the human touch essential to geriatric nursing. Ethical considerations, such as maintaining dignity and autonomy, and the future of geriatric nursing in an AI-driven world are also explored.

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Patient Experiences with AI in Healthcare Settings

Cited by 3 publications

References 20 publications

Patient Perspective on Artificial Intelligence in Healthcare: Insights for Diagnostic Communication and Tool Implementation (Preprint)

Patient Perspective on Artificial Intelligence in Healthcare: Insights for Diagnostic Communication and Tool Implementation (Preprint)

Enhancing Patients' E-Health Engagement Through Artificial Intelligence

Emotional Intelligence and AI in Geriatric Nursing

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