Trust in AI for Health Care: Key Insights and Recommendations

Importance of Trust in AI Systems in Health Care

The integration of artificial intelligence (AI) into health care systems is heralded as a transformative advancement in medical practice. However, the successful adoption of these technologies hinges significantly on user trust. Trust in AI systems is paramount for various stakeholders, including health care professionals, patients, and policymakers. The contemporary landscape of health care is increasingly characterized by complex AI systems that promise enhanced diagnostics, personalized treatment plans, and improved patient outcomes. Yet, despite these potential benefits, public skepticism and distrust pose substantial barriers to effective implementation.

Trust is inherently a relational construct, influenced by various factors such as the perceived reliability of AI systems, the transparency of their operations, and the ethical implications of their use. In health care, where decisions can have profound effects on patient health and safety, establishing a foundation of trust is essential. Research indicates that trust is a critical determinant of the willingness to engage with AI systems, directly impacting the extent to which these technologies are utilized effectively in clinical settings (Starke et al., 2025).

Key Factors Influencing Trust in Medical AI

To foster trust in AI systems within health care, it is essential to identify and understand the factors that influence it. Key factors include:

1. Transparency: The clarity regarding how AI algorithms function, the data they utilize, and the decision-making processes they follow. Transparency helps demystify AI systems for users and stakeholders, thus building trust.

2. Accuracy and Reliability: Users must believe that AI systems provide accurate and dependable results. Demonstrated effectiveness through clinical trials and real-world applications enhances trustworthiness.

3. User Experience: The design and usability of AI applications play a crucial role in user satisfaction. Systems that are intuitive and user-friendly foster a more positive interaction, enhancing trust.

4. Ethical Considerations: The ethical implications of AI deployment, including fairness, accountability, and the potential for bias, greatly influence trust. Stakeholders need assurance that AI systems do not perpetuate existing inequalities in health care.

5. Stakeholder Engagement: Involving health care professionals and patients in the design and implementation processes can improve trust. Engaging users from diverse backgrounds ensures that their needs and concerns are addressed.

6. Regulatory and Institutional Support: Trust is bolstered when AI systems meet regulatory standards and are backed by reputable institutions. This institutional endorsement provides users with confidence in the safety and efficacy of the technology.

Case Studies Highlighting Trust Dynamics in AI Applications

Several case studies illustrate the complexities of trust in AI applications within health care:

Case Study 1: Diagnostic AI in Radiology

AI systems used for diagnostic purposes in radiology, such as those analyzing chest X-rays, have demonstrated significant potential in enhancing diagnostic accuracy. For example, a system that achieved high sensitivity and low specificity in identifying conditions from X-rays showed promise in improving patient outcomes. However, concerns about the opacity of AI decision-making processes and the potential for job displacement among radiologists have raised questions about trust. Ensuring that AI systems are transparent and that their outputs can be understood by medical professionals is crucial for fostering trust in such applications.

Case Study 2: Predictive AI for Clinical Risk Assessment

Predictive AI models that assess the risk of deteriorating health conditions in patients can substantially improve clinical decision-making. For instance, AI systems that predict circulatory failure in intensive care units (ICUs) can alert clinicians to potential crises ahead of time, allowing for timely interventions. However, the reliance on these systems necessitates a strong foundation of trust, particularly given the high stakes involved in critical care settings. Trust can be enhanced through thorough validation and real-world performance assessments.

Case Study 3: Public Health AI for Disease Surveillance

AI tools like EPIWATCH for monitoring disease outbreaks illustrate the importance of trust in public health applications. The effectiveness of such tools depends on users’ confidence in the data accuracy and the system’s ability to provide timely and relevant information. Transparency around the data sources and algorithms used in these systems is essential to foster trust among public health officials and the broader community.

Strategies to Enhance Trustworthiness of AI in Health Care

To enhance the trustworthiness of AI in health care, several strategies can be implemented:

1. Implementing Transparent Practices: Developers should provide clear information about how AI systems work, the data they use, and the underlying algorithms. This transparency is critical for building trust among users.

2. Conducting Comprehensive Validation Studies: Regular and rigorous validation of AI systems in clinical settings ensures that they meet safety and efficacy standards. Publishing the results of these studies can also enhance credibility.

3. Engaging Stakeholders Throughout Development: Involving health care professionals and patients in the design and testing phases of AI development can help tailor systems to meet their needs and foster a sense of ownership and trust.

4. Establishing Ethical Guidelines and Oversight: Creating and enforcing ethical guidelines for AI deployment in health care will help ensure that these technologies are used fairly and responsibly, addressing potential biases and inequities.

5. Continuous Education and Training: Providing training for health care professionals on the capabilities and limitations of AI systems can enhance their confidence in using these technologies, thereby fostering trust.

Recommendations for Future Research on Trust in AI

To further explore and enhance trust in AI systems in health care, future research should focus on:

1. Longitudinal Studies: Conducting longitudinal research to assess how trust in AI systems evolves over time, particularly as users gain more experience with these technologies.

2. Diversity in Research Populations: Expanding the scope of research to include diverse populations to understand how cultural and contextual factors influence trust dynamics.

3. Investigating the Role of Emotions: Examining the emotional responses associated with trust in AI, exploring how feelings of fear, anxiety, and hope impact user acceptance and engagement.

4. Developing Trust Metrics: Creating standardized metrics for assessing trust in AI systems that consider various dimensions, including psychological, social, and ethical aspects.

5. Exploring Regulatory Frameworks: Assessing how different regulatory environments impact trust in AI technologies, particularly in varying health care settings across the globe.

FAQ

Why is trust important in AI for health care?

Trust is crucial because it directly influences the willingness of health care professionals and patients to use AI systems. Without trust, the benefits of AI technologies cannot be realized.

What factors influence trust in AI systems?

Key factors include transparency, accuracy, user experience, ethical considerations, stakeholder engagement, and regulatory support.

How can trust in AI systems be enhanced?

Trust can be enhanced through transparent practices, comprehensive validation studies, stakeholder engagement, ethical guidelines, and continuous education.

What are the implications of low trust in AI systems?

Low trust can lead to underutilization of AI technologies, potentially depriving health care systems of valuable tools that could improve patient outcomes.

What areas should future research focus on?

Future research should focus on longitudinal studies, diversity in research populations, emotional impacts on trust, developing trust metrics, and exploring regulatory frameworks.

References

1. Starke, G., Gille, F., Termine, A., Aquino, Y. S. J., Chavarriaga, R., Ferrario, A., Hastings, J., Jongsma, K., Kellmeyer, P., Kulynych, B., Postan, E., Racine, E., Sahin, D., Tomaszewska, P., Vold, K., Webb, J., & Ienca, M. (2025). Finding Consensus on Trust in AI in Health Care: Recommendations From a Panel of International Experts. Journal of Medical Internet Research, 27(1), e56306. https://doi.org/10.2196/56306

2. Mavragani, A., Ghammem, R., & Zuair, A. (2025). Effect of the Reassured Self-Compassion–Based School Program on Anxiety, Video Game Addiction, and Body Image Among Rural Female Adolescents: Retrospective Study. JMIR Formative Research, 9(1), e68840. https://doi.org/10.2196/68840

3. Fazli, G. S., Phipps, E., Crighton, E., Sarwar, A., & Ashley-Martin, J. (2025). Engaging, recruiting, and retaining pregnant people from marginalized communities in environmental health cohort studies: a scoping review. BMC Public Health, 25(1), 22033. https://doi.org/10.1186/s12889-025-22033-7