Assessing the impact of information on patient attitudes toward artificial intelligence-based clinical decision support (AI/CDS): a pilot web-based SMART vignette study

BACKGROUND

It is increasingly recognised that the success of artificial intelligence-based clinical decision support (AI/CDS) tools will depend on physician and patient trust, but factors impacting patients' views on clinical care reliant on AI have been less explored.

OBJECTIVE

This pilot study explores whether, and in what contexts, detail of explanation provided about AI/CDS tools impacts patients' attitudes toward the tools and their clinical care.

METHODS

We designed a Sequential Multiple Assignment Randomized Trial vignette web-based survey. Participants recruited through Amazon Mechanical Turk were presented with hypothetical vignettes describing health concerns and were sequentially randomised along three factors: (1) the level of detail of explanation regarding an AI/CDS tool; (2) the AI/CDS result; and (3) the physician's level of agreement with the AI/CDS result. We compared mean ratings of comfort and confidence by the level of detail of explanation using t-tests. Regression models were fit to confirm conditional effects of detail of explanation.

RESULTS

The detail of explanation provided regarding the AI/CDS tools was positively related to respondents' comfort and confidence in the usage of the tools and their perception of the physician's final decision. The effects of detail of explanation on their perception of the physician's final decision were different given the AI/CDS result and the physician's agreement or disagreement with the result.

CONCLUSIONS

More information provided by physicians regarding the use of AI/CDS tools may improve patient attitudes toward healthcare involving AI/CDS tools in general and in certain contexts of the AI/CDS result and physician agreement.

Trust Building in Public Health Approaches: The Importance of a "People-Centered" Concept in Crisis Response

Purpose

To argue for trust-building as a key solution for responding to public health crises in the face of political ambiguity in international health governance.

Patients and Methods

This perspective piece reviews fundamental concepts and discusses future directions using secondary data from open-access sources.

Results

The promise of learning from Covid-19 and previous public health crises, along with the growing recognition of a 'Health For All Policies' approach, clash with siloed preparations, management, and recovery plans for future emergency crises. Trust is proposed as a possible solution to overcome these limitations. It acts as a binding force that unites individuals within the community, fostering a sense of belonging and participation. Trust-building is viewed as a "People-Centered" approach in Crisis Response, aimed at creating active and resilient communities to foster preparation and readiness, respond to emergent risks, facilitate recovery, and mitigate risks. A remaining question is how to measure and identify the dimensions and determinants of trust in specific circumstances. Some ideas are systematized to highlight the pathway to build trust in public health approaches, including transparency, education, robust and equitable health systems, strengthened social capital, stakeholders' engagement, and health workforce training.

Conclusion

Trust in public health approaches can be fostered through consistent delivery of quality care, a clear, shared vision, and values underpinned by ethical standards. It requires a commitment to stakeholder well-being, including staff, and the integration of reliability, integrity, and transparency into policies, strategies, and practices. Exemplary leadership, openness in resource utilization, addressing waste or corruption, and effective communication of these principles are essential.

Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk

In its AI Act, the European Union chose to understand trustworthiness of AI in terms of the acceptability of its risks. Based on a narrative systematic literature review on institutional trust and AI in the public sector, this article argues that the EU adopted a simplistic conceptualization of trust and is overselling its regulatory ambition. The paper begins by reconstructing the conflation of "trustworthiness" with "acceptability" in the AI Act. It continues by developing a prescriptive set of variables for reviewing trust research in the context of AI. The paper then uses those variables for a narrative review of prior research on trust and trustworthiness in AI in the public sector. Finally, it relates the findings of the review to the EU's AI policy. Its prospects to successfully engineer citizen's trust are uncertain. There remains a threat of misalignment between levels of actual trust and the trustworthiness of applied AI.

The Reconstruction of Human Fingerprints From High-Resolution Computed Tomography Data: Feasibility Study and Associated Ethical Issues

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