How Should Clinicians Communicate With Patients About the Roles of Artificially Intelligent Team Members?

Schiff, Daniel; Borenstein, Jason

doi:10.1001/amajethics.2019.138

Cited by 55 publications

(24 citation statements)

References 9 publications

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“…AI agents should not follow a coercive approach to force patients to make health-related decisions under pressure. Regulations should illuminate patients’ roles in relation to AI applications so that they are aware of their position to refuse AI-based treatments where possible [ 97 ]. An important aspect that needs to be built into AI systems in health care is the transparency of AI algorithms so that the AI system does not remain a black box to the users.…”

Section: Discussionmentioning

confidence: 99%

Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study

Esmaeilzadeh¹,

Mirzaei²,

Dharanikota³

2021

J Med Internet Res

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Background It is believed that artificial intelligence (AI) will be an integral part of health care services in the near future and will be incorporated into several aspects of clinical care such as prognosis, diagnostics, and care planning. Thus, many technology companies have invested in producing AI clinical applications. Patients are one of the most important beneficiaries who potentially interact with these technologies and applications; thus, patients’ perceptions may affect the widespread use of clinical AI. Patients should be ensured that AI clinical applications will not harm them, and that they will instead benefit from using AI technology for health care purposes. Although human-AI interaction can enhance health care outcomes, possible dimensions of concerns and risks should be addressed before its integration with routine clinical care. Objective The main objective of this study was to examine how potential users (patients) perceive the benefits, risks, and use of AI clinical applications for their health care purposes and how their perceptions may be different if faced with three health care service encounter scenarios. Methods We designed a 2×3 experiment that crossed a type of health condition (ie, acute or chronic) with three different types of clinical encounters between patients and physicians (ie, AI clinical applications as substituting technology, AI clinical applications as augmenting technology, and no AI as a traditional in-person visit). We used an online survey to collect data from 634 individuals in the United States. Results The interactions between the types of health care service encounters and health conditions significantly influenced individuals’ perceptions of privacy concerns, trust issues, communication barriers, concerns about transparency in regulatory standards, liability risks, benefits, and intention to use across the six scenarios. We found no significant differences among scenarios regarding perceptions of performance risk and social biases. Conclusions The results imply that incompatibility with instrumental, technical, ethical, or regulatory values can be a reason for rejecting AI applications in health care. Thus, there are still various risks associated with implementing AI applications in diagnostics and treatment recommendations for patients with both acute and chronic illnesses. The concerns are also evident if the AI applications are used as a recommendation system under physician experience, wisdom, and control. Prior to the widespread rollout of AI, more studies are needed to identify the challenges that may raise concerns for implementing and using AI applications. This study could provide researchers and managers with critical insights into the determinants of individuals’ intention to use AI clinical applications. Regulatory agencies should establish normative standards and evaluation guidelines for implementing AI in health care in cooperation with health care institutions. Regular audits and ongoing monitoring and reporting systems can be used to continuously evaluate the safety, quality, transparency, and ethical factors of AI clinical applications.

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Section: Discussionmentioning

confidence: 99%

Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study

Esmaeilzadeh¹,

Mirzaei²,

Dharanikota³

2021

J Med Internet Res

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“…We agree because the items we identified to be included in the information process require specialized knowledge. Specifically, physicians need to know how AI/ML applications are constructed, which data were used to train them, and what their limitations are ( 10 ).…”

Section: Discussionmentioning

confidence: 99%

“…The second implication for the general practitioner is that the patient's fears or overconfidence in AI-aided diagnosis must be addressed. This can be achieved by describing the risks and potential benefits of the AI system, e.g., by providing studies that compare diagnostic accuracy of medical AI compared with human eye doctors ( 10 ). If patients accept and are aware of the risks and limitations of AI-aided diagnosis, they will save a long wait for an ophthalmologist appointment.…”

Section: Discussionmentioning

confidence: 99%

“…However, a specific answer to the ethical question “what information should be included in the information process if AI is involved in the diagnosis” has not been provided regarding the new devices for diagnosing DR. The ethical challenges stem from the opacity of black-box algorithms, potential biases within the training data, tensions between improving healthcare and generating profit, and the liability in case of performance errors ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

et al. 2021

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Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting.Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a broad search on general issues of AI-based diagnosis. An ethics content analysis was conducted inductively to extract two features of included publications: (1) novel information content for AI-aided diagnosis and (2) the ethical justification for its disclosure.Results: The narrow search yielded n = 537 records of which n = 4 met the inclusion criteria. The information process was scarcely addressed for primary care setting. The broad search yielded n = 60 records of which n = 11 were included. In total, eight novel elements were identified to be included in the information process for ethical reasons, all of which stem from the technical specifics of medical AI.Conclusions: Implications for the general practitioner are two-fold: First, doctors need to be better informed about the ethical implications of novel technologies and must understand them to properly inform patients. Second, patient's overconfidence or fears can be countered by communicating the risks, limitations, and potential benefits of diagnostic AI systems. If patients accept and are aware of the limitations of AI-aided diagnosis, they increase their chances of being diagnosed and treated in time.

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“…Would this consent meet the ethical or legal standards we expect in healthcare? (14,15). The authors explain that they are concerned about consent because the nature of opaque systems means we necessarily cannot know how the systems are learning or drawing connections.…”

Section: The First Option: Opaque Systems In Healthcarementioning

confidence: 99%

What Kind of Artificial Intelligence Should We Want for Use in Healthcare Decision-Making Applications?

Wadden

2021

bioethics

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How Should Clinicians Communicate With Patients About the Roles of Artificially Intelligent Team Members?

Cited by 55 publications

References 9 publications

Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study

Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study

Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

What Kind of Artificial Intelligence Should We Want for Use in Healthcare Decision-Making Applications?

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