Should Watson Be Consulted for a Second Opinion?

Luxton, David D.

doi:10.1001/amajethics.2019.131

Cited by 25 publications

(11 citation statements)

References 6 publications

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“…For instance, it will create a risky situation for both clinicians and patients when it is still unclear who becomes responsible if AI clinical applications offer wrong health care recommendations [ 54 ]. There is also no precise regulation regarding who is held liable when a physician follows the medical recommendations provided by AI and when a physician decides to override the recommendations [ 55 ].…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…The proposal we are concentrating on is to use diagnostic systems as automated second opinions (eg, Luxton 17 ). The core thesis of this proposal is that AI-DSS could replace human doctors in providing additional diagnostic services, akin to a second opinion.…”

Section: Using Ai To Provide Second Opinionsmentioning

confidence: 99%

Responsibility, second opinions and peer-disagreement: ethical and epistemological challenges of using AI in clinical diagnostic contexts

Kempt

Nagel

2021

J Med Ethics

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In this paper, we first classify different types of second opinions and evaluate the ethical and epistemological implications of providing those in a clinical context. Second, we discuss the issue of how artificial intelligent (AI) could replace the human cognitive labour of providing such second opinion and find that several AI reach the levels of accuracy and efficiency needed to clarify their use an urgent ethical issue. Third, we outline the normative conditions of how AI may be used as second opinion in clinical processes, weighing the benefits of its efficiency against concerns of responsibility attribution. Fourth, we provide a ‘rule of disagreement’ that fulfils these conditions while retaining some of the benefits of expanding the use of AI-based decision support systems (AI-DSS) in clinical contexts. This is because the rule of disagreement proposes to use AI as much as possible, but retain the ability to use human second opinions to resolve disagreements between AI and physician-in-charge. Fifth, we discuss some counterarguments.

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“…Some patients may feel able to control and manage their disease, with passive surveillance and/or less contact with the clinician, whereas others may feel overwhelmed by additional responsibilities [55]. AI may also create unrealistic expectations in some patients regarding clinical outcomes, which could have a negative impact on their care and service experience [56]. In addition, some AI-based decisions could be perceived as a restriction on the patient's right to make a free and informed decision [1,53].…”

Section: Human and Cognitive Dimensionsmentioning

confidence: 99%

Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity (Preprint)

Alami¹,

Lehoux²,

Auclair³

et al. 2020

Preprint

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Artificial intelligence (AI) is seen as a strategic lever to improve access, quality, and efficiency of care and services and to build learning and value-based health systems. Many studies have examined the technical performance of AI within an experimental context. These studies provide limited insights into the issues that its use in a real-world context of care and services raises. To help decision makers address these issues in a systemic and holistic manner, this viewpoint paper relies on the health technology assessment core model to contrast the expectations of the health sector toward the use of AI with the risks that should be mitigated for its responsible deployment. The analysis adopts the perspective of payers (ie, health system organizations and agencies) because of their central role in regulating, financing, and reimbursing novel technologies. This paper suggests that AI-based systems should be seen as a health system transformation lever, rather than a discrete set of technological devices. Their use could bring significant changes and impacts at several levels: technological, clinical, human and cognitive (patient and clinician), professional and organizational, economic, legal, and ethical. The assessment of AI’s value proposition should thus go beyond technical performance and cost logic by performing a holistic analysis of its value in a real-world context of care and services. To guide AI development, generate knowledge, and draw lessons that can be translated into action, the right political, regulatory, organizational, clinical, and technological conditions for innovation should be created as a first step.

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Should Watson Be Consulted for a Second Opinion?

Cited by 25 publications

References 6 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

Responsibility, second opinions and peer-disagreement: ethical and epistemological challenges of using AI in clinical diagnostic contexts

Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity (Preprint)

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