How Will Artificial Intelligence Affect Patient-Clinician Relationships?

Nagy, Matthew; Sisk, Bryan A.

doi:10.1001/amajethics.2020.395

Cited by 18 publications

(7 citation statements)

References 21 publications

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“…The type of role that clinician and patient assign to the medical AI-system in the decision-making process will likely determine not only what they expect from the system but also how they interact with it and one another [ 41 ]. This raises the fundamental question of how medical AI will impact professional identities, role perception and, ultimately doctor-patient relationships—issues we believe are currently under investigated [ 48 , 49 ].…”

Section: Discussionmentioning

confidence: 99%

“…Our findings highlight the fear of a technology-induced dehumanization of care, emphasizing the importance of relational aspects and empathy in stroke medicine and healthcare more generally, mirroring earlier work [ 54 – 56 ]. The role of interpersonal relationships is also discussed in an emerging body of literature, which suggests that the AI revolution will, in fact, require a shift towards a more relationship-oriented role of clinicians [ 49 , 53 , 57 – 59 ]. Nagy and colleagues, for instance, argue that even though AI may unburden clinicians from some of the analytical and administrative demands–as also suggested by findings of our study—it might, on the other hand, increase the interpersonal demands of patient care [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

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Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke

et al. 2023

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Introduction Artificial intelligence (AI) has the potential to transform clinical decision-making as we know it. Powered by sophisticated machine learning algorithms, clinical decision support systems (CDSS) can generate unprecedented amounts of predictive information about individuals’ health. Yet, despite the potential of these systems to promote proactive decision-making and improve health outcomes, their utility and impact remain poorly understood due to their still rare application in clinical practice. Taking the example of AI-powered CDSS in stroke medicine as a case in point, this paper provides a nuanced account of stroke survivors’, family members’, and healthcare professionals’ expectations and attitudes towards medical AI. Methods We followed a qualitative research design informed by the sociology of expectations, which recognizes the generative role of individuals’ expectations in shaping scientific and technological change. Semi-structured interviews were conducted with stroke survivors, family members, and healthcare professionals specialized in stroke based in Germany and Switzerland. Data was analyzed using a combination of inductive and deductive thematic analysis. Results Based on the participants’ deliberations, we identified four presumed roles that medical AI could play in stroke medicine, including an administrative, assistive, advisory, and autonomous role AI. While most participants held positive attitudes towards medical AI and its potential to increase accuracy, speed, and efficiency in medical decision making, they also cautioned that it is not a stand-alone solution and may even lead to new problems. Participants particularly emphasized the importance of relational aspects and raised questions regarding the impact of AI on roles and responsibilities and patients’ rights to information and decision-making. These findings shed light on the potential impact of medical AI on professional identities, role perceptions, and the doctor-patient relationship. Conclusion Our findings highlight the need for a more differentiated approach to identifying and tackling pertinent ethical and legal issues in the context of medical AI. We advocate for stakeholder and public involvement in the development of AI and AI governance to ensure that medical AI offers solutions to the most pressing challenges patients and clinicians face in clinical care.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke

et al. 2023

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“…The degree of transparency in data collection should be evaluated and discussed together with potential sources of bias and ways to reduce them [47]. The presence of an adequate time for discussion between the patient and clinician (to examine the benefits and risks of a treatment, the potential alternatives, the strength of predicted risks and prognostic outcomes, and to consider the patient's right not-to-know and its consequences) should be assessed [48,49].…”

Section: Ethical and Legal Issues Of In Silico Medicinementioning

confidence: 99%

Health Technology Assessment for In Silico Medicine: Social, Ethical and Legal Aspects

Leo

Tumolo

Sabina

et al. 2022

IJERPH

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The application of in silico medicine is constantly growing in the prevention, diagnosis, and treatment of diseases. These technologies allow us to support medical decisions and self-management and reduce, refine, and partially replace real studies of medical technologies. In silico medicine may challenge some key principles: transparency and fairness of data usage; data privacy and protection across platforms and systems; data availability and quality; data integration and interoperability; intellectual property; data sharing; equal accessibility for persons and populations. Several social, ethical, and legal issues may consequently arise from its adoption. In this work, we provide an overview of these issues along with some practical suggestions for their assessment from a health technology assessment perspective. We performed a narrative review with a search on MEDLINE/Pubmed, ISI Web of Knowledge, Scopus, and Google Scholar. The following key aspects emerge as general reflections with an impact on the operational level: cultural resistance, level of expertise of users, degree of patient involvement, infrastructural requirements, risks for health, respect of several patients’ rights, potential discriminations for access and use of the technology, and intellectual property of innovations. Our analysis shows that several challenges still need to be debated to allow in silico medicine to express all its potential in healthcare processes.

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“…The physician-patient relationship has been transformed by technical advances and the maturity of social sciences, but it is yet uncertain how AI tools will impact this relationship. Some argue that it will help by easing clinician workload and providing more personalized data for shared decision-making, while others question doctors' role once critical tasks are delegated to sophisticated algorithms ( 121 ). Clinical guidelines will need to be updated to consider the potential role of AI tools between healthcare workers and patients ( 95 , 122 ).…”

Section: Unintended Consequences Of Ai Applications In Cardiovascular...mentioning

confidence: 99%

Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging

Szabó

Raisi‐Estabragh²,

Salih³

et al. 2022

Front. Cardiovasc. Med.

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A growing number of artificial intelligence (AI)-based systems are being proposed and developed in cardiology, driven by the increasing need to deal with the vast amount of clinical and imaging data with the ultimate aim of advancing patient care, diagnosis and prognostication. However, there is a critical gap between the development and clinical deployment of AI tools. A key consideration for implementing AI tools into real-life clinical practice is their “trustworthiness” by end-users. Namely, we must ensure that AI systems can be trusted and adopted by all parties involved, including clinicians and patients. Here we provide a summary of the concepts involved in developing a “trustworthy AI system.” We describe the main risks of AI applications and potential mitigation techniques for the wider application of these promising techniques in the context of cardiovascular imaging. Finally, we show why trustworthy AI concepts are important governing forces of AI development.

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How Will Artificial Intelligence Affect Patient-Clinician Relationships?

Cited by 18 publications

References 21 publications

Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke

Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke

Health Technology Assessment for In Silico Medicine: Social, Ethical and Legal Aspects

Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging

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