Artificial Intelligence and Patient-Centered Decision-Making

Bjerring, Jens Christian; Busch, Jacob

doi:10.1007/s13347-019-00391-6

Cited by 125 publications

(123 citation statements)

References 61 publications

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“…Would it make a difference from the patient’s perspective? Seeking to address these questions, it was recently argued that so-called ‘black-box medicine’ conflicts with core ideals of patient-centered medicine [ 33 ]. Since clinicians are no longer able to fully comprehend the inner workings and calculations of the decision aid they are not able to explain to the patient how certain outcomes or recommendations were derived [ 33 ].…”

Section: Resultsmentioning

confidence: 99%

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective

Amann

Blasimme

Frey

et al. 2020

BMC Med Inform Decis Mak

750

417

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Background Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice. Methods Taking AI-based clinical decision support systems as a case in point, we adopted a multidisciplinary approach to analyze the relevance of explainability for medical AI from the technological, legal, medical, and patient perspectives. Drawing on the findings of this conceptual analysis, we then conducted an ethical assessment using the “Principles of Biomedical Ethics” by Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) as an analytical framework to determine the need for explainability in medical AI. Results Each of the domains highlights a different set of core considerations and values that are relevant for understanding the role of explainability in clinical practice. From the technological point of view, explainability has to be considered both in terms how it can be achieved and what is beneficial from a development perspective. When looking at the legal perspective we identified informed consent, certification and approval as medical devices, and liability as core touchpoints for explainability. Both the medical and patient perspectives emphasize the importance of considering the interplay between human actors and medical AI. We conclude that omitting explainability in clinical decision support systems poses a threat to core ethical values in medicine and may have detrimental consequences for individual and public health. Conclusions To ensure that medical AI lives up to its promises, there is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward.

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Section: Resultsmentioning

confidence: 99%

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective

Amann

Blasimme

Frey

et al. 2020

BMC Med Inform Decis Mak

750

417

View full text Add to dashboard Cite

show abstract

“…Once AI systems are unequivocally recognized as being more accurate and reliable than human practitioners in generating predictions, or suggesting treatments and diagnosis, a shift in epistemic authority may occur [13]. This shift will raise questions about a potential obligation to rely on these systems when engaged in medical decision-making processes [48][49][50] -an important debate that is, however, well beyond the scope of this paper.…”

Section: Discussionmentioning

confidence: 97%

“…In some medical domains, the use of AI systems will replace a considerable part of the work of human experts [12]. However, performance comparisons between AI systems and human experts suffer from the difficulty in reproducing and comparing results due to the lack of a unified approach [11,13,14]. Yet, it is reasonable to assume that AI systems will increasingly gain in epistemic authority, even though the assistive role of AI is frequently emphasized [15].…”

Section: Ai-based Decision Support In Healthcarementioning

confidence: 99%

AI support for ethical decision-making around resuscitation: proceed with care

et al. 2021

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Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around cardiopulmonary resuscitation and the determination of a patient’s Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions—conceptual, methodological and procedural—that need to be considered in further development and implementation efforts.

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“…Once AI systems are unequivocally recognized as being more accurate and reliable than human practitioners in generating predictions, or suggesting treatments and diagnosis, a shift in epistemic authority may occur [13]. This shift will raise questions about a potential obligation to rely on these systems when engaged in medical decision-making processes [48-50] – an important debate that is, however, well beyond the scope of this paper.…”

Section: Discussionmentioning

confidence: 99%

AI support for ethical decision-making around resuscitation: proceed with care

Biller‐Andorno¹,

Ferrario²,

Jöbges³

et al. 2020

Preprint

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Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around cardio-pulmonary resuscitation leading to the determination of a patient's Do Not Attempt to Resuscitate (DNAR) status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that 1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and 2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions - conceptual, methodological and procedural - that need to be considered in further development and implementation efforts.

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Artificial Intelligence and Patient-Centered Decision-Making

Cited by 125 publications

References 61 publications

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective

AI support for ethical decision-making around resuscitation: proceed with care

AI support for ethical decision-making around resuscitation: proceed with care

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