What This Computer Needs Is a Physician

Verghese, Abraham; Shah, Nigam H.; Harrington, Robert A.

doi:10.1001/jama.2017.19198

Cited by 336 publications

(122 citation statements)

References 7 publications

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“…The gender differences in AI risk perception noted in our survey are novel but may be commensurate with a large body of findings that women are more risk averse than men (24). Thus, female psychiatrists may be more cautious and circumspect in weighing up the benefits versus harms of AI/ML, especially where ambiguities persist with respect to ethics, biases, inequities, data privacy and risks of poorly validated "black box" algorithms (2,10,25). Unlike European countries which operate with universal health coverage and strict regulations about consumer and citizen data privacy, the US operates on multiple insurance-systems and has substantially weaker data privacy rules.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, some technology futurists argue that advancements in AI/ML may one day obviate the need of physicians altogether (7,11). Other informaticians and AI experts are less sanguine, forecasting that the role of doctors can never be fully replaced, and that the future of medicine will likely become a "team sport" between humans and machines (10,13). Consistent with the latter view, a labor market report from Oxford University predicted that while 47% of total US employment was at risk for substitution by intelligent technology over the next two decades, the work performed by doctors would be at lower risk for automation (16).…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence and the future of psychiatry: Insights from a global physician survey

Doraiswamy

Blease

Bodner

2020

Artificial Intelligence in Medicine

114

105

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Background:Futurists have predicted that new autonomous technologies, embedded with artificial intelligence (AI) and machine learning (ML), will lead to substantial job losses in many sectors disrupting many aspects of healthcare. Mental health appears ripe for such disruption given the global illness burden, stigma, and shortage of care providers.Objective: To characterize the global psychiatrist community's opinion regarding the potential of future autonomous technology (referred to here as AI/ML) to replace key tasks carried out in mental health practice.Design: Cross sectional, random stratified sample of psychiatrists registered with Sermo, a global networking platform open to verified and licensed physicians. Main outcome measures:We measured opinions about the likelihood that AI/ML tools would be able to fully replace -not just assist -the average psychiatrist in performing 10 key psychiatric tasks. Among those who considered replacement likely, we measured opinions about how many years from now such a capacity might emerge. We also measured psychiatrist's perceptions about whether benefits of AI/ML would outweigh the risks.Results: Survey respondents were 791 psychiatrists from 22 countries representing North America, South America, Europe and Asia-Pacific. Only 3.8% of respondents felt it was likely that future technology would make their jobs obsolete and only 17% felt that future AI/ML was likely to replace a human clinician for providing empathetic care. Documenting and updating medical records (75%) and synthesizing information (54%) were the two tasks where a majority predicted that AI/ML could fully replace human psychiatrists. Female-and US-based doctors were more uncertain that the benefits of AI would outweigh risks than male-and non-US doctors, respectively. Around one in 2 psychiatrists did however predict that their jobs would be substantially changed by AI/ML. Conclusions:To our knowledge, this is the first global survey to seek the opinions of physicians on the impact of autonomous AI/ML on the future of psychiatry. Our findings provide compelling insights into how physicians think about AI/ML which in turn may help us better integrate technology and reskill doctors to enhance mental health care.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and the future of psychiatry: Insights from a global physician survey

Doraiswamy

Blease

Bodner

2020

Artificial Intelligence in Medicine

114

105

View full text Add to dashboard Cite

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“…Automation is important, but overreliance on automation is not desirable. 8,20 Computer scientists and bioinformaticians, together with practitioners, biostatisticians, and epidemiologists, should outline the “intent behind the design,” 9(p982) including choosing appropriate questions and settings for machine learning use, interpreting findings, and conducting follow-up studies. Such measures would increase the likelihood that the results of the models are meaningful and ethical and that clinical decision support tools based on these algorithms have beneficial effects.…”

Section: Recommendationsmentioning

confidence: 99%

Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

et al. 2018

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A promise of machine learning in health care is the avoidance of biases in diagnosis and treatment; a computer algorithm could objectively synthesize and interpret the data in the medical record. Integration of machine learning with clinical decision support tools, such as computerized alerts or diagnostic support, may offer physicians and others who provide health care targeted and timely information that can improve clinical decisions. Machine learning algorithms, however, may also be subject to biases. The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and measurement error. There is concern that biases and deficiencies in the data used by machine learning algorithms may contribute to socioeconomic disparities in health care. This Special Communication outlines the potential biases that may be introduced into machine learning–based clinical decision support tools that use electronic health record data and proposes potential solutions to the problems of overreliance on automation, algorithms based on biased data, and algorithms that do not provide information that is clinically meaningful. Existing health care disparities should not be amplified by thoughtless or excessive reliance on machines.

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“…Despite the high-performing nature of these algorithms, prior work has shown that CDSSes can be difficult to successfully integrate into practice, citing a lack of HCI consideration as one of the primary reasons for failure [41,44,55,76,78]. For example, users may resist adopting a tool if they do not understand its capabilities, its intended use, or its utility over existing practices [48,72,74]. Algorithmic aversion has also been an underlying challenge for these systems [18,40,47].…”

Section: Introductionmentioning

confidence: 99%

"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making

Cai

Winter

Steiner

et al. 2019

Proc. ACM Hum.-Comput. Interact.

292

188

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Although rapid advances in machine learning have made it increasingly applicable to expert decision-making, the delivery of accurate algorithmic predictions alone is insufficient for effective human-AI collaboration. In this work, we investigate the key types of information medical experts desire when they are first introduced to a diagnostic AI assistant. In a qualitative lab study, we interviewed 21 pathologists before, during, and after being presented deep neural network (DNN) predictions for prostate cancer diagnosis, to learn the types of information that they desired about the AI assistant. Our findings reveal that, far beyond understanding the local, case-specific reasoning behind any model decision, clinicians desired upfront information about basic, global properties of the model, such as its known strengths and limitations, its subjective point-of-view, and its overall design objective-what it's designed to be optimized for. Participants compared these information needs to the collaborative mental models they develop of their medical colleagues when seeking a second opinion: the medical perspectives and standards that those colleagues embody, and the compatibility of those perspectives with their own diagnostic patterns. These findings broaden and enrich discussions surrounding AI transparency for collaborative decision-making, providing a richer understanding of what experts find important in their introduction to AI assistants before integrating them into routine practice. CCS Concepts: • Human-centered computing → Human computer interaction (HCI).

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What This Computer Needs Is a Physician

Cited by 336 publications

References 7 publications

Artificial intelligence and the future of psychiatry: Insights from a global physician survey

Artificial intelligence and the future of psychiatry: Insights from a global physician survey

Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making

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