Trust in artificial intelligence for medical diagnoses

Juravle, Georgiana; Boudouraki, Andriana; Terziyska, Miglena; Rezlescu, Constantin

doi:10.1016/bs.pbr.2020.06.006

Cited by 64 publications

(50 citation statements)

References 46 publications

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“…Review patients in a health-care context: six from outpatient settings, 14,20,25,28,30,31 five from inpatient settings, 12,23,33,37,38 one from a cohort of patients with chronic conditions, 15 one from users of an online symptom checker, 29 one from patient advocacy groups, 22 and one through university hospital cooper ation, melanoma support groups, and social media. 27 The other eight studies recruited participants outside of a health-care context: three recruited university students or affiliates, or both, 26,32,35 and five sampled the general population. 16,24,34,36,39 Among the quantitative and mixed methods studies, ten recruited convenience samples of participants, 14,23,28,30,31,33-37 five did anonymous online surveys for which the response rate could not be calculated, 16,26,27,29,32 three recruited all eligible patients, 12,15,38 one did a simple random sampling of mobile telephone users, 39 and one identified all relevant social media posts.…”

Section: Figure: Preferred Reporting Items For Systematic Reviews and Meta-analyses Flow Diagrammentioning

confidence: 99%

“…27 The other eight studies recruited participants outside of a health-care context: three recruited university students or affiliates, or both, 26,32,35 and five sampled the general population. 16,24,34,36,39 Among the quantitative and mixed methods studies, ten recruited convenience samples of participants, 14,23,28,30,31,33-37 five did anonymous online surveys for which the response rate could not be calculated, 16,26,27,29,32 three recruited all eligible patients, 12,15,38 one did a simple random sampling of mobile telephone users, 39 and one identified all relevant social media posts. 24 Regarding the type of AI being studied, nine (39%) studies assessed a hypothetical AI to be used in a given clinical scenario, eight (35%) assessed AI that was broadly defined, and six (26%) assessed currently available or soon-to-be available AI tools.…”

Section: Figure: Preferred Reporting Items For Systematic Reviews and Meta-analyses Flow Diagrammentioning

confidence: 99%

“…35 A familiarity with technology was not associated with trust in AI. 26,28 AI acceptability was associated with many nonparticipant factors. Participants showed a greater acceptance of AI if they were encouraged to choose the AI over a provider, 26 if the AI were to be applied in a lower risk setting, 26,32,37 if the AI was proven to be more accurate than the providers, 25,27 if the patient's physician recommended the AI, 22 or if the AI fitted societal and cultural norms.…”

Section: Ai Acceptabilitymentioning

confidence: 99%

See 2 more Smart Citations

Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review

Young

Amara

Bhattacharya

et al. 2021

The Lancet Digital Health

154

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Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept of a providerlevel performance in various medical specialties. However, there are many barriers to implementing AI, including patient acceptance and understanding of AI. Patients' attitudes toward AI are not well understood. We systematically reviewed the literature on patient and general public attitudes toward clinical AI (either hypothetical or realised), including quantitative, qualitative, and mixed methods original research articles. We searched biomedical and computational databases from Jan 1, 2000, to Sept 28, 2020, and screened 2590 articles, 23 of which met our inclusion criteria. Studies were heterogeneous regarding the study population, study design, and the field and type of AI under study. Six (26%) studies assessed currently available or soon-to-be available AI tools, whereas 17 (74%) assessed hypothetical or broadly defined AI. The quality of the methods of these studies was mixed, with a frequent issue of selection bias. Overall, patients and the general public conveyed positive attitudes toward AI but had many reservations and preferred human supervision. We summarise our findings in six themes: AI concept, AI acceptability, AI relationship with humans, AI development and implementation, AI strengths and benefits, and AI weaknesses and risks. We suggest guidance for future studies, with the goal of supporting the safe, equitable, and patient-centred implementation of clinical AI.

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Section: Figure: Preferred Reporting Items For Systematic Reviews and Meta-analyses Flow Diagrammentioning

confidence: 99%

Section: Figure: Preferred Reporting Items For Systematic Reviews and Meta-analyses Flow Diagrammentioning

confidence: 99%

Section: Ai Acceptabilitymentioning

confidence: 99%

See 1 more Smart Citation

Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review

Young

Amara

Bhattacharya

et al. 2021

The Lancet Digital Health

154

View full text Add to dashboard Cite

show abstract

“…Currently, very little research has been done characterizing patient and other stakeholder perspectives on applications of AI in healthcare. Additionally, the few studies that have assessed patient perspectives have focused on a narrow array of AI tools, which limits their utility as a guide in anticipating patient engagement with other AI applications in healthcare 13,14 . While engaging patients around specific applications of AI is a crucial step in the research and development process, engagement at this level of specificity does not facilitate analysis of broader public perspectives on AI and its application in healthcare, which is much needed for health policy development, innovation priority setting, and implementation design.…”

Section: Introductionmentioning

confidence: 99%

Patient apprehensions about the use of artificial intelligence in healthcare

et al. 2021

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While there is significant enthusiasm in the medical community about the use of artificial intelligence (AI) technologies in healthcare, few research studies have sought to assess patient perspectives on these technologies. We conducted 15 focus groups examining patient views of diverse applications of AI in healthcare. Our results indicate that patients have multiple concerns, including concerns related to the safety of AI, threats to patient choice, potential increases in healthcare costs, data-source bias, and data security. We also found that patient acceptance of AI is contingent on mitigating these possible harms. Our results highlight an array of patient concerns that may limit enthusiasm for applications of AI in healthcare. Proactively addressing these concerns is critical for the flourishing of ethical innovation and ensuring the long-term success of AI applications in healthcare.

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“…To this regard, it is known that a better communication between patients and physicians is associated with lower patient anxiety, fewer malpractice claims, and improved quality of life ( Levinson et al, 2010 ). As to patients’ trust in AI performance, Juravle et al (2020) reported three online experiments showing that given the option of receiving their diagnosis from AI or human physicians, patients trusted those latter more for both first diagnoses and a second opinion for high risk diseases, and their trust in AI did not increase when they were told that AI outperformed the human doctor, but the trust in AI diagnosis increased significantly when participants could choose their doctor.…”

Section: Artificial Intelligence and Humansmentioning

confidence: 99%

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

et al. 2021

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Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

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Trust in artificial intelligence for medical diagnoses

Cited by 64 publications

References 46 publications

Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review

Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review

Patient apprehensions about the use of artificial intelligence in healthcare

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

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