Trust in Artificial Intelligence: Clinicians Are Essential

Bhatt, Umang; Shams, Zohreh

doi:10.1007/978-3-030-81030-6_10

Cited by 1 publication

(2 citation statements)

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“…Clinicians' feedback further indicates the requirement to explain which diagnostic features lead to a patient receiving a particular recommendation and an expectation of knowing how those features will influence treatment (Brown et al 2020). AI systems can demonstrate trustworthiness to clinicians by displaying predictability, procedural transparency, algorithmic transparency, and robustness (Bhatt & Shams 2021). Clinician participation in the development of the system can improve trust, however clinicians are often not present at the developmental stages (Schwartz et al 2021).…”

Section: Resultsmentioning

confidence: 99%

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Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: An integrative review

Higgins

Short

Chalup

et al. 2023

Int J Mental Health Nurs

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An integrative review investigating the incorporation of artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health care settings was undertaken of published literature between 2016 and 2021 across six databases. Four studies met the research question and the inclusion criteria. The primary theme identified was trust and confidence. To date, there is limited research regarding the use of AI‐based decision support systems in mental health. Our review found that significant barriers exist regarding its incorporation into practice primarily arising from uncertainty related to clinician's trust and confidence, end‐user acceptance and system transparency. More research is needed to understand the role of AI in assisting treatment and identifying missed care. Researchers and developers must focus on establishing trust and confidence with clinical staff before true clinical impact can be determined. Finally, further research is required to understand the attitudes and beliefs surrounding the use of AI and related impacts for the wellbeing of the end‐users of care. This review highlights the necessity of involving clinicians in all stages of research, development and implementation of artificial intelligence in care delivery. Earning the trust and confidence of clinicians should be foremost in consideration in implementation of any AI‐based decision support system. Clinicians should be motivated to actively embrace the opportunity to contribute to the development and implementation of new health technologies and digital tools that assist all health care professionals to identify missed care, before it occurs as a matter of importance for public safety and ethical implementation. AI‐basesd decision support tools in mental health settings show most promise as trust and confidence of clinicians is achieved.

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Section: Resultsmentioning

confidence: 99%

“…2020). AI systems can demonstrate trustworthiness to clinicians by displaying predictability, procedural transparency, algorithmic transparency, and robustness (Bhatt & Shams 2021). Clinician participation in the development of the system can improve trust, however clinicians are often not present at the developmental stages (Schwartz et al .…”

Section: Resultsmentioning

confidence: 99%