Brad Partridge

Background/Objectives: This study explored the views of dermatologists in Australia on the use of Artificial Intelligence (AI) in dermatology.Method: Fellows and Trainees of the Australasian College of Dermatologists (ACD) were invited to participate in an anonymous online survey, resulting in a sample of 122 completed surveys (response rate 16.2%).Results: Although 44% have used AI in their dermatology practice, only a minority are using AI regularly for clinical (12%) or administrative purposes (17%). A key barrier is trust, with most (69%) participants either unwilling or unsure about trusting AI for supporting the diagnosis of skin cancer. Participants identified accuracy and information on datasets, limitations, benefits, and the purpose of AI as important for trust. 52% want AI accuracy to be equivalent to or superior to the best dermatologist if it is used for clinical diagnosis. Participants perceived a range of benefits and risks of AI, with key risks around accuracy and divestment of AI to tech companies, and key benefits being reduction of monotonous tasks and improved patient access. Few (10%) are concerned about AI replacing dermatologists even though almost half (47%) expect key aspects of dermatology work will be performed by AI.Conclusions: Dermatologists in Australia are at an early stage of integrating AI into practice, with most wary or unsure about the accuracy of currently available AI tools for diagnostic purposes. Developing workflows that are acceptable to clinicians may require knowledge outreach from Dermatology Colleges, such as the ACD, to help clinicians develop well informed views on AI.

Background: Evidence that artificial intelligence (AI) may improve melanoma detection has led to calls for increased human-AI collaboration in clinical workflows. However, AI-based support may entail a wide range of specific functions for AI. To appropriately integrate AI into decision-making processes, it is crucial to understand the precise role that clinicians see AI playing within their clinical deliberations. Objective: This study aims to provide an in-depth understanding of how a range of clinicians involved in melanoma screening and diagnosis conceptualize the role of AI within their decision-making and what these conceptualizations mean for good decision-making. Methods: This qualitative exploration used in-depth individual interviews with 30 clinicians, predominantly from Australia and New Zealand (n=26, 87%), who engaged in melanoma detection (n=17, 57% dermatologists; n=6, 20% general practitioners with an interest in skin cancer; and n=7, 23% melanographers). The vast majority of the sample (n=25, 83%) had interacted with or used 2D or 3D skin imaging technologies with AI tools for screening or diagnosis of melanoma, either as part of testing through clinical AI reader studies or within their clinical work. Results: We constructed the following 5 themes to describe how participants conceptualized the role of AI within decision-making when it comes to melanoma detection: theme 1 (integrative theme)—the importance of good clinical judgment; theme 2—AI as just one tool among many; theme 3—AI as an adjunct after a clinician’s decision; theme 4—AI as a second opinion for unresolved decisions; theme 5—AI as an expert guide before decision-making. Participants articulated a major conundrum—AI may benefit inexperienced clinicians when conceptualized as an “expert guide,” but overreliance, deskilling, and a failure to recognize AI errors may mean only experienced clinicians should use AI “as a tool.” However, experienced clinicians typically relied on their own clinical judgment, and some could be wary of allowing AI to “influence” their deliberations. The benefit of AI was often to reassure decisions once they had been reached by conceptualizing AI as a kind of “checker,” “validator,” or in a small number of equivocal cases, as a genuine “second opinion.” This raised questions about the extent to which experienced clinicians truly seek to “collaborate” with AI or use it to inform decisions. Conclusions: Clinicians conceptualized AI support in an array of disparate ways that have implications for how AI should be incorporated into clinical workflows. A priority for clinicians is the conservation of good clinical acumen, and our study encourages a more focused engagement with users about the precise way to incorporate AI into the clinical decision-making process for melanoma detection.

BACKGROUND Evidence that artificial intelligence (AI) may improve melanoma detection has led to calls for increased human-AI collaboration in clinical workflows. However, AI-based support may entail a wide range of specific functions for AI. To appropriately integrate AI into decision-making processes, it is crucial to understand the precise role that clinicians see AI playing within their clinical deliberations. OBJECTIVE This study aims to provide an in-depth understanding of how a range of clinicians involved in melanoma screening and diagnosis conceptualize the role of AI within their decision-making and what these conceptualizations mean for good decision-making. METHODS This qualitative exploration used in-depth individual interviews with 30 clinicians, predominantly from Australia and New Zealand (n=26, 87%), who engaged in melanoma detection (n=17, 57% dermatologists; n=6, 20% general practitioners with an interest in skin cancer; and n=7, 23% melanographers). The vast majority of the sample (n=25, 83%) had interacted with or used 2D or 3D skin imaging technologies with AI tools for screening or diagnosis of melanoma, either as part of testing through clinical AI reader studies or within their clinical work. RESULTS We constructed the following 5 themes to describe how participants conceptualized the role of AI within decision-making when it comes to melanoma detection: theme 1 (integrative theme)—the importance of good clinical judgment; theme 2—AI as just one tool among many; theme 3—AI as an adjunct after a clinician’s decision; theme 4—AI as a second opinion for unresolved decisions; theme 5—AI as an expert guide before decision-making. Participants articulated a major conundrum—AI may benefit inexperienced clinicians when conceptualized as an “expert guide,” but overreliance, deskilling, and a failure to recognize AI errors may mean only experienced clinicians should use AI “as a tool.” However, experienced clinicians typically relied on their own clinical judgment, and some could be wary of allowing AI to “influence” their deliberations. The benefit of AI was often to reassure decisions once they had been reached by conceptualizing AI as a kind of “checker,” “validator,” or in a small number of equivocal cases, as a genuine “second opinion.” This raised questions about the extent to which experienced clinicians truly seek to “collaborate” with AI or use it to inform decisions. CONCLUSIONS Clinicians conceptualized AI support in an array of disparate ways that have implications for how AI should be incorporated into clinical workflows. A priority for clinicians is the conservation of good clinical acumen, and our study encourages a more focused engagement with users about the precise way to incorporate AI into the clinical decision-making process for melanoma detection.

We investigated whether beliefs about addiction being a ‘disease’ or ‘brain disease’, and holding certain beliefs about addiction aetiology, are associated with public views about addicted persons and support for different types of treatment, coerced treatment and punishment for addiction. Data were collected as part of the 2012 Queensland Social Survey, a computer assisted telephone interview of 1263 residents of Queensland, Australia. Participants were presented with scenarios of two addicted males, one who was addicted to heroin and the other addicted to alcohol. Participants were then asked a series of questions for both characters. There was widespread support for all treatment modalities (alcohol: 80.8-98.0%, heroin: 89.9-97.2%). There was less support for coerced treatment for alcohol than heroin addiction (alcohol: 41%, heroin: 71%, χ2 = 273.90, p < 0.001). Being 35 years of age or older (alcohol: OR = 0.58 (0.37-0.91), heroin: OR = 0.49 (0.28-0.85)) and having 15 or more years of education (alcohol: OR = 0.60 (0.44-0.81), heroin: 0.55 (0.40-0.75)) predicted less support for coerced treatment. 31.7% of respondents agreed heroin use should be punished by imprisonment and being 35 years of age or older (OR = 0.51 (0.33-0.80)) predicted lack of support. The sample agreed that an alcohol or heroin dependent person would suffer career damage (alcohol: 96.2%, heroin: 98.9%), marriage breakdown (alcohol: 92.2%, heroin: 97.3%) and get in trouble with the law (alcohol: 92.3%, heroin: 98.9%). Respondents expressed more comfort with encountering alcohol rather than heroin addicted persons in the workplace or at a dinner party. Beliefs that addiction was a ‘brain disease’ or a ‘disease’ did not predict any of these attitudes. Beliefs about addiction aetiology were inconsistent predictors of outcomes measured. Age and educational attainment were the most consistent predictors of stigmatising beliefs and beliefs about coercion and punishment. Beliefs that addiction is a ‘disease’ or a ‘brain disease’ were not associated with an overall reduction in beliefs about stigma, coercion or punishment. Beliefs in different causes of addiction were not consistent predictors of beliefs about stigma, coercion or punishment.