Artificial intelligence in the detection of skin cancer

Beltrami, Eric J.; Brown, Alistair C; Salmon, P. R.; Leffell, David J.; Ko, Justin; Grant‐Kels, Jane M.

doi:10.1016/j.jaad.2022.08.028

Cited by 34 publications

(18 citation statements)

References 21 publications

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“…Ursin et al 2021b), and dermatology (e.g., skin cancer, cf. Beltrami et al 2022). Our findings may have also relevance for nondiagnostic medical AI systems, such as those assisting in administrative processes or those integrated in medical devices like defibrillators adjusting automatically (Brown et al 2022;Ranschaert et al 2021).…”

Section: Introductionmentioning

confidence: 74%

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

et al. 2023

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Definition of the problem The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. Which levels of explicability are needed to obtain informed consent when utilizing medical AI? Arguments We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example. Conclusion We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements.

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Section: Introductionmentioning

confidence: 74%

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

et al. 2023

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“…It is warranted to confirm our findings with additional studies conducted in other populations or by using a clinical trial design. Some recent studies investigating the application of novel technology of artificial intelligence including machine learning and deep learning to advance our knowledge toward a better understanding and futuristic management of skin cancer and non-cancer skin diseases have shown promising results [54][55][56][57][58][59][60]. It is believed that similar techniques and methods can be applied in the research of clinical risk prediction in the field of dermatology and in helping clinicians identify patients who can be benefited from the use of metformin in the prevention of non-cancer skin diseases in the future.…”

Section: Discussionmentioning

confidence: 99%

Differential Effects of Metformin on Immune-Mediated and Androgen-Mediated Non-Cancer Skin Diseases in Diabetes Patients: A Retrospective Cohort Study

Tseng

2023

Dermatology

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Background: Metformin’s effects on non-cancer skin diseases are rarely investigated. Objective: To investigate immune-mediated (urticaria, allergic contact dermatitis and psoriasis) and androgen-mediated (acanthosis nigricans, hidradenitis suppurativa and acne) skin diseases associated with metformin use. Methods: Metformin initiators (n=234,585) and non-metformin initiators (n=125,921) within the initial 12 months of antidiabetic drugs prescription during 1999-2009 were followed up until December 31, 2011. Cox regression weighted for propensity score was used to estimate hazard ratios for metformin initiators versus non-metformin initiators in intention-to-treat (ITT) and per-protocol (PP) analyses. Results: For immune-mediated skin diseases, hazard ratios were 0.930 (95% confidence interval: 0.920–0.940) and 0.930 (0.918–0.943) in ITT and PP analyses, respectively; and the hazard ratios for each specific outcome were all significantly below unity. For androgen-mediated skin diseases, the ITT and PP hazard ratios were 1.110 (1.060–1.162) and 0.990 (0.935–1.048), respectively; and all hazard ratios were not significant for each specific outcome except for acne in the ITT analysis (hazard ratio: 1.116, 95% confidence interval: 1.064–1.170). Conclusion: Metformin use is associated with a significantly lower risk of immune-mediated skin diseases but a lack of preventive effect on androgen-mediated skin diseases.

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“…25 This is the only currently FDA-approved device for melanoma detection but is restricted to use by dermatologists, given a lack of validation/approval in a primary care context and restrictions for usage on benign lesions such as seborrheic keratoses. 26,27 Therefore, additional evidence for use of efficacious devices in clinical settings outside of dermatologic practice is both high yield for primary care, the front line of most suspicious lesion presentation, and in contribution to the high degree of trust needed for regulatory recognition and implementation of such devices. The present study contributes evidence for a diagnostically valid device that can improve detection in this context and meaningfully impact behavior.…”

Section: Discussionmentioning

confidence: 99%

Clinical Utility of an AI-powered, Handheld Elastic Scattering Spectroscopy Device on the Diagnosis and Management of Skin Cancer by Primary Care Physicians

Jaklitsch,

Thames,

de Campos Silva

et al. 2023

J Prim Care Community Health

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Background: Patients with lesions suspicious for skin cancer often present to primary care physicians (PCPs), who may have limited training in skin cancer diagnosis. Objective: To measure the impact of an adjunctive handheld device for PCPs that employs elastic scattering spectroscopy (ESS) on the diagnosis and management of skin cancer. Methods: Fifty-seven PCPs evaluated 50 clinical images of skin lesions (25 malignant and 25 benign), first without and then with knowledge of the handheld ESS device output, and in each case indicated if a lesion was likely to be benign or malignant. Results: The diagnostic sensitivity of the PCPs with and without the use of the ESS device was 88% (95% CI, 84%-92%) and 67% (95% CI, 62%-72%), respectively ( P < .0001). In contrast, no significant difference was observed in the diagnostic specificity. The management sensitivity of the physicians with and without the use of the ESS device was 94% (95% CI, 91%-96%) and 81% (95% CI, 77%-85%), respectively ( P = .0009). Similarly, no significant difference was observed in the management specificity. Conclusion: The use of the ESS device may have the potential to help improve skin cancer diagnosis and confidence in management decision-making in a primary care setting.

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Artificial intelligence in the detection of skin cancer

Cited by 34 publications

References 21 publications

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

Differential Effects of Metformin on Immune-Mediated and Androgen-Mediated Non-Cancer Skin Diseases in Diabetes Patients: A Retrospective Cohort Study

Clinical Utility of an AI-powered, Handheld Elastic Scattering Spectroscopy Device on the Diagnosis and Management of Skin Cancer by Primary Care Physicians

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