Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis.

The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6-80.1) and specificity was 81.5% (95% CI 73.9-87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4-86.5) and specificity was 86.1% (95% CI 79.2-90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.

Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.

The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of explainable artificial intelligence. Specifically, we leveraged the expertise of dermatologists for the clinical task of differentiating melanomas from melanoma 'lookalikes' on the basis of dermoscopic and clinical images of the skin, and the power of generative models to render 'counterfactual' images to understand the 'reasoning' processes of five medical-image classifiers. By altering image attributes to produce analogous images that elicit a different prediction by the classifiers, and by asking physicians to identify medically meaningful features in the images, the counterfactual images revealed that the classifiers rely both on features used by human dermatologists, such as lesional pigmentation patterns, and on undesirable features, such as background skin texture and colour balance. The framework can be applied to any specialized medical domain to make the powerful inference processes of machine-learning models medically understandable.

Principles, applications, and future of artificial intelligence in dermatology.

This paper provides an overview of artificial-intelligence (AI), as applied to dermatology. We focus our discussion on methodology, AI applications for various skin diseases, limitations, and future opportunities. We review how the current image-based models are being implemented in dermatology across disease subsets, and highlight the challenges facing widespread adoption. Additionally, we discuss how the future of AI in dermatology might evolve and the emerging paradigm of large language, and multi-modal models to emphasize the importance of developing responsible, fair, and equitable models in dermatology.

Risk of Multiple Primary Cancers in Patients With Merkel Cell Carcinoma: A SEER-Based Analysis.

Importance: The risk of subsequent primary cancers after a diagnosis of cutaneous Merkel cell carcinoma (MCC) is not well established. Objective: To evaluate the risk of subsequent primary cancers after the diagnosis of a first primary cutaneous MCC. Design, Setting, and Participants: This cohort study analyzed data from 17 registries of the Surveillance, Epidemiology, and End Results (SEER) Program from January 1, 2000, to December 31, 2019. In all, 6146 patients diagnosed with a first primary cutaneous MCC were identified. Main Outcomes and Measures: The primary outcome was the relative and absolute risks of subsequent primary cancers after the diagnosis of a first primary MCC, which were calculated using the standardized incidence ratio (SIR; ratio of observed to expected cases of subsequent cancer) and the excess risk (difference between observed and expected cases of subsequent cancer divided by the person-years at risk), respectively. Data were analyzed between January 1, 2000, and December 31, 2019. Results: Of 6146 patients with a first primary MCC diagnosed at a median (IQR) age of 76 (66-83) years, 3713 (60.4%) were men, and the predominant race and ethnicity was non-Hispanic White (5491 individuals [89.3%]). Of these patients, 725 (11.8%) developed subsequent primary cancers, with an SIR of 1.28 (95% CI, 1.19-1.38) and excess risk of 57.25 per 10 000 person-years. For solid tumors after MCC, risk was elevated for cutaneous melanoma (SIR, 2.36 [95% CI, 1.85-2.97]; excess risk, 15.27 per 10 000 person-years) and papillary thyroid carcinoma (SIR, 5.26 [95% CI, 3.25-8.04]; excess risk, 6.16 per 10 000 person-years). For hematologic cancers after MCC, risk was increased for non-Hodgkin lymphoma (SIR, 2.62 [95% CI, 2.04-3.32]; excess risk, 15.48 per 10 000 person-years). Conclusions and Relevance: This cohort study found that patients with MCC had an increased risk of subsequently developing solid and hematologic cancers. This increased risk may be associated with increased surveillance, treatment-related factors, or shared etiologies of the other cancers with MCC. Further studies exploring possible common etiological factors shared between MCC and other primary cancers are warranted.

A Social Media-Based Public Health Campaign to Reduce Indoor Tanning in High-Risk Populations.

Introduction: Indoor tanning beds cause more than 450,000 new skin cancers each year, yet their use remains common, with a global indoor tanning prevalence of 10.4%. Social media provides an opportunity for cost-effective, targeted public health messaging. We sought to direct Instagram users at high risk of indoor tanning to accurate health information about the risks of indoor tanning and to reduce indoor tanning bed use. Methods: We disseminated a public health campaign on Instagram on April 6-27, 2022 with 34 video and still-image advertisements. We had 2 target audiences at high risk of indoor tanning: women aged 18-30 years in Kentucky, Nebraska, Ohio, or Tennessee interested in indoor tanning and men aged 18-45 years in California interested in indoor tanning. To evaluate the impact of the campaign, we tracked online metrics, including website visits, and conducted an interrupted time-series analysis of foot traffic data in our target states for all tanning salons documented on SafeGraph from January 1, 2018 to 3 months after the campaign. Results: Our indoor tanning health information advertisements appeared on Instagram feeds 9.1 million times, reaching 1.06 million individuals. We received 7,004 views of our indoor tanning health information landing page (Average Time on Page of 56 seconds). We did not identify a significant impact on foot traffic data on tanning salons. Conclusions: We show the successful use of social media advertising to direct high-risk groups to online health information about indoor tanning. Future research quantifying tanning visits before and after indoor tanning interventions is needed to guide future public health efforts.

Incidence Trends of Primary Cutaneous T-Cell Lymphoma in the US From 2000 to 2018: A SEER Population Data Analysis.

Using SEER database data, this cohort study analyzed cutaneous T-cell lymphoma incidence by tumor subtype, sex, age, race and ethnicity, socioeconomic status, and geography.

Toxic epidermal necrolysis associated with pembrolizumab.

INTRODUCTION: Stevens-Johnson syndrome and toxic epidermal necrolysis are severe cutaneous drug eruptions characterized by epidermal detachment. Pembrolizumab is a monoclonal antibody that binds to the programmed death-1 receptor, and it has been associated with numerous cutaneous adverse side-effects, including Stevens-Johnson syndrome. CASE REPORT: We describe a 63-year-old male with metastatic lung adenocarcinoma who developed a rapidly progressing maculopapular rash three days after a first dose of pembrolizumab. On day 16, the rash affected more than 80% of body surface area with detachment of large sheets of necrolytic epidermis in 30-40% of body surface area. However, the patient only presented with mild mucosal involvement. Histopathologic examination of a skin biopsy showed a subepidermal blister with overlying prominent full thickness epidermal keratinocytic necrosis and a superficial perivascular infiltrate of lymphocytes. A toxic epidermal necrolysis secondary to pembrolizumab was then diagnosed. Management and outcome: In addition to supportive cares, the patient received corticosteroids and cyclosporine. The patient responded rapidly to the immunosuppressant therapy, and nearly complete re-epithelialization was achieved 24 days after the start of the reaction. DISCUSSION: In our review of the literature, 15 other cases of Stevens-Johnson syndrome/toxic epidermal necrolysis were reported with programmed death-1/programmed cell death ligand-1 inhibitors. To our knowledge, this is the first case of toxic epidermal necrolysis secondary to pembrolizumab published in the literature. The American Society of Clinical Oncology guidelines suggest that cyclosporine, in addition to corticosteroids, be initiated when toxic epidermal necrolysis is suspected. Clinicians should be aware of this rare dermatological emergency with the increasing use of pembrolizumab in oncology.