Artificial intelligence powered mobile health apps for skin cancer detection: current challenges and a systems thinking approach for improved public health outcomes in low- and middle-income countries

Abstract

Artificial intelligence (AI) powered mobile health (mHealth) apps are emerging as vital self-triage tools for skin cancer detection. By utilizing smartphone cameras, these apps analyze skin lesions to assess the risk and provide tailored care recommendations, ranging from self-care guidance to directing users to appropriate healthcare providers. While this positively impacts Sustainable Development Goals 3, the rapid proliferation of these apps introduces significant challenges. A persistent digital divide, stratified by gender, geography, income, and education, limits widespread adoption. It is further exacerbated by varying levels of digital literacy and patient anxieties. The unregulated nature of commercial app stores poses diagnostic risks. At the same time, limited training data for AI models exposes individuals with underrepresented skin types to significant diagnostic errors. Increased self-diagnosis leads to increased downstream care pressures, overwhelming dermatology and pathology services in LMICs. This review highlights the increasing incidence of skin cancer and discusses the risk-benefit profile of mHealth apps in diagnosis. It covers the multifaceted challenges confronting LMICs, including the evolving and fragmented regulatory landscape, while comparing them with those of high-income countries. Finally, we developed a causal loop diagram (CLD) to facilitate informed multistakeholder action for improving public health outcomes through AI-based mHealth apps. The CLD establishes the positive and negative associations of key variables across four pillars: data acquisition and quality, AI model development and validation, user experience and accessibility, and public health impact. We advocate for a multidisciplinary convergence among dermatological experts, AI scientists, app developers, and regulators, fostering international collaboration, knowledge sharing, best practices, and targeted capacity building to ensure equitable and accountable mHealth deployment in LMICs.

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