Ensemble Deep Learning and LLM-Assisted Reporting for Automated Skin Lesion Diagnosis

• URL: http://arxiv.org/abs/2510.06260v1
• Date: Sun, 05 Oct 2025 08:07:33 GMT
• Title: Ensemble Deep Learning and LLM-Assisted Reporting for Automated Skin Lesion Diagnosis
• Authors: Sher Khan, Raz Muhammad, Adil Hussain, Muhammad Sajjad, Muhammad Rashid,
• Abstract summary: We introduce a unified framework that reimagines AI integration for dermatological diagnostics.<n>First, a purposefully heterogeneous ensemble of architecturally diverse convolutional neural networks provides complementary diagnostic perspectives.<n>Second, we embed large language model capabilities directly into the diagnostic workflow, transforming classification outputs into clinically meaningful assessments.
• Score: 2.9307254086347427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cutaneous malignancies demand early detection for favorable outcomes, yet current diagnostics suffer from inter-observer variability and access disparities. While AI shows promise, existing dermatological systems are limited by homogeneous architectures, dataset biases across skin tones, and fragmented approaches that treat natural language processing as separate post-hoc explanations rather than integral to clinical decision-making. We introduce a unified framework that fundamentally reimagines AI integration for dermatological diagnostics through two synergistic innovations. First, a purposefully heterogeneous ensemble of architecturally diverse convolutional neural networks provides complementary diagnostic perspectives, with an intrinsic uncertainty mechanism flagging discordant cases for specialist review -- mimicking clinical best practices. Second, we embed large language model capabilities directly into the diagnostic workflow, transforming classification outputs into clinically meaningful assessments that simultaneously fulfill medical documentation requirements and deliver patient-centered education. This seamless integration generates structured reports featuring precise lesion characterization, accessible diagnostic reasoning, and actionable monitoring guidance -- empowering patients to recognize early warning signs between visits. By addressing both diagnostic reliability and communication barriers within a single cohesive system, our approach bridges the critical translational gap that has prevented previous AI implementations from achieving clinical impact. The framework represents a significant advancement toward deployable dermatological AI that enhances diagnostic precision while actively supporting the continuum of care from initial detection through patient education, ultimately improving early intervention rates for skin lesions.

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