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There has been a significant rise in skin cancer incidence during the last three decades and the waiting time for skin lesion assessment in both the NHS and private sectors in the UK has increased significantly. Therefore, to reduce waiting time and to make a faster decision, there is a need to develop automated methods that can be used to classify whether a skin lesion is suspicious or non-suspicious during teledermatology triage. In this study, we propose an AI framework that uses patient metadata together with image data to classify skin lesions into suspicious or non-suspicious categories. To evaluate our proposed approach, we collected 79,246 skin lesion images along with their 22 meta-features such as lesion size, lesion colour, lesion shape, patient age, and gender from 19,295 patients who attended a network of private skin cancer diagnostic centres across the UK. We developed three separate models for skin lesion classification: (1) an AI model using only metadata that achieved 85.24 ± 2.20% sensitivity and 61.12 ± 0.90% specificity; (2) an AI model using only images that achieved 99.72 ± 1.35% sensitivity and 63.22 ± 3.11% specificity; and (3) a fused model based on both metadata and images that achieved 99.66 ± 0.28% sensitivity and 74.45 ± 0.80% specificity. The decisions of the developed AI models were then fused through a majority voting technique, which achieved a sensitivity of 99.50 ± 1.18% and a specificity of 82.72 ± 1.64%, significantly outperforming the state-of-the-art methods that rely solely on image data. Furthermore, we add a post-processing step to explain AI model decisions by implementing a soft-attention module that provides essential explainability and supports healthcare professionals in informed decision-making. The developed AI framework has great potential for the detection of suspicious skin lesions. With a reduction in patient referrals for possible biopsies, waiting times for skin cancer diagnosis and treatment will be shortened, resulting in improved outcomes.