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How does the implementation of artificial intelligence impact the accuracy of skin lesion diagnosis in hospitals?

Generalizability theory

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Teledermatology

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The implementation of artificial intelligence (AI) in hospitals significantly impacts the accuracy of skin lesion diagnosis. AI systems like AIaMD have shown high sensitivity in detecting melanoma, skin cancer, and premalignant lesions. Additionally, AI models trained using advanced techniques like mixup and test-time augmentation have demonstrated the ability to classify various skin lesion categories accurately. Furthermore, innovative frameworks like federated contrastive learning and intelligent skin lesion diagnosis schemes have been proposed to enhance diagnostic model generalizability and improve performance. Despite challenges like data scarcity, AI-driven approaches, including unsupervised domain adaptation methods, have shown effectiveness in improving diagnostic accuracy, especially in binary classification tasks. Overall, AI integration in skin lesion diagnosis pathways holds great promise for enhancing diagnostic accuracy and optimizing patient outcomes.

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Open access•Journal Article•DOI BT09 Clinical performance of an artificial intelligence-based medical device deployed within an urgent suspected skin cancer pathway R. Jenkins, Christopher Felix Brewer, Dilraj Kalsi, Daniel J. Mullarkey - Show less +3 more 01 Jun 2023-British Journal of Dermatology	Implementation of artificial intelligence in skin lesion diagnosis significantly improves accuracy by enhancing sensitivity for skin cancer detection, increasing specificity for benign lesions, and improving the conversion rate of urgent suspected skin cancer referrals.
Journal Article•DOI Skin Lesion Diagnosis Using Convolutional Neural Networks Yongmin Li 18 May 2023-arXiv.org	The implementation of Convolutional Neural Networks in AI enhances skin lesion diagnosis accuracy, potentially providing equal or superior results compared to dermatologists, especially in underserved regions.
Open access•Journal Article•DOI Skin Lesion Intelligent Diagnosis in Edge Computing Networks: A Federated Contrastive Learning Approach Yanhang Shi, Xue Li, Si Guang Chen - Show less +2 more 01 May 2023-ACM Transactions on Intelligent Systems and Technology	The implementation of federated contrastive learning in edge computing networks enhances skin lesion diagnosis accuracy by breaking data silos and improving model generalizability to unseen data.
Open access•Posted Content•DOI Skin Lesion Diagnosis Using Convolutional Neural Networks 18 May 2023	The implementation of Convolutional Neural Networks in skin lesion diagnosis enhances accuracy, potentially providing results comparable to or better than those of dermatologists, especially in underserved regions.

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