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REVIEW ARTICLE

Artificial intelligence in skin cancer diagnosis and prognosis: A comprehensive narrative review of current applications and future perspectives

Harpreet Singh1* ,  Rajdeep Singh2 ,  Pinki Kushwaha3 ,  Jatin Agarwal4 ,  Sagar Varshney1 ,  Arvind Kumar1 ,  Arun Kumar Mishra5 ,  Hitesh Chopra6 ,  Shivani Chopra7 ,  Tabarak Malik8,9*
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1 School of Pharmaceutical Sciences, Faculty of Pharmacy, IFTM University, Moradabad, Uttar Pradesh, India
2 Department of Computer Applications, School of Computer Science & Applications, IFTM University, Moradabad, Uttar Pradesh, India
3 Department of Pharmaceutical Chemistry, Moradabad Educational Trust, Group of Institutions, Faculty of Pharmacy, Moradabad, Uttar Pradesh, India
4 Department of Pharmaceutics, Moradabad Educational Trust, Group of Institutions, Faculty of Pharmacy, Moradabad, Uttar Pradesh, India
5 Sahu Onkar Saran School of Pharmacy, Faculty of Pharmacy, IFTM University, Moradabad, Uttar Pradesh, India
6 Centre for Research Impact & Outcome, Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
7 Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India
8 Department of Biomedical Sciences, Institute of Health, Jimma University, Jimma, Oromia Region, Ethiopia
9 Division of Research & Development, Lovely Professional University, Phagwara, Punjab, India
AIH, 025470106 https://doi.org/10.36922/AIH025470106
Received: 21 November 2025 | Revised: 20 February 2026 | Accepted: 4 March 2026 | Published online: 5 May 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Skin cancer is among the most prevalent malignancies worldwide, and early, accurate diagnosis is crucial for improving patient outcomes. Recent advances in artificial intelligence (AI), particularly machine learning and deep learning, have shown substantial potential to enhance skin cancer detection, classification, and prognostic assessment. This review provides a comprehensive synthesis of current AI-based approaches for melanoma and non-melanoma skin cancers, highlighting methodological innovations and clinical applications. Literature from 2015 to 2025 was screened from PubMed/MEDLINE, Scopus, IEEE Xplore, and ScienceDirect, focusing on peer-reviewed studies reporting AI-driven diagnostic, classification, or prognostic outcomes. Deep learning models, especially convolutional neural networks, demonstrated high diagnostic performance in image-based skin cancer detection, often comparable to experienced dermatologists. AI has also shown promise in lesion segmentation, risk stratification, and prognostic modeling. However, challenges remain, including class imbalance, underrepresentation of darker skin tones, limited external validation, algorithmic opacity, and integration into clinical workflows. To enable broader clinical adoption, future research should prioritize diverse multicenter datasets, explainable AI systems, multimodal data integration, and prospective clinical validation studies. Overall, AI technologies offer significant potential to improve the accuracy and efficiency of skin cancer diagnosis and prognosis, but their translation into routine dermatological practice requires careful attention to reliability, equity, and interpretability.

Graphical abstract
Keywords
Artificial intelligence
Machine learning
Deep learning
Skin cancer
Melanoma
Diagnosis
Prognosis
Dermatology
Funding
None.
Conflict of interest
The authors declare no conflict of interest.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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