A STUDY OF AI-POWERED TOOLS DIAGNOSTIC PERFORMANCE FOR SKIN CANCER DETECTION AND EFFICIENCY OF AI ALGORITHMS IN DETECTING MELANOMA AND OTHER SKIN CANCERS
Main Article Content
Keywords
Artificial Intelligence, Melanoma, Skin Cancer, Diagnostic Accuracy, Deep Learning, Dermoscopy, Clinical Integration, Augmented IntelligenceArtificial Intelligence, Melanoma, Skin Cancer, Diagnostic Accuracy, Deep Learning, Dermoscopy, Clinical Integration, Augmented Intelligence
Abstract
Background
Skin cancer remains one of the most common malignancies worldwide, and early diagnosis is crucial for improving survival. Traditional diagnostic tools, such as dermoscopy and biopsy, are effective but resource-intensive and subject to interpretation variability. Artificial intelligence (AI) has emerged as a promising solution for enhancing accuracy, reducing clinician burden, and providing consistent diagnostic support.
Objectives
This systematic review aimed to evaluate the diagnostic performance of AI-powered tools for skin cancer detection compared to conventional methods. It also examines emerging AI trends, barriers to clinical adoption, and directions for future research.
Methodology
Following the PRISMA guidelines and using the PICO framework, relevant literature from 2019 to 2024 was reviewed via PubMed. Seven key studies comparing AI methods, including convolutional neural networks (CNNs) and hybrid models, with standard diagnostic techniques were analyzed. The metrics assessed included sensitivity, specificity, accuracy, and area under the curve (AUC).
Results
AI models showed excellent diagnostic performance, with sensitivities between 92.3% and 94% and specificities of up to 95.8%, reducing false positives. The accuracy exceeded 87.5%, with AI systems matching or surpassing non-expert clinicians. AUC values ranged from 0.92 to 0.96, indicating robust reliability. Hybrid and augmented AI approaches improved clinical performance by 5–10%.
Conclusion
AI-based diagnostic tools offer significant potential for improving skin cancer detection by enhancing accuracy and consistency. However, successful integration into clinical practice requires diverse datasets, real-world validation, and ethical safeguards.
References
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Amar Singh
MD, Associate Professor, Department Of Dermatology, Venereology And Leprosy, Shri Ram Murti Smarak Institute Of Medical Sciences, Bareilly, India
Sumeet Pal Saini
MD, Assistant Professor, Department Of Dermatology, Venereology And Leprosy, RIMT Medical College And Hospital, Mandi, Punjab
Pravesh Valecha
MD,Assistant Professor Department Of Dermatology, Venereology And Leprosy, American International Institute Of Medical Sciences, Udaipur, India
Dr Astha Pant
MD, Assistant Professor, Department Of Dermatology, Venereology And Leprosy, Government Doon Medical College, Dehradun, Uttrakhand