Artificial Intelligence in Dermatopathology: a systematic review.

Abstract

Background: Medical research, driven by advancing technologies like Artificial Intelligence (AI), is transforming healthcare. Dermatology, known for its visual nature, benefits from AI, especially in dermatopathology with digitized slides. This review explores into AI's role, challenges, opportunities, and future potential in enhancing dermatopathological diagnosis and care.

Materials and methodology: Adhering to PRISMA and Cochrane Handbook standards, this systematic review explored AI's function in dermatopathology. It employed an interdisciplinary method, encompassing diverse study types and comprehensive database searches. Inclusion criteria encompassed peer-reviewed articles from 2000 to 2023, with a focus on practical AI use in dermatopathology.

Results: Numerous studies have investigated AI's potential in dermatopathology. We reviewed 112 papers. Notable applications include AI classifying histopathological images of nevi and melanomas, although challenges exist regarding subtype differentiation and generalizability. AI achieved high accuracy in melanoma recognition from formalin-fixed paraffin-embedded samples but faced limitations due to small datasets. Deep learning algorithms showed diagnostic accuracy for specific skin conditions, but challenges persisted, such as small sample sizes and the need for prospective validation.

Conclusion: This systematic review underscores AI's potential in enhancing dermatopathology for better diagnosis and patient care. Addressing challenges like limited datasets and potential biases is essential. Future directions involve expanding datasets, conducting validation studies, promoting interdisciplinary collaboration, and creating patient-centred AI tools to enhance dermatopathology's accuracy, accessibility, and patient-focused care.