Artificial Intelligence and Machine Learning Transforming Dermatopathology with Diagnosis and Predictive Analytics

¹California Health Sciences University College of Osteopathic Medicine, Clovis, CA, USA

²Des Moines University College of Osteopathic Medicine, West Des Moines, IA, USA

³Albert Einstein College of Medicine, Bronx, NY, USA

⁴Rowan-Virtua School of Osteopathic Medicine, Stratford, NJ, USA

⁵Nova Southeastern University Dr. Kiran Patel College of Osteopathic Medicine, Fort Lauderdale, FL, USA

⁶Mercer University School of Medicine, Macon, GA, USA

⁷Department of Dermatology, Northwell Health, New Hyde Park, NY, USA

*Corresponding author: Kelly Frasier, DO, MS, Department of Dermatology, Northwell Health, New Hyde Park, NY, USA, Phone: 3105956882, Email: [email protected]

Received Date: February 09, 2025

Publication Date: February 19, 2025

Citation: Flores J, et al. (2025). Artificial Intelligence and Machine Learning Transforming Dermatopathology with Diagnosis and Predictive Analytics. Dermis. 5(1):29.

Copyright: Flores J, et al. © (2025).

ABSTRACT

The integration of artificial intelligence (AI) and machine learning (ML) into dermatopathology is revolutionizing the field by enhancing diagnostic precision, enabling advanced predictive analytics, and optimizing workflows. Dermatopathology traditionally relies on subjective interpretations of histopathological features, which can be prone to variability among pathologists; however, AI and ML algorithms, particularly those utilizing deep learning techniques and convolutional neural networks (CNNs), demonstrate exceptional capabilities in pattern recognition and data integration. Many algorithms, trained on large annotated datasets such as HAM10000 and ISIC, have achieved diagnostic accuracies surpassing 95% for conditions such as melanoma, basal cell carcinoma, and inflammatory dermatoses, with the added benefit of standardizing diagnostic practices by minimizing interobserver variability. Beyond diagnostics, AI-driven predictive analytics is emerging as a transformative tool, enabling prognostic assessments and personalized patient stratification by integrating histopathological, molecular, and clinical data. For example, ML models have been employed to correlate histopathological patterns with molecular markers, allowing risk stratification and identification of therapeutic targets, particularly for aggressive conditions like cutaneous melanoma. Additionally, AI-powered digital pathology platforms are streamlining workflows by automating routine tasks such as mitotic figure counting, margin assessment, and cellular quantification, which reduces diagnostic turnaround times and allows dermatopathologists to focus on complex cases, addressing growing demands in underserved regions. Despite these advancements, challenges remain in the form of algorithmic bias due to insufficiently diverse training datasets, regulatory barriers, and ethical concerns regarding data privacy and model interpretability. Addressing these challenges requires the development of comprehensive, explainable AI systems and the establishment of transparent frameworks for clinical integration. The transformative potential of AI and ML in dermatopathology is evident, with these technologies poised to redefine the field by delivering precision diagnostics, personalized care, and enhanced efficiency, ultimately advancing dermatopathology into a new era of evidence-based, patient-centered medicine.

Keywords: Artificial Intelligence, Machine Learning, Dermatopathology, Skin Cancer