Kailas Vodrahalli is a PhD student in electrical engineering at Stanford University.

Kailas Vodrahalli

Artificial Intelligence in Dermatology: Emerging Insights and Diverging Perspectives

AI in Dermatology: Emerging Insights and Diverging Perspectives

Artificial intelligence (AI) technology may be transforming the field of 

, providing clinicians and patients with faster diagnoses, greater diagnostic accuracy, and expanded access to health care. The topic is rapidly becoming one of the most debated in medicine, driven by a heated clash between supporters and skeptics over whether it will truly benefit patients.

A plethora of recent studies have been published regarding new types of AI technology and their use in dermatology. In this 

 feature, 3 speakers were interviewed regarding their contributions to recent AI-related studies in dermatology as well as their more general impressions of the heightened interest in further implementing AI in medicine.

The feature included an interview with Harald Kittler, MD, a professor of dermatology for the Medical University of Vienna, whose international research team had evaluated a learning method in which the accuracy of AI results incorporated human decision-making criteria. The study, which demonstrated a 12% improvement in correct skin cancer diagnoses made by dermatologists, was published in Nature Medicine.

An interview was also conducted with Kailas Vodrahalli, a PhD student in electrical engineering at Stanford University. Vodrahilli touched upon his own team’s research, involving an evaluation of an AI decision support tool using a machine learning algorithm and determining whether the tool could improve the telemedicine-submitted image quality by providing real-time responses to patients. The study concluded that the AI decision support tool’s use, along with their machine learning algorithm, was linked to improvements in the quality of skin disease photographs.

This feature also included an interview with Shuai (Steve) Xu, MD, MSc, a dermatologist, physician engineer, Ruth K. Freinkel MD professor at the Feinberg School of Medicine, and CEO of Sibel Health. His research pointed to the efficacy of the use of haptic feedback on an 

, with occurrences of nocturnal scratching decreasing among adult patients suffering from mild atopic dermatitis.

Kittler, Xu, and Vodrahilli addressed their own findings and discuss the general discourse surrounding AI’s use in dermatology. Each expert’s unique insights, given their differing specialties and backgrounds, allow for an update on new AI research and any current progress AI technology has had in reshaping skin disease detection, clinical decision-making enhancement, and pushing the boundaries of dermatological care.

Check out the feature video posted above to discover more about the findings uncovered by these experts.

Kittler received nonfinancial support from Derma Medical Systems, Fotofinder and Heine, and speaker fees from Fotofinder. Vodrahalli reported receiving grants from the National Science Foundation (NSF) Graduate Research Fellowships Program during the conduct of the study and had a patent for Automated Clinical Image Quality Assessment pending for the work in this study. Xu reported grants from Maruho Co Ltd during the conduct of his study, serves on the advisory board for AbbVie outside the submitted work, and has a patent for WO2023183258 pending.

Barata C, Rotemberg V, Codella NCF, Tschandl P, Rinner C, Akay BN, Apalla Z, Argenziano G, Halpern A, Lallas A, Longo C, Malvehy J, Puig S, Rosendahl C, Soyer HP, Zalaudek I, Kittler H. A reinforcement learning model for AI-based decision support in skin cancer. Nat Med. 2023 Aug;29(8):1941-1946. doi: 10.1038/s41591-023-02475-5. Epub 2023 Jul 27. PMID: 37501017; PMCID: PMC10427421.

Vodrahalli K, Ko J, Chiou AS, Novoa R, Abid A, Phung M, Yekrang K, Petrone P, Zou J, Daneshjou R. Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality. JAMA Dermatol. 2023 May 1;159(5):496-503. doi: 10.1001/jamadermatol.2023.0091. PMID: 36920380; PMCID: PMC10018405.

Yang AF, Patel S, Chun KS, et al. Artificial Intelligence–Enabled Wearable Devices and Nocturnal Scratching in Mild Atopic Dermatitis. JAMA Dermatol. Published online February 05, 2025. doi:10.1001/jamadermatol.2024.5697.

Videos

In this feature, 3 experts highlight their own research on new technological advances and their views on the benefits and drawbacks of artificial intelligence in dermatology.

Kailas Vodrahalli

Articles