Artificial intelligence scribing in dermatology
DOI:
https://doi.org/10.18203/issn.2455-4529.IntJResDermatol20254131Keywords:
Artificial intelligence, Dermatology, AI scribingAbstract
Artificial intelligence (AI) scribing tools are transforming clinical documentation by automating note-taking through speech recognition and natural language processing. In dermatology, AI scribes offer the potential to improve efficiency, reduce physician burnout, and enhance the quality of patient care with diagnostic integration and precise visual and descriptive documentation. However, challenges remain in the form of transcription errors, integration with electronic health records, cost barriers, and concerns over data privacy. Additionally, dermatology-specific AI scribes are significantly under-researched, with only one early pilot study demonstrating promising benefits. The successful adoption of AI scribes in dermatology depends on refining language models, ensuring regulatory compliance, and tailoring systems to meet specialty-specific needs such as high-quality image documentation and description. Overall, AI scribing represents a valuable augmentative tool with the potential to reshape dermatologic care when implemented thoughtfully and ethically.
Metrics
References
Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020;92(4):807-12. DOI: https://doi.org/10.1016/j.gie.2020.06.040
Coiera E, Kocaballi B, Halamka J, Laranjo L. The digital scribe. NPJ Digit Med. 2018;1:58. DOI: https://doi.org/10.1038/s41746-018-0066-9
Falcetta FS, de Almeida FK, Lemos JCS, Goldim JR, da Costa CA. Automatic documentation of professional health interactions: A systematic review. Artif Intell Med. 2023;137:102487. DOI: https://doi.org/10.1016/j.artmed.2023.102487
Quiroz JC, Laranjo L, Kocaballi AB, Berkovsky S, Rezazadegan D, Coiera E. Challenges of developing a digital scribe to reduce clinical documentation burden. Npj Digit Med. 2019;2(1):114. DOI: https://doi.org/10.1038/s41746-019-0190-1
van Buchem MM, Boosman H, Bauer MP, Kant IMJ, Cammel SA, Steyerberg EW. The digital scribe in clinical practice: a scoping review and research agenda. NPJ Digit Med. 2021;4(1):57. DOI: https://doi.org/10.1038/s41746-021-00432-5
Nahm WJ, Sohail N, Burshtein J, Goldust M, Tsoukas M. Artificial Intelligence in Dermatology: A Comprehensive Review of Approved Applications, Clinical Implementation, and Future Directions. Int J Dermatol. 2025;19:17847. DOI: https://doi.org/10.1111/ijd.17847
Kunze KN, Bepple J, Bedi A, Ramkumar PN, Pean CA. Commercial Products Using Generative Artificial Intelligence Include Ambient Scribes, Automated Documentation and Scheduling, Revenue Cycle Management, Patient Engagement and Education, and Prior Authorization Platforms. Arthrosc J Arthrosc Relat Surg Off Publ Arthrosc Assoc N Am Int Arthrosc Assoc. 2025;25:4. DOI: https://doi.org/10.1016/j.arthro.2025.05.021
Klufas T, Zedek M, Ajmani A, Zhou AE, Grant-Kels JM. The good, the bad, and the ugly: Ethical considerations regarding artificial intelligence assistance in administrative physician tasks. Clin Dermatol. 2025;43(3):416-9. DOI: https://doi.org/10.1016/j.clindermatol.2025.02.004
Cao DY, Silkey JR, Decker MC, Wanat KA. Artificial intelligence-driven digital scribes in clinical documentation: Pilot study assessing the impact on dermatologist workflow and patient encounters. JAAD Int. 2024;15:149-51. DOI: https://doi.org/10.1016/j.jdin.2024.02.009
Sun QW, Miller J, Hull SC. Charting the ethical landscape of generative AI-augmented clinical documentation. J Med Ethics. 2025;27:110656. DOI: https://doi.org/10.1136/jme-2024-110656
Lee C, Britto S, Diwan K. Evaluating the Impact of Artificial Intelligence (AI) on Clinical Documentation Efficiency and Accuracy Across Clinical Settings: A Scoping Review. Cureus. 2024;16(11):e73994. DOI: https://doi.org/10.7759/cureus.73994
Toscano F, O’Donnell E, Broderick JE. How Physicians Spend Their Work Time: an Ecological Momentary Assessment. J Gen Intern Med. 2020;35(11):3166-72. DOI: https://doi.org/10.1007/s11606-020-06087-4
Budd J. Burnout Related to Electronic Health Record Use in Primary Care. J Prim Care Community Health. 2023;14:21501319231166921. DOI: https://doi.org/10.1177/21501319231166921
Mess SA, Mackey AJ, Yarowsky DE. Artificial Intelligence Scribe and Large Language Model Technology in Healthcare Documentation: Advantages, Limitations, and Recommendations. Plast Reconstr Surg Glob Open. 2025;13(1):e6450. DOI: https://doi.org/10.1097/GOX.0000000000006450
Biro J, Handley JL, Cobb NK, Kottamasu V, Collins J, Krevat S, et al. Accuracy and Safety of AI-Enabled Scribe Technology: Instrument Validation Study. J Med Internet Res. 2025;27:e64993. DOI: https://doi.org/10.2196/64993
Grzybowski A, Jin K, Wu H. Challenges of artificial intelligence in medicine and dermatology. Clin Dermatol. 2024;42(3):210-5. DOI: https://doi.org/10.1016/j.clindermatol.2023.12.013
Salinas MP, Sepúlveda J, Hidalgo L, Peirano D, Morel M, Uribe P, et al. A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis. NPJ Digit Med. 2024;7(1):125. DOI: https://doi.org/10.1038/s41746-024-01103-x
Liu Y, Primiero CA, Kulkarni V, Soyer HP, Betz-Stablein B. Artificial Intelligence for the Classification of Pigmented Skin Lesions in Populations with Skin of Color: A Systematic Review. Dermatol Basel Switz. 2023;239(4):499-513. DOI: https://doi.org/10.1159/000530225
Gomez Rossi J, Rojas-Perilla N, Krois J, Schwendicke F. Cost-effectiveness of Artificial Intelligence as a Decision-Support System Applied to the Detection and Grading of Melanoma, Dental Caries, and Diabetic Retinopathy. JAMA Netw Open. 2022;5(3):e220269. DOI: https://doi.org/10.1001/jamanetworkopen.2022.0269