Establishing Artificial Intelligence Compliance Systems in Dermatology Private Practice: Review Article

Liana Ly; Patel Bianca; Serfaty Sophie A; MuhammadPoe Karima; Patel Tej; Sollitto Catherine; Frasier Kelly

doi:10.58372/2835-6276.1380

Authors

Liana Ly Touro University California College of Osteopathic Medicine
Patel Bianca
Serfaty Sophie A
MuhammadPoe Karima
Patel Tej
Sollitto Catherine
Frasier Kelly

DOI:

https://doi.org/10.58372/2835-6276.1380

Keywords:

artificial intelligence, compliance system, cybersecurity, dermatology

Abstract

The integration of artificial intelligence (AI) tools into dermatology private practice introduces new dimensions of diagnostic support, workflow efficiency, and patient engagement, yet also demands rigorous compliance infrastructure to mitigate legal, ethical, and operational risk. Establishing an AI compliance system within a private dermatologic setting requires the development of a multidisciplinary oversight process encompassing algorithm validation, data governance, cybersecurity, and adherence to emerging regulatory standards such as the EU AI Act, U.S. FDA guidelines on Software as a Medical Device (SaMD), and state-level privacy legislation like the California Consumer Privacy Act (CCPA). Risk stratification of AI tools used for triaging skin lesions, generating differential diagnoses, or automating clinical documentation must be based on their functional classification (assistive vs. autonomous), intended use, and data origin. Practices must implement thorough consent protocols for the use of patient data in AI training or real-time decision support, ensuring transparency in model limitations, explainability, and the delineation of clinical accountability. Vendor contracts should include audit rights, data use limitations, and indemnification clauses to safeguard against liability stemming from algorithmic error or patient harm. Internal policies must define clear documentation standards for AI-assisted clinical decisions and incorporate human-in-the-loop review mechanisms to prevent overreliance on algorithmic outputs. Periodic auditing of AI tool performance, bias monitoring across skin types and demographic variables, and alignment with dermatology-specific clinical quality measures are necessary to ensure regulatory conformity and equitable care delivery. Staff training programs should incorporate technical tool use, regulatory literacy, ethical implications, and protocol escalation pathways in cases of AI malfunction or disagreement with clinical judgment. A well-structured AI compliance system in dermatology private practice serves not only to fulfill legal obligations, but to uphold standards of clinical safety, transparency, and patient trust as algorithmic technologies become integrated into the routine administration of dermatologic care.

References

Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., Al Yami, M. S., Al Harbi, S., & Albekairy, A. M. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23(1), 689. https://doi.org/10.1186/s12909-023-04698-z

Koka, S. S.-A., & Burkhart, C. G. (2023). Artificial Intelligence in Dermatology: Current Uses, Shortfalls, and Potential Opportunities for Further Implementation in Diagnostics and Care. The Open Dermatology Journal, 17(1), e187437222304140. https://doi.org/10.2174/18743722-v17-e230505-2022-27

Omiye, J. A., Gui, H., Daneshjou, R., Cai, Z. R., & Muralidharan, V. (2023). Principles, applications, and future of artificial intelligence in dermatology. Frontiers in Medicine, 10, 1278232. https://doi.org/10.3389/fmed.2023.1278232

Wang, G., Meng, X., & Zhang, F. (2023). Past, present, and future of global research on artificial intelligence applications in dermatology: A bibliometric analysis. Medicine, 102(45), e35993. https://doi.org/10.1097/MD.0000000000035993

Wongvibulsin, S., Yan, M. J., Pahalyants, V., Murphy, W., Daneshjou, R., & Rotemberg, V. (2024). Current State of Dermatology Mobile Applications With Artificial Intelligence Features. JAMA Dermatology, 160(6), 646–650. https://doi.org/10.1001/jamadermatol.2024.0468

Nelson, C. A., Pachauri, S., Balk, R., Miller, J., Theunis, R., Ko, J. M., & Kovarik, C. L. (2021). Dermatologists’ Perspectives on Artificial Intelligence and Augmented Intelligence—A Cross-sectional Survey. JAMA Dermatology, 157(7), 871. https://doi.org/10.1001/jamadermatol.2021.1685

Goessinger, E. V., Niederfeilner, J., Cerminara, S., Maul, J., Kostner, L., Kunz, M., Huber, S., Koral, E., Habermacher, L., Sabato, G., Tadic, A., Zimmermann, C., Navarini, A., & Maul, L. V. (2024). Patient and dermatologists’ perspectives on augmented intelligence for melanoma screening: A prospective study. Journal of the European Academy of Dermatology and Venereology, 38(12), 2240–2249. https://doi.org/10.1111/jdv.19905

Nair, M., Svedberg, P., Larsson, I., & Nygren, J. M. (2024). A comprehensive overview of barriers and strategies for AI implementation in healthcare: Mixed-method design. PloS One, 19(8), e0305949. https://doi.org/10.1371/journal.pone.0305949

Haykal, D. (2025). Keep an Eye on AI: How Artificial Intelligence Is Redefining Patient Education in Dermatology. Journal of Cosmetic Dermatology, 24(5), e70225. https://doi.org/10.1111/jocd.70225

Vo, V., Chen, G., Aquino, Y. S. J., Carter, S. M., Do, Q. N., & Woode, M. E. (2023). Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis. Social Science & Medicine (1982), 338, 116357. https://doi.org/10.1016/j.socscimed.2023.116357

Van Kolfschooten, H., & Van Oirschot, J. (2024). The EU Artificial Intelligence Act (2024): Implications for healthcare. Health Policy, 149, 105152. https://doi.org/10.1016/j.healthpol.2024.105152

U.S. Food and Drug Administration. (2022). Global approach to software as a medical device. U.S. Department of Health and Human Services. Retrieved June 17, 2025, from https://www.fda.gov/medical-devices/software-medical-device-samd/global-approach-software-medical-device

Yom-Tov, E., & Ofran, Y. (2022). Implementation of data protection laws in the European Union and in California is associated with a move of clinical trials to countries with fewer data protections. Frontiers in Medicine, 9, 1051025. https://doi.org/10.3389/fmed.2022.1051025

Nahm, W. J., Sohail, N., Burshtein, J., Goldust, M., & Tsoukas, M. (2025). Artificial Intelligence in Dermatology: A Comprehensive Review of Approved Applications, Clinical Implementation, and Future Directions. International Journal of Dermatology, ijd.17847. https://doi.org/10.1111/ijd.17847

Kololgi, S. P., & Lahari, C. S. (2023). Harnessing the Power of Artificial Intelligence in Dermatology: A Comprehensive Commentary. Indian Journal of Dermatology, 68(6), 678–681. https://doi.org/10.4103/ijd.ijd_581_23

Goodman, K. W., Berner, E. S., Dente, M. A., Kaplan, B., Koppel, R., Rucker, D., Sands, D. Z., & Winkelstein, P. (2010). Challenges in ethics, safety, best practices, and oversight regarding HIT vendors, their customers, and patients: a report of an AMIA special task force. Journal of the American Medical Informatics Association, 18(1), 77–81. https://doi.org/10.1136/jamia.2010.008946

Cestonaro, C., Delicati, A., Marcante, B., Caenazzo, L., & Tozzo, P. (2023). Defining medical liability when artificial intelligence is applied on diagnostic algorithms: a systematic review. Frontiers in Medicine, 10. https://doi.org/10.3389/fmed.2023.1305756

Osnat, B. (2025). Patient perspectives on artificial intelligence in healthcare: A global scoping review of benefits, ethical concerns, and implementation strategies. International Journal of Medical Informatics, 203, 106007. https://doi.org/10.1016/j.ijmedinf.2025.106007

Chau, M., Rahman, M. G., & Debnath, T. (2025). From black box to clarity: Strategies for effective AI informed consent in healthcare. Artificial Intelligence in Medicine, 167, 103169. https://doi.org/10.1016/j.artmed.2025.103169

Ramesh, A., Kambhampati, C., Monson, J., & Drew, P. (2004). Artificial intelligence in medicine. Annals of The Royal College of Surgeons of England, 86(5), 334–338. https://doi.org/10.1308/147870804290

Denecke, K., & Baudoin, C. R. (2022). A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems. Frontiers in Medicine, 9, 795957. https://doi.org/10.3389/fmed.2022.795957

Liopyris, K., Gregoriou, S., Dias, J., & Stratigos, A. J. (2022). Artificial Intelligence in Dermatology: Challenges and Perspectives. Dermatology and Therapy, 12(12), 2637–2651. https://doi.org/10.1007/s13555-022-00833-8

Chan, S., Reddy, V., Myers, B., Thibodeaux, Q., Brownstone, N., & Liao, W. (2020). Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations. Dermatology and Therapy, 10(3), 365–386. https://doi.org/10.1007/s13555-020-00372-0

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056

Ma, Y., Song, Y., Balch, J. A., Ren, Y., Vellanki, D., Hu, Z., Brennan, M., Kolla, S., Guan, Z., Armfield, B., Ozrazgat-Baslanti, T., Rashidi, P., Loftus, T. J., Bihorac, A., & Shickel, B. (2024). Promoting AI Competencies for Medical Students: A Scoping Review on Frameworks, Programs, and Tools (No. arXiv:2407.18939). arXiv. https://doi.org/10.48550/arXiv.2407.18939

Association of American Medical Colleges. Ensure ethical and transparent use. (n.d.). https://www.aamc.org/about-us/mission-areas/medical-education/principles-ai-use/ensure-ethical-and-transparent-use

Pham, T. (2025). Ethical and legal considerations in healthcare AI: Innovation and policy for safe and fair use. Royal Society Open Science, 12(5), 241873. https://doi.org/10.1098/rsos.241873

Rony, M. K. K., Numan, S. M., Akter, K., Tushar, H., Debnath, M., Johra, F. T., Akter, F., Mondal, S., Das, M., Uddin, M. J., Begum, J., & Parvin, M. R. (2024). Nurses’ perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare. Heliyon, 10(17), e36702. https://doi.org/10.1016/j.heliyon.2024.e36702

Rincon, J., Pelletier, A. R., Gilliland, D., Wang, W., Wang, D., Sankar, B. S., Scott-Sheldon, L., Gebreab, S., Hersh, W., Rashidi, P., Baxter, S., Schulz, W., Ideker, T., Bensoussan, Y., Boutros, P. C., Bui, A. A. T., Walsh, C., Watson, K. E., & Ping, P. (2025). Bridge2AI: Building A Cross-disciplinary Curriculum Towards AI-Enhanced Biomedical and Clinical Care (No. arXiv:2505.14757). arXiv. https://doi.org/10.48550/arXiv.2505.14757

Nelson, J., Wills, A., & Owen, J. (2025). Architecture of an AI Medical Assistant: From Data Ingestion to Decision Support.

Crigger, E., & Khoury, C. (2019). Making Policy on Augmented Intelligence in Health Care. AMA Journal of Ethics, 21(2), E188-191. https://doi.org/10.1001/amajethics.2019.188

Brancaccio, G., Balato, A., Malvehy, J., Puig, S., Argenziano, G., & Kittler, H. (2024). Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check. Journal of Investigative Dermatology, 144(3), 492–499. https://doi.org/10.1016/j.jid.2023.10.004

Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N., Halpern, A., Janda, M., Lallas, A., Longo, C., Malvehy, J., Paoli, J., Puig, S., Rosendahl, C., Soyer, H. P., Zalaudek, I., & Kittler, H. (2020). Human-computer collaboration for skin cancer recognition. Nature Medicine, 26(8), 1229–1234. https://doi.org/10.1038/s41591-020-0942-0

Chanda, T., Hauser, K., Hobelsberger, S., Bucher, T.-C., Garcia, C. N., Wies, C., Kittler, H., Tschandl, P., Navarrete-Dechent, C., Podlipnik, S., Chousakos, E., Crnaric, I., Majstorovic, J., Alhajwan, L., Foreman, T., Peternel, S., Sarap, S., Özdemir, İ., Barnhill, R. L., … Brinker, T. J. (2024). Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma. Nature Communications, 15(1), 524. https://doi.org/10.1038/s41467-023-43095-4

Kompa, B., Snoek, J., & Beam, A. L. (2021). Second opinion needed: Communicating uncertainty in medical machine learning. Npj Digital Medicine, 4(1), 4. https://doi.org/10.1038/s41746-020-00367-3

Kakish, D. R. K., AlSamhori, J. F., Fajardo, A. N. R., Qaqish, L. N., Jaber, L. A., Abujudeh, R., Al‐Zuriqat, M. H. M., Mohammed, A. Y., & Nashwan, A. J. (2025). Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics. Dermatological Reviews, 6(1), e70018. https://doi.org/10.1002/der2.70018

Gwillim, E. C., Azzawi, S., & Aigen, A. R. (2024). Underserved populations and health equity in dermatology: Digital medicine and the role of artificial intelligence. Clinics in Dermatology, 42(5), 498–506. https://doi.org/10.1016/j.clindermatol.2024.06.021

Daneshjou, R., Barata, C., Betz-Stablein, B., Celebi, M. E., Codella, N., Combalia, M., Guitera, P., Gutman, D., Halpern, A., Helba, B., Kittler, H., Kose, K., Liopyris, K., Malvehy, J., Seog, H. S., Soyer, H. P., Tkaczyk, E. R., Tschandl, P., & Rotemberg, V. (2022). Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology: CLEAR Derm Consensus Guidelines From the International Skin Imaging Collaboration Artificial Intelligence Working Group. JAMA Dermatology, 158(1), 90. https://doi.org/10.1001/jamadermatol.2021.4915

Bottomley, D., & Thaldar, D. (2023). Liability for harm caused by AI in healthcare: An overview of the core legal concepts. Frontiers in Pharmacology, 14, 1297353. https://doi.org/10.3389/fphar.2023.1297353

Choudhury, A., & Asan, O. (2020). Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Medical Informatics, 8(7), e18599. https://doi.org/10.2196/18599

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: Review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236–1246. https://doi.org/10.1093/bib/bbx044