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Dr. Diala Haykal
Dermatology

Machine Learning and Medical Training

By Dr. Diala Haykal

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"AI does not redefine our profession, it reveals its true finesse."

Machine Learning and Medical Training: How AI is Revolutionizing Dermatological Diagnosis Education

Dermatology is a specialty where the clinical eye is king. But today, that eye is enhanced by a new tool: artificial intelligence. More than just a diagnostic aid, AI is becoming a full-fledged educational partner, redefining learning methods in dermatology.

Thousands of Images to Train an Expert Eye

Machine learning, particularly through convolutional neural networks (CNNs), enables algorithms to “see” and classify dermatological conditions with a precision that sometimes rivals seasoned specialists. These models are trained on vast databases, including images of benign, malignant, inflammatory, pigmented lesions, and more.

For students or young practitioners, these tools offer an unprecedented opportunity: access to a diversity of clinical cases they might never encounter during traditional training. AI acts as an interactive tutor, providing immediate feedback, error correction, and a dynamic learning curve.

An Immersive and Evolving Pedagogy

Some educational platforms already integrate AI into their interfaces, offering smart diagnostic quizzes, simulations of complex cases, and even automated differential diagnosis analyses. The future doctor learns to recognize subtle signs, prioritize hypotheses, and refine clinical reasoning.

These adaptive learning environments rely on positive reinforcement, error analysis, and personalized progression. They are especially valuable for complex visual diagnoses, where experience often makes the difference.

Toward Integration into University Curricula

Many universities and medical schools worldwide are starting to integrate these tools into their dermatology programs, not to replace traditional teaching but to enhance it. The goal: to train more confident, agile practitioners, better equipped to face the diversity of clinical presentations, including those encountered in telemedicine.

A Springboard for Clinical Practice, In-Person and Remote Expertise

Beyond initial training, these technologies play a key role in continuing professional development, especially where access to specialized dermatology expertise is limited. AI thus becomes a valuable tele-expertise tool, allowing general practitioners or isolated clinicians to validate their hypotheses while continuing to learn. This dynamic creates a novel bridge between training, daily practice, and continuous care improvement.

Conclusion

Artificial intelligence does not replace medical rigor. It structures it, stimulates it, teaches it. In a world where skin diseases evolve with lifestyles and environment, AI becomes an essential educational extension. It sharpens vision and supports more informed decisions.

Références

  1. Haykal D, Garibyan L, Flament F, Cartier H. Hybrid cosmetic dermatology: AI generated horizon. Skin Res Technol. 2024;30(5):e13721. doi:10.1111/srt.13721
  2. Haykal D, Cartier H, Yi K, Wortsman X. The Transformative Potential of AI in Ultrasound for Facial Aesthetics. J Cosmet Dermatol. 2025;24(2):e16691. doi:10.1111/jocd.16691
  3. Haykal D, Cartier H, du Crest D, Galadari H, Landau M, Haddad A. What happens when simulations get real and cosmetic dermatology goes virtual?. J Cosmet Dermatol. 2023;22(10):2682-2684. doi:10.1111/jocd.15888
  4. Haykal D, Cartier H, Flament F. A Psychosocial Exploration of Augmented Reality and Virtual Reality Apps in Cosmetic Procedures. J Cosmet Dermatol. 2024;23(12):3863-3870. doi:10.1111/jocd.16612
  5. Haykal D. Unleashing the Power of Biosensors and Artificial Intelligence in Dermatology. Aesthet Surg J Open Forum. 2024;6:ojae030. Published 2024 Apr 24. doi:10.1093/asjof/ojae030

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