Artificial and human intelligence in Dermoscopy

The course is divided into five detailed units, each focusing on critical aspects of artificial and human intelligence in dermoscopy.

Unit 1: AI Diagnosis of Dermoscopic Images – Reality or Vision?
This unit introduces the development and application of artificial intelligence (AI) in dermoscopy. It covers how AI performs in classifying benign versus malignant lesions, comparing its effectiveness with human experts. The unit discusses landmark studies, the advantages of AI, and its current limitations.

Unit 2: Relevant Influences on AI Diagnosis
Unit 2 examines various factors that can impact the accuracy of AI in diagnosing dermoscopic images, such as hair, borders, and skin markings. It delves into the challenges AI faces with different melanoma subtypes and locations, highlighting how certain influences can affect diagnostic performance​.

Unit 3: Management Decisions and Dermoscopic Diagnosis with AI and HI
This unit focuses on the importance of management decisions in dermatology, emphasising the complementary roles of AI and human intelligence (HI). Through case examples, participants explore how AI can assist but also where human expertise remains essential for accurate diagnosis and clinical judgment​.

Unit 4: Future Development
The course looks ahead at the future of AI in dermoscopy. This unit explores ongoing research and innovations, suggesting that the combination of AI and HI will lead to better diagnostic accuracy and patient outcomes. It emphasises the need for continued collaboration between these technologies and human oversight.

Unit 5: Conclusion and Key Takeaways
In the final unit, the course wraps up by reinforcing the potential for combining AI with human intelligence to enhance diagnostic precision. Participants are encouraged to integrate AI into their practice while maintaining critical oversight, with a focus on optimising patient care​.

This course equips healthcare professionals with the knowledge to effectively use AI in dermoscopic diagnostics, ensuring improved clinical outcomes through the integration of advanced technologies with human expertise.