Dermatologists

AI is already helping dermatologists with some routine tasks. For example, AI “scribes” (computer programs) can listen to doctor–patient chats and automatically write up the visit notes, cutting paperwork and doctor burnout ^{[1] [2]}. Other AI tools can review patient histories and images: one system was trained to distinguish eczema from other skin problems by using patient records and symptoms ^[3].

Because dermatology is image-heavy, AI image analysis is growing fast. Apps and hospital systems can check a photo of a mole or rash and flag dangerous lesions almost as well as a specialist ^{[4] [4]}. These tools help doctors make quicker decisions, but they don’t replace the doctor.

Tasks like doing skin surgery or taking a biopsy still require a human touch. After a biopsy is taken, AI can help read the microscope slides – for example, one AI algorithm recognized melanoma with about 96% accuracy ^[4]. Reading new research and talking with patients or colleagues can’t be automated.

In short, AI is augmenting routine parts of the job (like writing notes and scanning images) ^{[1] [4]}, but the hands-on, judgment-based work of dermatology remains firmly human.

AI tools are spreading in healthcare but adoption is mixed. Many doctors actually welcome help: one survey found about 77% of dermatologists believe AI will improve care and most think it should be part of their training ^[4]. In practice, dozens of startups now sell AI scribes and skin apps (about 60 companies offer AI note-taking tools ^[1]) and large clinics are already piloting them.

However, AI adoption also faces hurdles. Hospitals report that many AI tools are still early-stage or costly ^[4]. For example, a study of health systems found that while nearly all were trying AI for clinical notes (100% had active projects), 77% said the tools feel “immature” and many worried about cost and unclear regulations ^{[4] [4]}.

In addition, medical AI must meet strict rules and doctors worry about accuracy and fairness (for example, image-based AI trained on one patient group may not work for everyone). These concerns mean adoption will be cautious and regulated.