Cautionary Side of AI in Dermatology: Education, Human-Computer Interaction, and Unintended Consequences

Conclusions

• AI tools are rapidly increasing in dermatology, but clinician education on their safe, ethical, and effective use is still lagging behind.
• Without proper training, AI may contribute to de-skilling in experienced clinicians and never-skilling in trainees who become overly dependent on it.
• Dermatology AI tools should be evaluated cautiously using transparent information about training data, intended use, validation, limitations, bias, and skin-tone representation.
• Current evidence suggests many AI tools, especially app-based ones, lack adequate supporting data and dermatologist involvement.
• Image quality issues such as blur, brightness changes, and surgical ink markers can meaningfully reduce AI performance.
• Dermatologic AI systems often perform worse on darker skin tones, raising major fairness and generalizability concerns.
• Human-AI interaction can be unreliable because faulty AI can mislead experts and even accurate AI may perform better alone than when combined with physicians in some settings.
• Generative AI can be sycophantic and may reinforce incorrect user beliefs, increasing the risk of misinformation.
• Deepfakes, fake publications, and AI-generated medical fraud create serious risks for patients, clinicians, and public trust.
• Medical chatbots and AI health products can be hacked or manipulated to produce dangerous advice, false medical claims, or unsafe prescribing guidance.
• The environmental cost of generative AI is substantial and should be considered part of the technology’s harms.
• Overall, AI in dermatology may be useful, but its adoption must be guided by strong standards for safety, privacy, fairness, transparency, and regulation.