Derm Dispatch: Navigating the Ethics of AI-Assisted Dermatology
In the first episode of 2026, a critical discussion unfolded regarding the expanding role of artificial intelligence (AI) in dermatology and the broader clinical enterprise. The conversation emphasized that AI should not be viewed as a replacement for clinicians but rather as a permanent and necessary clinical partner. It is suggested that AI should be budgeted akin to electricity or staffing, rather than justified solely by short-term return on investment.
Challenges in AI Adoption
A significant barrier to the adoption of AI in dermatology is the technical and financial burden of integrating AI into electronic medical records (EMRs). It was highlighted that delaying adoption until a clear return on investment (ROI) is demonstrated fails to recognize the inevitability of AI becoming embedded in standard care practices. Institutions are encouraged to work backward from their clinical goals, such as:
- Improving efficiency
- Detecting diseases earlier
- Reducing morbidity
By selecting AI tools that align with these outcomes, institutions can enhance patient care effectively.
Equity and Bias in AI
During the discussion, the themes of equity and bias emerged prominently. The speaker pointed out that AI systems are only as good as the data used to train them. Historically, dermatologic training materials have underrepresented skin of color, leading to potential diagnostic inequities, particularly for Fitzpatrick skin types V and VI. To mitigate this risk, localized training of smaller AI models utilizing institution-specific data is advocated. Additionally, the concept of federated learning systems was introduced, where anonymized data “weights” are shared across sites to enhance performance while respecting patient privacy.
Trust and Transparency
For the successful integration of AI in dermatology, trust and transparency are deemed critical. A proposed “AI Bill of Rights” would disclose how AI tools are employed, their known biases, and their limitations—similar to a nutrition label—thus aiding patients in understanding how AI informs their care decisions.
The Dangers of Over-Automation
While acknowledging the potential of AI to enhance diagnostic accuracy and efficiency, it was cautioned that clinicians must retain foundational diagnostic skills and the capacity to challenge machine outputs. Emphasis on clinical reasoning in training is essential, including designated periods when AI tools are turned off to maintain clinical gestalt.
The Future of Dermatology
Looking ahead, the vision for dermatology is to evolve from episodic diagnosis to longitudinal, predictive care. The integration of digital twins, genetic data, wearables, and AI-driven analytics could facilitate earlier disease detection, personalized treatment trajectories, and even preventive interventions before pathology manifests clinically. This innovation has the potential to reshape the management of chronic inflammatory diseases and skin cancer throughout a patient’s lifetime.
