AAD Project Expands HS Education, Registry Insights, and QI Initiatives

Conclusions

• The project suggests that combining real-world registry data with AI can improve recognition and diagnosis of hidradenitis suppurativa, especially by identifying undiagnosed patients through broader clinical signals.
• The findings appear to show that HS presents through a wider and less classic set of patterns than traditionally recognized, including pain, inflammation, acne, cellulitis, dermatitis, and mental health comorbidities.
• The initiative indicates that AI-derived patient finder results can be translated into practical clinician education that increases confidence in HS treatment options such as biologics and combination therapies.
• The presentation concludes that a multi-channel educational strategy is effective, since clinical community posts, podcasts, on-demand courses, and conferences generated substantial engagement and learner reach.
• The work implies that patient-facing educational materials also need updating because registry and AI findings can inform more accurate public information about HS and related boil-like conditions.
• The final phase of the project suggests that quality improvement coaching and peer-to-peer learning can turn analytic findings into actionable practice changes across participating dermatology sites.
• Overall, the research seems to conclude that AI is most valuable when paired with expert review, education, and QI implementation rather than used as a standalone diagnostic tool.