Patient Awareness of Dermatologic Conditions Using AI, With Rory Sayres, PhD, and Yun Liu, PhD

In a continuation of their discussion with HCPLive, Rory Sayres, MD, and Yun Liu, PhD, of Google Research, went into greater depth about the implications of their recent JAMA study, focusing on how artificial intelligence (AI)-generated information is interpreted by consumers and what improvements may be needed to better support clinical decision-making.1,2

A key theme in this video segment was the gap noted by the 2 speakers between improved condition recognition and limited gains in understanding appropriate next steps. While AI tools can meaningfully increase a user’s ability to identify a likely skin condition, Sayres expressed, translating this recognition into actionable decisions, such as whether to seek care, remains more complex. Such a disconnect, he noted, is consistent with prior data observed on use of online health information, suggesting influencing behavior is inherently more difficult than improving knowledge alone.

“There are definitely a lot of diseases [in ophthalmology], but when an ophthalmologist is looking at like an image of a retina, that number of possible diseases is somewhat smaller,” Sayres explained. “Whereas when you look at dermatology, one of the first things we saw is the set of conditions you might want to assess for numbers at least in the hundreds.”

The investigators emphasized that accuracy of the underlying AI predictions plays a critical role in shaping outcomes. The study’s “Wizard of Oz” arm, which presented dermatologist-generated differentials, demonstrated stronger improvements in next-step decision-making compared with the AI arm. This finding highlights that even modest gaps in prediction quality can influence how users interpret and act on information. Liu noted that improving the precision and ranking of suggested conditions may therefore be an important step toward enhancing the clinical usefulness of these tools.

Outside of prediction accuracy, the 2 speakers pointed to the value of how information is presented to users. In their study design, they included structured condition “cards” with images, descriptions, and treatment information, but their results indicate the providing of more data is not sufficient. Instead, Sayres indicated future iterations of AI tools may require guiding users through decision-making processes, potentially by providing them with more apparent recommendations or contextual cues regarding the level of severity and urgency.

In their discussion, Liu and Sayres also touched on user behavior and variability in how individuals act on their health information. Participants brought differing levels of prior knowledge, interpretation strategies, and confidence, all of which can impact patient outcomes regardless of the use of AI tools. As a result, the 2 investigators describe optimizing user experience and interface design as just as necessary as algorithmic accuracy improvements.

Looking ahead, Sayres and Liu noted the promise held by AI tools as adjuncts to, not replacements for, clinical care. Although current data support their role in improving consumer understanding of skin conditions, further refinement was described necessary to ensure these technologies can reliably guide patients toward appropriate next steps.

The quotes contained in this summary were edited for the purposes of clarity.

Disclosures: Sayres and Liu reported stock ownership from Google outside the submitted work.

References

1. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018 Nov 10;392(10159):1789-1858. doi: 10.1016/S0140-6736(18)32279-7. Epub 2018 Nov 8. Erratum in: Lancet. 2019 Jun 22;393(10190):e44. doi: 10.1016/S0140-6736(19)31047-5. PMID: 30496104; PMCID: PMC6227754.

2. Sayres R, Jain A, Liu Y, et al. Consumer Understanding of Skin Concerns With an AI-Powered Informational Tool. JAMA Dermatol. 2026 Apr 15:e260597. doi: 10.1001/jamadermatol.2026.0597. Epub ahead of print. PMID: 41984449; PMCID: PMC13084546.