趋势解读：Research into how AI can help users understand，讨论数据集与基础模型

Rory Sayres and Yun Liu, Research Scientists, Google Research We present recent published findings on how dermatology AI tools may help laypeople with their own skin-related questions. More than half of adults use the Internet for health information, and one-third turn to artificial intelligence (AI). However, access to information does not mean that it is easy to understand or correctly interpreted . In short, the human component of AI for health information remains important to research to help people benefit from better health information. Specifically, this is important in the space of dermatology (skin, hair, nails; henceforth “skin” for brevity) because people have trouble looking for the right information online related to their skin concern. For instance, you may notice “red dots on legs,” but not have the background knowledge to specifically search for “ palpable purpura ”. Over the years, we have built a technical foundation in this area, including developing AI models to inform differential diagnoses, performing validation of model generalization , and releasing datasets like SCIN to help clinicians and researchers. However, the most significant impact can only be realized by supporting the decision-making of people who have skin concerns through providing high-quality information. To do this right, understanding how humans engage with AI to inform their decisions is critical. Previous studies evaluating non-AI tools have shown that while people might get better at identifying a condition using the internet, they don't necessarily get better at deciding what next steps to take. We need to ensure that as AI tools become available, we carefully study and improve upon the human factors to support people in making better decisions. With the above in mind, today we share some of our recent and past research on consumer understanding of AI tools for their dermatology-related questions. These include a recent large-scale quantitative paper that demonstrates increased ability to name conditions with AI assistance, as well as some benefits in determining what next steps to take. It also includes an in-depth mixed-methods study addressing how people use these tools on their own skin concerns, and how the understanding they gain compares to that from conversation with doctors. In “ Consumer Understanding of Skin Concerns With an AI-Powered Informational Tool ,” published this week in JAMA Dermatology , we investigated how structured AI assistance changes a user's ability to identify a condition and determine their next steps. We showed 2,345 survey participants retrospective, de-identified skin condition cases — complete with images and structured medical history — and asked them to imagine the cases were their own. Screenshot of the AI interface (specifically created on the survey platform for this research study) participants saw. They were presented with a case vignette ( A ), and provided with a scrollable carousel of predictions from an AI ( B ). If they clicked on a condition, they were given detailed information about the condition ( C ). Participants were randomized into three groups to research the cases: (Negative) control : Participants used existing tools they were familiar with, such as standard text-based web searches. AI : Participants used a prototype AI (user interface in figure above) that provided a scrollable carousel of 3 to 7 matching conditions based on an AI model's predictions, complete with textbook images and details about symptoms and treatments.