San Diego — Just a day before the annual meeting of the American Academy of Dermatology began, a study was published online in, cautioning that most downloadable mobile apps driven by artificial intelligence for use in monitoring dermatologic conditions lack validation.
To date, "there is not much transparency in what data AI models are using," Rotemberg said at the meeting. Based on the expectation that dermatologists will be purchasing rather than developing their own AI-based systems, she reiterated more than once that "transparency of data is critical," even if vendors are often reluctant to reveal how their proprietary systems have been developed.
"At a minimum, app developers should provide details on what AI algorithms are used, what data sets were used for training, testing, and validation, whether there was any clinician input, whether there are any supporting publications, how user-submitted images are used, and if there are any measures used to ensure data privacy," Wongvibulsin wrote in the poster.
While Rotemberg did not comment on these claims, she cautioned that AI models differ with regards to how they were trained and the relative heterogeneity of the training dataset defined by types of patients, types of skin, and types of AI learning processes. All of these variables are relevant in whether the AI will perform in a given clinical setting at the level it performed during development.
At the AAD symposium, Rotemberg asked the audience for suggestions about the needs they hoped AI might address for in office care or efficiency. Their responses included generating prior authorizations for prescriptions, triaging email for importance, and helping to improve efficiency for common front desk tasks. She liked all of these suggestions, but she warned that as powerful as it can be, AI is not likely to provide technology that will fit seamlessly into workflows without adjustment.
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