Slideshow: AI in Skin Cancer Awareness Creates New Opportunities for Care


Research exploring the growing role of artificial intelligence (AI) in health care sheds a light on the potential of the technology to improve skin cancer detection and awareness among patients and health care professionals.

Earlier this year, the FDA cleared the first-of-its-kind DermaSensor, a real-time, non-invasive skin cancer evaluation system, for use in non-dermatology primary care settings.1 The handheld device uses painless spectroscopy technology and artificial intelligence (AI) analysis of suspicious lesions to provide feedback about skin cancer risks in patients.

Previously limited to visual mole assessments, primary care physicians are now empowered by DermaSensor, a quantitative, point-of-care tool, to provide precise and timely skin care evaluations. This innovation has the potential to relieve burdens associated with a shortage of dermatologists and accelerate necessary patient care.

The clearance of DermaSensor highlights the growing role of AI in health care. Although use of the device has not yet trickled into the pharmacy setting, other tools like automated dispensing systems and predictive analytics have already begun digitizing the field.

The rapid pace of AI development highlights a crucial opportunity for pharmacists. As the most accessible health care providers—and often the first to be approached by patients with undiagnosed skin conditions—they are well-positioned to learn about evolving AI applications in dermatology. This knowledge can empower the health care professionals to leverage AI’s potential in future care.

READ MORE: Dermatology Resource Center

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1. FDA clearance granted for first AI-powered medical device to detect all three common skin cancers (melanoma, basal cell carcinoma, and squamous cell carcinoma). News release. DermaSensor Inc. January 17, 2024. Accessed May 23, 2024.
2. Krakowski I, Kim J, Cai ZR, et al. Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis. NPJ Digit Med. 2024;7(1):78. Published 2024 Apr 9. doi:10.1038/s41746-024-01031-w
3. Roffman D, Hart G, Girardi M, Ko CJ, Deng J. Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network. Sci Rep. 2018;8(1):1701. Published 2018 Jan 26. doi:10.1038/s41598-018-19907-9
4. Key statistics for basal and squamous cell skin cancers. Data sheet. American Cancer Society. October 31, 2023. Accessed May 28, 2024.
5. Nelson CA, Pérez-Chada LM, Creadore A, et al. Patient perspectives on the use of artificial intelligence for skin cancer screening: A qualitative study. JAMA Dermatol. 2020;156(5):501-512. doi:10.1001/jamadermatol.2019.5014
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