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Gregoor et al. evaluated the healthcare implications and costs of an AI-enabled mobile health app for skin cancer detection, involving 18,960 beneficiaries of a Netherlands insurer. They

report a 32% increase in claims for premalignant and malignant skin lesions among app users, largely attributed to benign skin lesions and leading to higher annual costs for app users

(€64.97) compared to controls (€43.09). Cost-effectiveness analysis showed a comparable cost to dermatologist-based diagnosis alone. This editorial emphasizes the balance in AI-based

dermatology between increased access and increased false positives resulting in overutilization. We suggest refining the diagnostic schemas with new referral pathways to capitalize on

potential savings. We also discuss the importance of econometric analysis to evaluate the adoption of new technologies, as well as adapting payment models to mitigate the risk of

overutilization inherent in AI-based diagnostics such as skin cancer detection. The use of AI for medical diagnosis has found an early home at scale in skin cancer. The complex process of

diagnosis can involve integrating data on a patient’s symptoms and history, physical exam, lab values, and imaging studies. AI tools, including machine learning and deep learning algorithms,

can learn from and efficiently process large volumes of data. Researchers have used AI-based tools to aid in the process of diagnosis in various different contexts, including the detection

of diseases of the skin1, liver2, heart3, and other organs4. Moreover, other tools are made to interface directly with patients and influence their care. Gregoor et al. analyze data from the

pilot of a mobile health app that used an AI algorithm for skin cancer detection5. In 2019, a Dutch health insurance company offered 2.2 million adults free access to this app. Gregoor et

al. matched 18,960 users who completed at least one successful assessment with the app to controls who did not use the app. They reported a 32% increase in claims for premalignant and

malignant skin lesions among app users compared to non-app users. App users had twice as many biopsies and excisions matched as well as four times (5.9%) the claims for benign skin tumors

and nevi compared with controls (1.7%). App users also had fewer claims for malignant skin lesions than controls. The increased benign claims and fewer malignant claims resulted in higher

total annual costs for app users (€64.97) vs. controls (€43.09); costs per individual claim for malignant lesions were also higher for app users (€613.36) vs. controls (€520.05). Estimates

on the cost of capturing one additional premalignant and malignant skin lesions via the app ranged from €2657 to €488. COMPARING AI-BASED SKIN CANCER DIAGNOSIS WITH CONVENTIONAL DERMATOLOGY

This study is noteworthy in its effort to characterize the healthcare implications of dermatology AI-based diagnosis beyond the existing literature on diagnostic accuracy alone—i.e., cost,

cost-effectiveness, and utilization. The reasons for overutilization enabled by this app are manyfold. The app performed as expected from previous reports by the developers—sensitivity of

87–95% and specificity of 70–78%6,7. The deployment of this AI skin app at a broad scale shows the real-world costs of more false positives (benign lesion claims) and fewer true positives

(malignant lesion claims) compared to the management of non-app users. More false positives and fewer true positives compared with conventional care can take an emotional and financial toll

on patients and the healthcare system. The overall cost-effectiveness of the screening may be comparable to that of a dermatologist. A recent study in the US found that the cost of detecting

an additional skin premalignancy or malignancy through total body exams was $23468. Depending on the assumptions of these calculations, the skin app performed at a comparable cost per new

positive identification. In context, increased total costs per app user at a comparable cost-benefit ratio suggests that the app users are enjoying more of the “benefits”—i.e., they had more

skin lesions diagnosed than non-app users, likely due to increased access. This supports using AI skin apps insofar as access is the limiting determinant of diagnosis. In addition, the

algorithm was preset to flag premalignancies like actinic keratosis, a dysplastic but inherently benign lesion, as high risk. Actinic keratosis is a benign lesion on the same spectrum as

squamous cell carcinoma, but with a low risk (0.1%) of malignant transformation9. Higher scrutiny in flagging these findings for review/claims should be utilized to triage for more

cost-effective care. This may require more nuanced classification schemas as high risk, low risk, or moderate risk. Moderate-risk categories could then be evaluated and triaged by

dermatologists and advanced practice providers, with lower reimbursement or diagnostic priority to save costs. ADDRESSING OVERUTILIZATION These data fall along similar findings across other

areas of digital health, with the benefit of increased access and timeliness balanced by the risk of overutilization. For example, unique aspects of telehealth, such as more efficient triage

and decreased emergency department utilization, suggest a potential for cost-effectiveness, paralleling the benefits and risks observed in dermatology AI. However, in another area of

digital health, telehealth implementation in primary care during COVID-19 may have led to overutilization, with some studies suggesting that telehealth visits were used as additions rather

than substitutes to in-person visits10. A more direct parallel to skin AI may be radiology AI, wherein increased imaging given ease of AI-enabled use led to increased invasive testing and

follow-up due to false positives11. As AI implementation will inevitably continue to increase, a few different strategies will be necessary to address the conundrum of overutilization.

Primarily, the use of AI algorithms should be rationalized before implementation. When appropriate, AI algorithms should be evaluated against traditional methods using econometric analysis

and pilot studies such as that of Gregoor et al. The rate and cost of true positive identification should be assessed against the rate of false positives and the performance of traditional

care. On a systems level, this will require regulators and administrators to establish guidelines for the responsible use of AI diagnostics. The V3 framework for biometric monitoring can be

adapted for AI diagnostics more broadly; it involves verification of the technology with preset criteria, analytical validation of the algorithm, and clinical validation in a real-world

target context12. Payment and incentive models will need to be adapted as well. The current per-use reimbursement models, while feasible for early AI products, may result in the overuse of

AI, analogous to experiences with traditional medical devices. In the pursuit of value-based care, reimbursement should incorporate outcomes instead of volume. In addition, when piloting new

AI devices like the skin cancer app in this study, payers should utilize advanced market commitments and time-limited reimbursements for new AI applications. Such an approach can more

sensitively control the adoption of AI technologies and mitigate risks of overuse13. As AI for clinical diagnostics moves more broadly into the implementation stage, the threat of

overutilization should be anticipated. Rising to this challenge will require adjustment to payment models and evidence-based stewardship. REFERENCES * Kumar, Y., Koul, A., Singla, R. &

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AI. _NPJ Digit. Med._ 5, 112 (2022). Article  PubMed  PubMed Central  Google Scholar  Download references AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Harvard Medical School, Boston, MA,

USA Kaushik P. Venkatesh, Marium Raza & Joseph Kvedar Authors * Kaushik P. Venkatesh View author publications You can also search for this author inPubMed Google Scholar * Marium Raza

View author publications You can also search for this author inPubMed Google Scholar * Joseph Kvedar View author publications You can also search for this author inPubMed Google Scholar

CONTRIBUTIONS The first draft was written by K.P.V. M.R. and J.K. provided critical revisions and approved the final draft. CORRESPONDING AUTHOR Correspondence to Kaushik P. Venkatesh.

ETHICS DECLARATIONS COMPETING INTERESTS J.K. is the Editor-in-Chief of _npj Digital Medicine_. The other authors declare no competing interests. RIGHTS AND PERMISSIONS OPEN ACCESS This

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AI-based skin cancer detection: the balance between access and overutilization. _npj Digit. Med._ 6, 147 (2023). https://doi.org/10.1038/s41746-023-00900-0 Download citation * Received: 19

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