Artificial intelligence and skin cancer

Artificial intelligence is poised to rapidly reshape many fields, including that of skin cancer screening and diagnosis, both as a disruptive and assistive technology. Together with the collection and availability of large medical data sets, artificial intelligence will become a powerful tool that can be leveraged by physicians in their diagnoses and treatment plans for patients. This comprehensive review focuses on current progress toward AI applications for patients, primary care providers, dermatologists, and dermatopathologists, explores the diverse applications of image and molecular processing for skin cancer, and highlights AI’s potential for patient self-screening and improving diagnostic accuracy for non-dermatologists. We additionally delve into the challenges and barriers to clinical implementation, paths forward for implementation and areas of active research.

Introduction

Artificial intelligence (AI) stands at the forefront of technological innovation and has permeated into almost every industry and field. In dermatology, significant progress has been made toward the application of AI in skin cancer screening and diagnosis. Notably, a milestone that marked the era of modern artificial intelligence in dermatology was the demonstration of skin cancer classification abilities by deep learning convolutional neural networks (CNNs), which was on par with the performance of board-certified dermatologists (1). This CNN was trained on a dataset that was two orders of magnitude greater than those previously utilized. The dermatologist-level classification ability has since been experimentally validated by other papers (2, 3). Recent progress in the field of AI enables models to not only analyze image data but also integrate clinical information, including patient demographics and past medical history (4–6). Advancements allow for the simultaneous evaluation and identification of multiple lesions from wide-field images (7, 8). Moreover, models can now gain information from whole slide images without having to use costly pixel-wise human-made annotations (9). Despite these advancements, research has found that AI models lack robustness to simple data variations, have proven inadequate in real-world dermatologic practice performance, and that barriers remain before achieving clinical readiness (2, 10–14).

Clinical applications

Artificial intelligence has been employed to predict the most common types of skin cancers, melanoma (1) and non-melanoma skin cancer (1), through image analysis. In addition, machine learning has been used on RNA datasets to develop classifiers that also predict skin cancer, as well as the prognosis of skin lesions. Several of these methods can be, or have the potential to be, readily deployed by patients, primary care practitioners, dermatologists, and dermatopathologists.

Patients

With the rising prevalence of smartphone usage, patients can directly screen for and monitor lesions with AI applications. These applications can run AI models on patients’ own local devices, which ensures the protection of patient data (15). The feasibility of an AI model to assist patients’ with self-assessed risk using smartphones has been validated with a model that was trained on pictures captured from patients’ smartphones, and which exhibited comparable performance to general practitioners’ ability to distinguish lower-risk vs. higher-risk pigmented lesions (16). Moreover, AI significantly increased the abilities of 23 non-medical professionals to correctly determine a diagnosis of malignancy from 47.6 to 87.5% without compromising specificity (12). In the future, AI models may assist with overseeing and assessing changes to lesions as they progress (17) and collaborate with apps that allow patients to examine themselves and document moles (18, 19).

Despite progress with these AI models, there is no smartphone application that is endorsed on the market in the United States for non-professionals to evaluate their lesions as they do not have satisfactory performance or generalizability (20). Limitations include biases introduced due to the narrow range of lesion types, skin pigmentation types, and low number of high-quality curated images used in training. Further, inadequate follow-up has been a limitation with regards to identifying false negative diagnoses (21). Notably, users may not be adequately protected from the risks of using smartphone diagnostic apps by Conformit Europenne (CE) certification, which endorsed two apps with flaws (SkinVision and TeleSkin’s skinScan app). A prospective trial of SkinVision found low sensitivity and specificity for melanoma classification (22). In contrast to CE, the US Food and Drug Administration’s (FDA) requirements for endorsement are more stringent (21).

Primary care

Artificial intelligence applications can enhance skin cancer screening in the primary care setting and streamline referrals to dermatologists. Referral data from primary care practitioners to teledermatology consultations were used to train a model capable of a top-3 accuracy and specificity of 93 and 83%, respectively, given 26 skin conditions that makeup 80% of encountered primary care cases (4). This performance was on par with dermatologists and surpassed primary care physicians (PCPs) and nurse practitioners. This type of model could assist PCPs in diagnosing patients more accurately and broadening their differential diagnoses. In cases in which the top 3 diagnoses from the model have the same management strategy, patients may start treatment while awaiting further workup or follow-up with dermatology. Nevertheless, further testing on populations with a low prevalence of skin cancer is essential to demonstrate efficacy in the broader population (23).

Dermatology

Models have been trained to use electronic health record (EHR) data and/or gene sequencing data to predict an individual’s likelihood of developing melanoma (24–27) or nonmelanoma skin cancer (27–31). While AI models could potentially flag patients at high risk of skin cancer to be screened, studies are limited by the variability of included predictive factors, inconsistent methods of evaluating models, and inadequate validation (32). Moreover, EHRs often do not include some of the most important risk determinants for skin cancer, such as exposure to UV light and the patient’s familial history; the omission of such data may result in decreased performance (28).

Artificial intelligence has the potential to supplement dermatologists’ diagnostic and treatment capabilities in what is known as augmented intelligence (AuI). For diagnosis, AuI might assist dermatologists in more effectively managing teledermatology referrals (4) and increase the efficacy of in-person visits (33). However, in a prospective trial comparing AI to dermatologists in a teledermatology setting, dermatologists outperformed the AI (13). Despite AI currently underperforming dermatologists, AI could provide a new perspective that could still be beneficial as AI and humans exhibit distinct types of errors. For instance, models may provide insights into certain images’ classification ambiguity, whereas humans are better able to distinguish variability in image quality such as blurriness or shadowing (12).

Augmented intelligence can also assist with suggesting clinical decisions given inputted images, such as recommending whether a lesion warrants excision (34). The integration of AuI into dermatologic patient management resulted in a 19.2% reduction in unnecessary excisions of benign lesions (35). Although current CNNs’ performance has been shown to fall short when compared with using sequential dermatoscopic photography in predicting melanoma, AuI may be used in the future by dermatologists to evaluate and monitor lesion change (36). Of interest, in this study, neither dermatologists nor the CNN had satisfactory diagnostic performance levels on baseline images, but both dermatologists and CNN had improved performances when follow-up images were provided, and the best performance was combining CNN and dermatologist assessment together.

Integration of AI into advanced imaging techniques may reduce the extent of training necessary to use them (37). One area of application is in the detection of the dermal-epidermal junction, which is crucial in a non-invasive method of skin cancer diagnosis called reflectance confocal microscopy (RCM) imaging (38). Furthermore, there are ongoing efforts to analyze RCM images with AI (39).

The FDA has not approved any medical devices or algorithms based on artificial intelligence in the field of dermatology (40, 41). On the other hand, the FotoFinder Moleanalyzer Pro, an AI application for dermatology, was approved in the European market. It demonstrated performance on par with dermatologists in store-and-forward dermatology (42) and a prospective diagnostic study (43), however, the latter had extensive exclusion criteria, e.g., excluding patients of skin type IV and greater. The first randomized controlled trial comparing AI skin lesion prediction to dermatologists’ assessment reported that AI did not exceed attending dermatologists in skin cancer detection (44).

Dermatopathology

With the growing application of whole slide imaging (WSI) in the field of dermatopathology (45), AI can potentially support dermatopathologists in several ways, particularly skin cancer recognition. Among the AI models trained to detect melanoma from digitized slides (5, 46–50), two models were able to match the performance of pathologists in an experimental setting. These models were limited in that they were only given either a part of (46) or a single (49) hematoxylin and eosin (H&E)-stained slide. In contrast, pathologists can utilize supplementary data such as immunohistochemistry or relevant patient data. However, integrating patient information, such as age, sex, and lesion location, into CNN models did not enhance performance (5). One limitation to implementing AI in dermatopathology is the unreliable prediction that may be made when a model is given an input that differs from the training dataset. One potential solution is the use of conformal prediction, which has been shown to increase accuracy of prostate biopsy diagnosis by flagging unreliable predictions (51).

Studies have been done to evaluate AI’s ability for diagnosing basal cell carcinoma (BCC) using WSI (9, 52, 53). Campanella et al. showed the ability of a convolutional neural network to achieve 100% sensitivity for detecting BCC, on the test set; importantly, a multiple instance learning approach was introduced that obviated the necessity of time-consuming pixel-level slide annotations to distinguish between areas with and without disease (9). Kimeswenger et al. subsequently incorporated an “attention” function to draw attention to areas of digital slides that include indications of BCC. Interestingly, CNN pattern recognition varied from that of pathologists for BCC diagnosis as tissues were flagged based on different image regions (53). These CNNs could also be applied to identify and filter slides for Mohs micrographic surgery (52). In the setting of rising caseloads, AI can help to decrease pathologists’ workload generated by these commonly diagnosed, low risk entities. Duschner et al. applied AI to automated diagnosis of BCCs, and demonstrated both sensitivity and specificity of over 98%. Notably, the model demonstrated successful generalization to samples from other centers with similar sensitivity and specificity (54).

Artificial intelligence has also had some success in predicting sentinel lymph node status (55), visceral recurrence, and death (56) based on histology of primary melanoma tumors. In the future, AI could be utilized to identify mitotic figures, delineate tumor margins, and determine the results of immunohistochemistry stains; further, AI could recommend more immunostaining or genetic panels that could be of use diagnostically (57). While AI predictions have not been consistently successful for melanoma (58), AI has been demonstrated to identify the mutation given a lung adenocarcinoma slide that has been stained with H&E (59–61).

Oryginal article published at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10985205/ by Maria L. Wei, ^{1 , 2 ,*} Mikio Tada, ³ Alexandra So, ⁴ and Rodrigo Torres ²