The synergy of skin and science – A comprehensive review of artificial intelligence’s impact on dermatology

INTRODUCTION

Artificial intelligence (AI) is currently being used extensively in many different industries, greatly enhancing each one – the health-care industry most prominently among them. Clinical errors generated by physicians in medical diagnosis and treatment recommendations can be eliminated by AI, according to published literature.^[1]

The AI has brought significant advancements to the detection and treatment of skin illnesses, including psoriasis, skin cancer, dermatomyositis, and onychomycosis.^[2] Published literature depicts a number of the most highly reviewed smartphone applications for skin care, including MDacne – Custom Acne Treatment, Acne Intelligence, Medgic – Scan, Analyze and Detect Skin Problems, Skin Bliss: Cosmetics and Beauty, and Miiskin – Skin Cancer eHealth, which are completely AI programmed.^[3] Skin conditions such as melanoma have the potential to grow rapidly into cancer. Fortunately, with the help of smartphone apps like SkinVision^® – which have been downloaded over 900,000 times on Android – laypeople are now able to identify melanoma and further classify its risk, halting the disease’s progression.^[4] The clinical laboratory improvement amendments govern the DermTech Melanoma Test^™, a physician-ordered laboratory-designed test. DermTech is developing Luminate SM to detect ultraviolet damage at the genetic level, using a proprietary adhesive, which can amplify the RNA molecule.^[5] The specialty of dermatology faces challenges with significant disparities in health-care accessibility, which are amplified by the current urbanization trends and further complicated by the COVID-19 pandemic. Dermatologists tend to specialize in metropolitan areas, leaving rural and overlooked regions with a shortage of these specialists. Meanwhile, the pandemic has accelerated the transition to virtual healthcare delivery, calling into question established care models and requiring creative solutions to guarantee dermatological services to all.

Conversely, teledermatology offers a viable solution to address the discrepancy in accessibility to dermatological care by providing virtual consultations and diagnostic assessments. This approach improves marginalized communities’ access to healthcare while also overcoming geographical limitations. Teledermatology is enhanced by AI analytics and monitoring, which optimizes patient self-care, personalized therapy recommendations, and diagnostic accuracy.^[6] Dermatology relies on recognizing morphological traits, mostly patterns in visual data which are used for diagnosis. Although newer skin imaging methods such as dermoscopy, Reflectance confocal microscopy (RCM), and Very high frequency (VHF) ultrasonography show promise, reliable diagnosis still depends on in-person patient examinations. According to published studies, human intelligence as a whole performs better in identifying skin conditions than conventional neural networks. Other factors that have been identified as challenges since the use of AI was introduced in medicine, consist of bias in the data and algorithm of the technology, a lack of transparency about how these technologies operate, and the anxiety that follows, user privacy concerns, and issues like health-care professionals relying too much on AI. The findings might represent the consequence of many different variables.^[7] In this review of the scientific literature, we will delve extensively into the wide-ranging subject of AI and look at its actual contributions to the field of dermatology.

MATERIAL AND METHODS

Thirty-one published articles from 2019 to 2024 were analyzed for this literature review using keywords such as “skincare and AI,” “healthcare innovations,” “online applications for skincare,” “precision of AI in skincare” from Google Scholar, PubMed, and various online articles sharing more information about AI and dermatology. This literature review describes a vast array of uses of AI in dermatology that go beyond skin cancer diagnosis. Numerous published case studies, clinical trial studies, and literature reviews were examined for the purpose of this study. The study also covers AI’s natural language processing (NLP), neural networks, and machine learning, along with hazards and limitations of AI as well as how it is impacting dermatological decisions and how much it advances dermatological procedures.

RESULTS

In the discipline of dermatology, AI has proven to be extremely helpful, particularly in the early detection of skin malignancies and in resolving scheduling conflicts during patient visits. AI has also been crucial in the development of teledermatology. Furthermore, several applications use the smartphone camera to analyze and predict the future of skin. These applications also provide amazing visual demonstrations of how various skincare and haircare products may affect our skin, as well as AI has assisted dermatologists make more accurate decisions regarding their treatment strategies. However, some of the drawback’s AI has promoted in dermatology include privacy concerns associated with employing face recognition systems in these applications, incomplete knowledge of how AI’s internal processes work, an overreliance on technology by dermatologists and laypeople for diagnosis, or self-diagnosis by users of these applications, which occasionally may not be accurate. The AI-based applications have also led many individuals to believe misconceptions about how to take care of their skin and hair and to focus only on maintaining a “perfect appearance.” As a result, from the review of published literature, it is suggested that AI should only be used as a tool to assist dermatologists and regular people, never as a substitute for a professional dermatological consultation. In addition, we may be able to depend more on the scientific advances that these technologies bring about in the field of dermatology if the gaps in the literature and the limitations of AI are addressed and presented appropriately.

DISCUSSION

By generating greater accuracy in treatment plans and diagnostic models, AI has improved both people’s lives and those of dermatologists. However, applying AI in dermatology comes with several downsides. One of them is the complexity of data collections stored in these applications. Larger and more intricate models have become the foundation for recent advancements in AI systems, particularly in deep neural networks and generative AI (like ChatGPT). As networks become more complex, they become more challenging to comprehend and audit, and humans are less able to provide the necessary resilience to step in when a system breaks.^[10] Health-care practitioners are required to adhere to stringent standards, which include refraining from sharing patient data with other companies, as a result of healthcare patient confidentiality legislation, such as the Health Insurance Portability and Accountability Act of 1996 (HIPPA).^[20] This has shown to be a barrier to accessing a large amount of health-care data for AI developers. In addition, this restriction might have an impact on the variety of datasets and images that these AI systems process, which could have a detrimental effect on health-care disparities and increase their prevalence. The challenge of algorithmic explainability comes from having discovered that machine learning models’ internal operations tend to be difficult to comprehend and opaque. This usually does not occur during an appointment with the patient, where the dermatologist can more clearly and simply explain to the patient when, why, and how to take the treatment prescribed.^[21] This aspect of AI can result in a loss of control over decision-making and a decline in patient and medical staff’s trust in these technologies. Even though AI might be more advantageous than more conventional approaches, like using a logistic regression model, it is not used often in medical curriculum reviews. These benefits include, for instance, Artificial neural network (ANNs’) capacity to resolve multidimensional issues, offer improved classification accuracy, and create robust correlations between variables.^[22] The lack of ability of deep learning algorithms to articulate their method of decision-making renders it challenging to assess AI’s efficacy in medical education curriculum. A vital aspect of the medical curriculum is clinical reasoning, which includes evaluating and managing patients’ medical conditions by taking a thorough medical history and developing differential diagnoses. The intricacy of deep learning restricts its usefulness in supporting clinical reasoning. Research contrasting AI with conventional teaching approaches is hard to come by and necessitates sizable sample numbers as well as distinct surrogate indicators for impartial evaluation. Time restrictions and a lack of expertise can also make it difficult to integrate AI into medical school curriculum.^[23] In medical education, collaboration secures clinical relevance and accuracy. Furthermore, individuals these days are increasingly inclined to turn to the internet and AI for answers about their skin and hair, which might give rise to conflicting opinions on the treatment plans recommended by dermatologists and those generated by AI.^[24] Countless datasets have been fed into AI technology in an attempt to diagnose skin disorders, but it has been observed that this technology is not as effective when it comes to skin of color (SOC). SOC photos are currently noticeably underrepresented in AI training datasets since these datasets were initially trained on resources with lighter skin tones.^[24,25] To avoid biased algorithms, it is crucial to teach AI computers to recognize skin lesions across skin tones, as dermatological illnesses may present differently in patients with diverse skin tones. Furthermore, AI in some instances cannot understand the real texture, quality as well as form of the skin if the device used to capture the image does not have enough pixel or lighting adjustments; thus, it could form a biased treatment, which might further do bad more than a good output. Furthermore, an individual might not be fully informed where their data is being collected and processed, thus promoting a higher chance for these data to be mishandled.^[26] The legal issues, ethical issues, and data privacy issues regarding AI are not yet fully resolved. As we have mentioned before, AI is incredibly user-friendly, but those who lack digital and health literacy frequently find it challenging to use these apps. There could be several causes for this, such as a language barrier, where the individual utilizing AI technology is unable to comprehend the slang, dialect, or language utilized by the application.^[27] This could result in miscommunication, a delay in receiving treatment, or an inaccurate self-diagnosis. The trained dataset contains a reduced representation of several languages, which leads to these language disparities. Even though the majority of people are experienced smartphone users, those without smartphones or those using outdated models for whom these apps are not compatible may find these apps to be of no use. These groups primarily consist of the elderly, those 65 years of age and older, people with limited resources, and those without formal education.^[28] These groups might not find the independent and user-friendly aspects of these programs beneficial, or they might be heavily dependent on their family members to use them for personal use. In either case, they might only find the in-person interactions with dermatologists to be helpful. The other drawback of AI is that human dermatologists may have a propensity to rely solely on AI rather than expanding their knowledge and becoming up to date on new methods and developments in the field.^[29] Careful consideration of the equity issue in AI diagnosis is necessary to prevent unintentionally aggravating healthcare inequities.^[30] If technology does not support the recommended treatment by a human dermatologist, it might lead to prejudice and erode patient-physician trust.^[31]

CONCLUSION

The use of AI in dermatology has generated discussion since the advent of this technology. Concerns about personal privacy have been raised by the use of facial recognition technology in dermatology. However, AI has also played a crucial role in the early detection of skin cancer, the exclusion of conditions like malignant melanoma, the assessment of skin ulcers, the recommendation of appropriate treatment, and the assistance of dermatologists in making more precise diagnoses. Nevertheless, AI technologies might occasionally display outcomes that vary for the same input, indicating a lack of logic in these systems. As such, we cannot fully rely on them in the same way as we could on a skilled dermatologist. AI does not consist of the lengthy, peer-reviewed research and historical participation that form the basis of medicine. The human element that is essential for patient-doctor relationships is lacking in these technologies. Legal systems find it difficult to handle culpability when AI harms patients. Liability clarification and overcoming resistance are critical to using AI in healthcare. Furthermore, errors made by the AI should never be liable on the physician. It is more of a combined approach, rather than trusting the technology blindly and taking the decisions.^[31] Furthermore, there are many situations in which a dermatologist must connect all the physical findings with the histology findings and the patient’s history to draw a judgment rather than merely depending on their theoretical knowledge. An AI that receives input such as this is unlikely to deliver a correct diagnosis since these machines are limited in their ability to comprehend, integrate, and produce a logical explanation for all the data they encounter. In certain instances, AI has also given laypeople unrealistic expectations, which has led to doubts about dermatologist’s treatment recommendations and anxiety about one’s skin condition. As a result, it is accurate to end this literature study by saying that AI should only be used as a tool to assist dermatologists and regular people, never as a substitute for a professional dermatological consultation. In addition, we may be able to depend more on the scientific advances that these technologies bring about in the field of dermatology if the gaps in the literature and the limitations of AI are addressed and presented appropriately.