Artificial Intelligence will overhaul other technologies used for the diagnosis of skin diseases in the future.
Fremont, CA: Artificial Intelligence is leaving its traces in all aspects of the medical field, including drug discovery and development, medical image recognition, and many more. Dermatology is most suited for applying AI in image recognition for assisted diagnosis. At present, technologies like dermoscopy, reflectance confocal microscopy (RCM), and very high-frequency (VHF) ultrasound are employed for skin imaging for clinical diagnosis of diseases. The choice of selection among these techniques varies based on different conditions of skin lesions. In recent years, many developed countries in the world have actively proposed strategic plans for the development of AI and promoted the development of AI to a new vital era. The unremitting efforts of researchers, dermatologists, and the network providers have led to the rapid growth of AI in dermatology.
The extremely valuable skin image resources increase the possibility of AI for its application in dermatology and are crucial primary data for AI. State-of-the-art machine-learning classifiers play an essential role in clinical practice and are superior to human experts in the diagnosis of pigmented skin lesions. There are different imaging technologies with varied advantages that use the massive skin imaging data to provide necessary resources leading to the development of skin imaging. Here, AI technology depends on the highly efficient computing ability and imitates through deep learning to offer high-quality medical services and solve the problem of the uneven distribution of medical resources.
Although AI in dermatology has developed rapidly in recent years, it has encountered bottlenecks in the clinic, and several associated problems need to be solved immediately. As AI technology mostly relies on skin images, the current scale of skin disease image data is insufficient, the quantity of information shared between hospitals is inadequate, and the standard and quality of skin images are not uniform. Because it is difficult to obtain high-quality image data, it will lead to the unreliability of research results. Also, there are innumerable numbers of diseases in dermatology, and the dermatological AI can recognize only a limited number of them.
Lack of enough means to integrate different platforms offered by the medical and AI complex talents makes it indispensable to cooperate closely with multi-disciplinary personnel in computer science, biomedical, and medical. The current AI diagnosis also involves legal issues, ethical issues, and data privacy issues that have not yet been fully resolved. Besides, the involvement of database sources requires approval processes and the specificity of AI products. The diagnosis of skin diseases is not only confined to clinical and skin images but also requires patient history, gender, age, and other information to obtain an accurate diagnosis. Therefore, skin image data and patient data need to be integrated, and AI is used to comprehensively analyze these data, thereby playing a more significant role in disease diagnosis, treatment decision-making, and prognosis judgment in the future.
Also, AI is bridging the barrier of easy disease diagnosis and is not a substitute for communication between doctors and patients. Theoretical and methodological research in the fields of AI, machine perception and pattern recognition, machine learning, natural language processing, intelligent systems and applications, knowledge representation and processing, cognitive and neuroscience-inspired AI are encouraged.
In general, dermatology AI has received unprecedented attention from all aspects of scientific research support, technology development, and market capital. Still, there are numerous potentials for AI in the dermatology field. In the future, with the promotion and innovation of AI theory and technology, the expansion and quality improvement of database resources, AI will bring more professional, accurate, and personalized auxiliary diagnosis and treatment to dermatologists. Thus, the ultimate goal is to leverage AI technology to serve doctors and patients better.
