Artificial Intelligence: An Embracing Technology in Dermatology
Artificial Intelligence: An Embracing Technology in Dermatology
Man has used machines since time immemorial in his quest to sustain and make life easier. Machine evolution has mirrored the human spirit and enterprise, progressing from simple tools to computers today. Machine dependency has permeated almost every aspect of human life, including medicine. While a dependence on subjective human skill marked the emergence of modern medicine, newer technological innovations have gradually progressed towards a more objective approach. Artificial intelligence and the implications of its application in various domains are two of the most discussed advancements in modern technology in recent times. Experts in artificial intelligence in medicine contemplate artificial intelligence to be the stethoscope of the twenty-first century.
Artificial intelligence is becoming more important in dermatology, with studies demonstrating that it can match or outperform dermatologists in the diagnosis of disease lesions from clinical and dermoscopic images. Real-world clinical validation, on the other hand, is currently missing.
What is Artificial Intelligence?
Computer systems are used in artificial intelligence (AI) to carry out operations that would typically need human intellect, such as speech recognition and vision. Robotics, machine learning, and the internet are just a few tools and algorithms that AI uses to mimic how the human brain functions. AI has the ability to perform better than humans since it has unlimited processing and storage capacity.
A look towards History of the Use of Artificial Intelligence in Healthcare
The advent of artificial intelligence in medicine corresponds to the advent of artificial intelligence in the modern era. Artificial intelligence has been used in medicine since the 1950s when physicians first attempted to improve their diagnosis using computer-aided programs. Gunn pioneered the use of artificial intelligence technology in surgery in 1976 when he investigated the possibility of using computer analysis to diagnose acute abdominal pain.
Dermatological AI is still in its early stages, far behind radiological AI. Radiological AI is on top in treating small pulmonary nodules and lung cancer. AI can now follow up on and judge nodule changes at different times, starting with the location of presumed nodules, including the summary of its shape and the nodule identification, benign and malignant judgments. These minute differences are difficult to distinguish, but AI is capable of doing so. The use of AI in radiology will greatly narrow the gap between doctors at different levels and doctors at different levels of hospitals, as well as improve diagnosis accuracy. The use of AI in the field of radiology has been exemplary in dermatology, and its perks have greatly aided development.
Applications of AI in Dermatology
Keratinocyte Carcinomas and Melanoma
A sizable and expanding body of research shows that using AI to distinguish benign nevi from melanoma is now successful. The fundamental concept behind these apps is the ability to dissect photographs of lesions into their individual pixels for study, whether they are dermatoscopic or non-dermatoscopic. AI has been applied in areas other than photo identification. Applications can process numerical data in different sequences and extract trends in addition to processing picture pixels.
Ulcer Assessment
The body of knowledge about applications for diabetes and pressure ulcers is expanding. As a result, the vast majority of studies present techniques for enhancing wound assessments through image recognition. Applications that can measure precisely where a wound end and to distinguish between the various tissue types involved have been reported in articles.
Psoriasis and Other Inflammatory Skin Diseases
There are numerous original research articles on the use of AI for inflammatory dermatoses. The majority of these works to date have concentrated on enhancing image recognition techniques for psoriasis categorization.
Predicting Skin Sensitization Substances
Additionally, there is growing research into the use of AI to reduce skin-irritating chemical exposure. Although this use of AI has the potential to affect the entire population, extensive technological and clinical validation studies are required.
Novel Applications in Pathology and Gene Expression Profiling
Applications that can automatically analyze and classify histological images have been presented.
The Future of Dermatology AI: Scope and Challenges
Currently, image data for various skin diseases are insufficient, information sharing between sources is limited, and skin image quality varies. Medical and AI researchers come from a variety of backgrounds, and a multidisciplinary approach in computer science, biomedical, and medical sciences are required. Dermatological AI can only recognize a few specific skin diseases, despite the fact that dermatological conditions vary greatly. It will be difficult to train AI to recognize and classify a long and diverse list of dermatological disorders with varying clinical presentations. AI diagnosis also raises legal, ethical, and data privacy concerns that must be addressed.
Conclusion
In the field of dermatology, artificial intelligence is rapidly gaining traction. It has the potential to transform patient care, particularly in terms of improving the sensitivity and accuracy of screening for skin lesions, including malignancies. However, AI research requires clinical and photographic data from all skin types, and the data must be generated through improved international skin imaging collaboration for more in-depth studies.
In conclusion, physicians should not view AI as a potential threat to their skills; rather, it has the potential to be an adjunct to clinical practice in the coming years. Understanding AI concepts will assist practicing dermatologists in providing better skin care.
