Artificial Intelligence (AI) continues to carve out its place in numerous industries, and dermatology is no exception. Its applications in diagnosing and treating skin diseases could revolutionize the practice. However, the complexity of these tasks and the inherent variability in the way different skin conditions manifest pose considerable challenges.
While a prevalent question arises regarding the number of images necessary to train an AI to accurately diagnose a dermatological disease, this isn’t an easily quantifiable element. The number of images required is dependent on various factors. This includes the number of different diseases (or classes) the AI is trained on, the architecture of the AI, and the inherent variation in disease presentation across different skin tones, body parts, and angles. The use of algorithms that have been trained on one set of images and then fine-tuned with new datasets could potentially increase performance, especially when accommodating for different variables.
The Limitations of AI and the Need for Human Oversight
Current AI algorithms have varying degrees of accuracy, and while there’s potential for improvement, their dependence on human oversight remains critical. Algorithms can be sensitive to minute variations undetectable by the human eye. For instance, algorithms trained on images taken with an iPhone might perform less accurately when presented with images taken by an Android. Lighting conditions, angles, and other variables could also affect performance. Standardizing imaging could be a potential solution; however, the variability within dermatology data poses a challenge.
Considering the current limitations, it’s unlikely that AI will replace the role of dermatologists in the foreseeable future. The diagnosis and treatment of skin conditions aren’t solely dependent on visual evaluation; they involve a complex combination of clinical history, diagnostic tests, patient counseling, and surgical interventions.
AI could potentially serve as an augmentation tool, assisting in areas where decision-making needs additional support. For example, AI could provide feedback to patients on the quality of their photo before a telehealth visit, ensuring more precise and helpful images are sent in. Around 40% of patient images aren’t of the best quality for making a diagnosis; an AI tool could greatly enhance this aspect of telemedicine.
The Future of AI in Dermatology
In the wake of the remote era and the increased use of telehealth, the application of AI in dermatology has become even more evident. There’s an increasing need for tools that could help examine lesions better at home, triage patients more efficiently, and perhaps even enhance decision-making in primary care settings where dermatological expertise may be lacking.
While it’s challenging to predict the exact trajectory AI will take in dermatology over the next 5, 10, or 15 years, the future appears promising. A significant focus is likely to be placed on enhancing capabilities rather than creating autonomous systems. Ideally, this could involve helping patients take better dermatology photos, triaging and fast-tracking patients with severe diseases more efficiently, and aiding non-specialists in their decision-making process. Although the technology isn’t at the point of creating an autonomous system just yet, the enhancement of current practices with AI holds significant potential.
The evolution of AI in dermatology is not about replacing dermatologists but rather providing them with a suite of innovative tools that augment their abilities and enhance patient care. While there is still a long way to go, the progress made so far suggests a future where AI plays a pivotal role in dermatological practice.
