Annals of Medical and Surgical Dermatology

ABSTRACT

Skin disorders present a notable health challenge, necessitating precise and prompt identification to achieve the best outcomes for patients. The use of artificial intelligence (AI) shows great promise in transforming the field of dermatology by enhancing diagnostic precision, effectiveness, and availability of healthcare. This review paper delves into the potential and hurdles of employing AI in dermatology. AI-driven image analysis tools offer the potential for timely and precise detection of skin cancers and other skin conditions. Moreover, AI has the capability to streamline mundane duties, allowing dermatologists more time to focus on intricate cases and patient interactions. When combined with tele-dermatology, AI can substantially enhance healthcare accessibility for individuals in underserved regions. Ultimately, AI plays a pivotal role in advancing personalized medicine by examining extensive patient information to customize treatment strategies. Significant challenges must be overcome for the responsible and effective implementation of AI. Data bias can result in inaccurate diagnoses, especially for patients with darker skin tones. Addressing this issue entails utilizing diverse datasets that encompass a wide range of skin types and conditions. The absence of interpretability in numerous AI models may impede trust and acceptance among clinicians. Employing Explainable AI (XAI) techniques is vital to establish trust and enhance transparency. Lastly, the seamless integration of AI tools into clinical workflows is imperative. By fostering cooperation among dermatologists, AI developers, and data scientists, we can tackle these challenges and unleash the full potential of AI to revolutionize dermatological care. This will pave the way for a future with more precise diagnoses, personalized treatment strategies, and enhanced access to quality care for all patients.

Article History

KEYWORDS

1. Dermatological diagnosis
2. Skin conditions
3. Computational technology
4. Medical imaging
5. Healthcare access