{
  "abstract": "Background Artificial Intelligence (AI) is rapidly permeating and reshaping many fields including skin cancer; a growing research field, particularly through computer vision and image classification; one of the most common forms worldwide, with a significant increase in incidence over the last few decades. Early and accurate detection of this type of cancer can result in better prognoses and less invasive treatments for patients.AI research is important in defining the best practices and scope of integrating AI-enabled technologies within a clinical setting.The FDA has not approved any medical advice or algorithms based on AI in the field of dermatology, however, in the European market, foto-finder mole-analyzer pro was endorsed to act as AI which won’t work on skin type IV and over and can’t outdo attending dermatologists in skin cancer detection.The application of AI in dermatology has the potential to revolutionize early detection of skin cancer. However, it is imperative to validate and collaborate with healthcare professionals to ensure its clinical effectiveness and safety.Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training data sets.AI has the potential to supplement dermatologists’ diagnostic and treatment capabilities in what is known as augmented intelligence (AuI).The practical utility of AI-assisted diagnosis in a clinical environment is still largely unknown.Objectives to analyse the characteristics and trends of AI skin cancer publications from dermatology journals. To analyze and predict the captured images of the commonest skin cancer types submitted by patient’s smartphones, to distinguish and flag higher versus lower risks pigmented lesions.Methods A systematic literature was conducted by searching PubMed, Scopus, Embase, and Web of Science, encompassing studies published until April 4th, 2023. Study selection, data extraction, and critical appraisal were carried out by two independent reviewers. Results were subsequently presented through a narrative synthesis.Results Through the search, 760 studies were identified in four databases, with only 18 studies were selected, focusing on developing, implementing, and validating systems to detect, diagnose, and classify skin cancer in clinical settings. This review covers descriptive analysis, data scenarios, data processing and techniques, study results and perspectives, and physician diversity, accessibility, and participation.Conclusion The field of skin cancer detection offers a compelling use case for the application of AI within the realm of image-based diagnostic medicine. Through the analysis of large datasets, AI algorithms can classify clinical or dermoscopic images with remarkable accuracy. Although these AI-based applications can operate both autonomously and under human supervision, the best results are achieved through a collaborative approach that pulls the proficiency of both AI and human experts as AI models lack robustness to simple data variations, thus proven inadequate in real-world dermatologic practice performance which acts as a barrier to achieving clinical promptness. The application of AI in dermatology has the potential to revolutionize early detection of skin cancer. However, it is imperative to validate and collaborate with healthcare professionals to ensure its clinical effectiveness and safety.",
  "authors": [
    {
      "affiliations": [
        "Liverpool, UK"
      ],
      "name": "Ebtisam Elghblawi"
    }
  ],
  "title": "016 The role of artificial intelligence in overdiagnosis; AI and skin cancer",
  "uid": "3bf2bc2b-eb67-549e-8a76-fdd008b0fad2"
}