Mapping the landscape of artificial intelligence in skin cancer research: a bibliometric analysis

Abstract

ObjectiveArtificial intelligence (AI), with its potential to diagnose skin cancer, has the potential to revolutionize future medical and dermatological practices. However, the current knowledge regarding the utilization of AI in skin cancer diagnosis remains somewhat limited, necessitating further research. This study employs visual bibliometric analysis to consolidate and present insights into the evolution and deployment of AI in the context of skin cancer. Through this analysis, we aim to shed light on the research developments, focal areas of interest, and emerging trends within AI and its application to skin cancer diagnosis.MethodsOn July 14, 2023, articles and reviews about the application of AI in skin cancer, spanning the years from 1900 to 2023, were selected from the Web of Science Core Collection. Co-authorship, co-citation, and co-occurrence analyses of countries, institutions, authors, references, and keywords within this field were conducted using a combination of tools, including CiteSpace V (version 6.2. R3), VOSviewer (version 1.6.18), SCImago, Microsoft Excel 2019, and R 4.2.3.ResultsA total of 512 papers matching the search terms and inclusion/exclusion criteria were published between 1991 and 2023. The United States leads in publications with 149, followed by India with 61. Germany holds eight positions among the top 10 institutions, while the United States has two. The most prevalent journals cited were Cancer, the European Journal of Cancer, and Sensors. The most frequently cited keywords include “skin cancer”, “classification”, “artificial intelligence”, and “deep learning”.ConclusionsResearch into the application of AI in skin cancer is rapidly expanding, and an increasing number of scholars are dedicating their efforts to this field. With the advancement of AI technology, new opportunities have arisen to enhance the accuracy of skin imaging diagnosis, treatment based on big data, and prognosis prediction. However, at present, the majority of AI research in the field of skin cancer diagnosis is still in the feasibility study stage. It has not yet made significant progress toward practical implementation in clinical settings. To make substantial strides in this field, there is a need to enhance collaboration between countries and institutions. Despite the potential benefits of AI in skin cancer research, numerous challenges remain to be addressed, including developing robust algorithms, resolving data quality issues, and enhancing results interpretability. Consequently, sustained efforts are essential to surmount these obstacles and facilitate the practical application of AI in skin cancer research.

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“…If the dataset is too small and includes inaccurate annotation or inequities, there is a risk of producing incorrect or biased results [14,33,36]. AI models are trained and evaluated on datasets that cannot include the entire population and complex clinical environments so they might have inherent biases [37,39]. In addition, the inevitable intrinsic uncertainties of medical interpretations are not usually considered to train AI models [40].…”

“…As a result, the performance and reliability of AI models affected by observer variability can be underrated in clinical decision-making. Therefore, in the current state, AI cannot replace the role of clinicians in medical fields, and rigorous validation through real-world clinical trials is still required to ensure generalizability, robustness, reliability, and safety [33,36,37]. So far, AI can offer good support for the decision-making of experts and understanding the diagnostic results of non-experts [14,32,36,37,41].…”

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