A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges
A substantial body of research has been published on artificial intelligence applications in skin cancer influenced by the latter's rising rates, the scarcity of specialized healthcare professionals, and rapid advancements in automated diagnosis and treatment methods. We present a comprehensive...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Healthcare Analytics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442523001260 |
| _version_ | 1797671410145951744 |
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| author | Eman Rezk May Haggag Mohamed Eltorki Wael El-Dakhakhni |
| author_facet | Eman Rezk May Haggag Mohamed Eltorki Wael El-Dakhakhni |
| author_sort | Eman Rezk |
| collection | DOAJ |
| description | A substantial body of research has been published on artificial intelligence applications in skin cancer influenced by the latter's rising rates, the scarcity of specialized healthcare professionals, and rapid advancements in automated diagnosis and treatment methods. We present a comprehensive review employing text mining to identify key themes of artificial intelligence in skin cancer diagnosis and treatment research. Our text mining model uncovers nine key topics, including dermatological data, machine and deep learning methods, segmentation, data generation, melanoma, basal cell carcinoma, model validation, and treatment. We extensively review the literature on each topic to offer valuable insights and highlight research gaps. Our findings indicate a need for a comprehensive and diverse dataset that includes lesion images, clinical data, and treatment information. In addition, our topic analysis ranks deep learning-based diagnosis as the top topic, followed by data generation and melanoma diagnosis. These insights demonstrate the bias towards deep learning methods and the shortage of studies on rare and precancerous skin lesions. Despite the gaps defined, artificial intelligence can be utilized for triage, initial screening, providing second opinion in diagnosing complex cases, and educational purposes. Additionally, artificial intelligence models can enhance patient outcomes through early diagnosis, treatment recommendation, and response prediction. |
| first_indexed | 2024-03-11T21:15:07Z |
| format | Article |
| id | doaj.art-934ad4e4489e4917898199035d4c89b1 |
| institution | Directory Open Access Journal |
| issn | 2772-4425 |
| language | English |
| last_indexed | 2024-03-11T21:15:07Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj.art-934ad4e4489e4917898199035d4c89b12023-09-29T04:45:32ZengElsevierHealthcare Analytics2772-44252023-12-014100259A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challengesEman Rezk0May Haggag1Mohamed Eltorki2Wael El-Dakhakhni3School of Computational Science and Engineering, McMaster University, 1280, Main St W, Hamilton, ON, L8S 4L7, Canada; Corresponding author.Institute for Multi-Hazard Systemic Risk Studies (INTERFACE), McMaster University, 1280 Main St W, Hamilton, ON, L8S 4L7, CanadaFaculty of Health Sciences, McMaster University, 1200 Main St W, Hamilton, ON, L8N 3Z5, CanadaSchool of Computational Science and Engineering, McMaster University, 1280, Main St W, Hamilton, ON, L8S 4L7, Canada; Institute for Multi-Hazard Systemic Risk Studies (INTERFACE), McMaster University, 1280 Main St W, Hamilton, ON, L8S 4L7, CanadaA substantial body of research has been published on artificial intelligence applications in skin cancer influenced by the latter's rising rates, the scarcity of specialized healthcare professionals, and rapid advancements in automated diagnosis and treatment methods. We present a comprehensive review employing text mining to identify key themes of artificial intelligence in skin cancer diagnosis and treatment research. Our text mining model uncovers nine key topics, including dermatological data, machine and deep learning methods, segmentation, data generation, melanoma, basal cell carcinoma, model validation, and treatment. We extensively review the literature on each topic to offer valuable insights and highlight research gaps. Our findings indicate a need for a comprehensive and diverse dataset that includes lesion images, clinical data, and treatment information. In addition, our topic analysis ranks deep learning-based diagnosis as the top topic, followed by data generation and melanoma diagnosis. These insights demonstrate the bias towards deep learning methods and the shortage of studies on rare and precancerous skin lesions. Despite the gaps defined, artificial intelligence can be utilized for triage, initial screening, providing second opinion in diagnosing complex cases, and educational purposes. Additionally, artificial intelligence models can enhance patient outcomes through early diagnosis, treatment recommendation, and response prediction.http://www.sciencedirect.com/science/article/pii/S2772442523001260Artificial intelligenceMachine learningDeep learningSkin cancerDiagnosisTreatment |
| spellingShingle | Eman Rezk May Haggag Mohamed Eltorki Wael El-Dakhakhni A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges Healthcare Analytics Artificial intelligence Machine learning Deep learning Skin cancer Diagnosis Treatment |
| title | A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges |
| title_full | A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges |
| title_fullStr | A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges |
| title_full_unstemmed | A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges |
| title_short | A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges |
| title_sort | comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment emerging trends and challenges |
| topic | Artificial intelligence Machine learning Deep learning Skin cancer Diagnosis Treatment |
| url | http://www.sciencedirect.com/science/article/pii/S2772442523001260 |
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