Current Developments of Artificial Intelligences in Head and Neck Cancer Histopathological Images Analysis

The rapid progress in artificial intelligence (AI) technologies has made a significant impact on medical image analysis, particularly in cancer diagnosis. The integration of AI algorithms into histopathological image analysis holds promise for enhancing precision and efficiency in cancer diagnosis. This review provides an in-depth analysis of recent developments in AI methodologies, including deep learning approaches applied to the interpretation of histopathological images specific to head and neck cancer (HNC). Furthermore, it discusses the challenges and opportunities associated with the implementation of AI in this domain, such as data variability, interpretability, and clinical integration. Through this overview of current research findings, we aim to offer valuable insights on the state-of-the-art AI technologies, their potential impact on clinical practice, and future directions for advancing the field of HNC histopathological image analysis.

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Publication Details

Journal
Korean Journal of Otorhinolaryngology - Head and Neck Surgery
Published
2024-08-05
DOI
https://doi.org/10.3342/kjorl-hns.2024.00010
Citations
1
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.35

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article

Current Developments of Artificial Intelligences in Head and Neck Cancer Histopathological Images Analysis

Young Chan Lee
1 citations
Korean Journal of Otorhinolaryngology - Head and Neck Surgery
AI in cancer detection
0.35
article

Current Developments of Artificial Intelligences in Head and Neck Cancer Histopathological Images Analysis

Young Chan Lee
article en
1 citations

Abstract

The rapid progress in artificial intelligence (AI) technologies has made a significant impact on medical image analysis, particularly in cancer diagnosis. The integration of AI algorithms into histopathological image analysis holds promise for enhancing precision and efficiency in cancer diagnosis. This review provides an in-depth analysis of recent developments in AI methodologies, including deep learning approaches applied to the interpretation of histopathological images specific to head and neck cancer (HNC). Furthermore, it discusses the challenges and opportunities associated with the implementation of AI in this domain, such as data variability, interpretability, and clinical integration. Through this overview of current research findings, we aim to offer valuable insights on the state-of-the-art AI technologies, their potential impact on clinical practice, and future directions for advancing the field of HNC histopathological image analysis.

Korean Journal of Otorhinolaryngology - Head and Neck SurgeryVol. 67(9)
Kyung Hee University (KR), Gangdong University (KR), Kyung Hee University Hospital at Gangdong (KR)
National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 35%
AI in cancer detection
0.35
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