人工智能和机器学习模型在口腔癌及口腔潜在恶性疾病早期检测中的潜在作用
The Potential Role of AI- and Machine Learning Models in the Early Detection of Oral Cancer and Oral Potentially Malignant Disorders.
作者信息
Groenevelt Cynthia, Fransen Mirjam, Poell Jos, Dusseljee-Peute Linda
机构信息
Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, eHealth Living and Learning Lab Amsterdam, Amsterdam, The Netherlands.
Amsterdam Public Health Research Institute, Quality of Care, Digital Health, Amsterdam, The Netherlands.
出版信息
Stud Health Technol Inform. 2025 May 12;326:147-151. doi: 10.3233/SHTI250257.
INTRODUCTION
Artificial Intelligence (AI) is playing an increasing role in advancing diagnostic processes and decision-making in healthcare. In the early detection of oral cancer and oral potentially malignant disorders (OPMDs), its role is still being explored. This paper evaluates advancements in AI applications for the early detection of oral cancer and OPMDs.
METHODS
A narrative umbrella review was performed on reviews that explicitly evaluated non-invasive diagnostic techniques combined with AI-modalities or machine learning techniques in the early detection of oral cancer and OPMDs.
RESULTS
Key findings of eight studies published between 2015 and 2024 demonstrate various AI-modalities and their diagnostic accuracy, accessibility and affordability, limitations and challenges and ethical and regulatory needs.
CONCLUSION
AI- and deep learning models hold promise in improving the early detection of oral cancer and OPMDs, offering high diagnostic accuracy that can significantly enhance patient outcomes. Challenges such as limited explainability and ethical concerns must be addressed to fully integrate these technologies into daily clinical practice.
引言
人工智能（AI）在推动医疗保健领域的诊断流程和决策制定方面发挥着越来越重要的作用。在口腔癌和口腔潜在恶性疾病（OPMDs）的早期检测中，其作用仍在探索之中。本文评估了人工智能应用在口腔癌和OPMDs早期检测方面的进展。
方法
对明确评估非侵入性诊断技术与人工智能模式或机器学习技术相结合用于口腔癌和OPMDs早期检测的综述进行叙述性综合评价。
结果
2015年至2024年间发表的八项研究的主要发现展示了各种人工智能模式及其诊断准确性、可及性和可负担性、局限性和挑战以及伦理和监管需求。
结论
人工智能和深度学习模型有望改善口腔癌和OPMDs的早期检测，提供高诊断准确性，可显著改善患者预后。必须解决诸如可解释性有限和伦理问题等挑战，以便将这些技术充分整合到日常临床实践中。
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