Scholarlink

Cancer is a global concern that has far-reaching consequences for health, society, and the economy. In 2022, there were an anticipated 20 million new cancer cases and 9.7 million cancer deaths worldwide. Cancer affects 1 out of 5 people in their lifespan, and 1 out of 9 men and 1 in 12 women die due to of it. Ninety percent of death occurring due to advanced or metastasize cancer, this emphasizes the importance of early diagnosis and treatment. In reality, early detection, diagnosis, and treatment improve quality of life and overall survival for almost cancers. Concurrently, scientists are working hard in identifying and developing basic to complex cancer biomarkers to tackle and breakdown carcinogenesis and develop early diagnostic and potential treatment regimen. Recent advancement in Artificial inelegancy (AI) and Machine Learning (ML) helps to improve the accuracy, speed, and personalization of cancer detection and prediction. These approaches are currently being implemented in cancer diagnosis process, ranging from imaging analysis to genetic profiling, providing unparalleled accuracy. AI helps to identify the tumor specific genetic variants and molecular markers and allowing to tailored more accurate diagnosis and treatment regimen. ML algorithms help to predict which genetic biomarker is most likely to cause cancer progression, directing targeted therapy decisions. Furthermore, AI-powered liquid biopsy methods advance non-invasive cancer screening by detecting circulating tumor DNA and other blood-based biomarkers. These advances enable early diagnosis and real-time monitoring of cancer progression and treatment response. Nevertheless, these advantages, problems persist, such as the requirement for big, diverse datasets to train AI/ML models, concerns about algorithm openness, and the integration of AI/ML approaches into clinical practices. Regulatory and ethical concerns must also be addressed, notably regarding data privacy and the potential of algorithmic prejudice. In this book chapter, we emphasized the importance of AI and ML in improving cancer detection by providing more accurate, faster, and tailored diagnostic choices.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial Intelligence and Machine Learning in Improving Diagnostic Accuracy

  • 章节

  • 发表日期：

    2025年

查看全文
  • Srikruthi Neriyanuri,
  • Srivatsa Venkata Sai Palepu,
  • Prerna Vats,
  • Bhavika Baweja,
  • Rajeev Nema,
  • Monisha Banerjee,
  • Atar Singh Kushwah

摘要

Cancer is a global concern that has far-reaching consequences for health, society, and the economy. In 2022, there were an anticipated 20 million new cancer cases and 9.7 million cancer deaths worldwide. Cancer affects 1 out of 5 people in their lifespan, and 1 out of 9 men and 1 in 12 women die due to of it. Ninety percent of death occurring due to advanced or metastasize cancer, this emphasizes the importance of early diagnosis and treatment. In reality, early detection, diagnosis, and treatment improve quality of life and overall survival for almost cancers. Concurrently, scientists are working hard in identifying and developing basic to complex cancer biomarkers to tackle and breakdown carcinogenesis and develop early diagnostic and potential treatment regimen. Recent advancement in Artificial inelegancy (AI) and Machine Learning (ML) helps to improve the accuracy, speed, and personalization of cancer detection and prediction. These approaches are currently being implemented in cancer diagnosis process, ranging from imaging analysis to genetic profiling, providing unparalleled accuracy. AI helps to identify the tumor specific genetic variants and molecular markers and allowing to tailored more accurate diagnosis and treatment regimen. ML algorithms help to predict which genetic biomarker is most likely to cause cancer progression, directing targeted therapy decisions. Furthermore, AI-powered liquid biopsy methods advance non-invasive cancer screening by detecting circulating tumor DNA and other blood-based biomarkers. These advances enable early diagnosis and real-time monitoring of cancer progression and treatment response. Nevertheless, these advantages, problems persist, such as the requirement for big, diverse datasets to train AI/ML models, concerns about algorithm openness, and the integration of AI/ML approaches into clinical practices. Regulatory and ethical concerns must also be addressed, notably regarding data privacy and the potential of algorithmic prejudice. In this book chapter, we emphasized the importance of AI and ML in improving cancer detection by providing more accurate, faster, and tailored diagnostic choices.