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Background <p>Pancreatic cancer is the deadliest form of cancer with a low survival rate due to its late diagnosis. Hence, early detection and swift intervention are very crucial for its management. However, the current diagnostic markers lack sufficient precision, and the effectiveness of treatment options remains imprecise, emphasizing the need for more advanced approaches.</p> Main body <p>Artificial intelligence (AI) technology enables rapid detection of high-risk groups for pancreatic cancer using various techniques such as medical imaging, pathological examination, biomarkers, and other methods, facilitating early detection of pancreatic cancer. Simultaneously, AI algorithms may also be used to forecast the duration of survival, the likelihood of recurrence, the cancer metastasis, and the response to treatment, all of which can impact the prognosis. Moreover, AI is applied in handling cancer cases in oncology departments, pancreatic cancer in particular, and creating computer-assisted diagnostic systems.</p> Conclusion <p>The end-to-end application of AI in pancreatic cancer management calls for multidisciplinary collaboration among doctors, laboratory scientists, data analysts, and engineers. Despite its limitations, its powerful computational capabilities will soon be crucial for combating pancreatic cancer and other health conditions.</p>

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Advancing pancreatic cancer management: the role of artificial intelligence in diagnosis and therapy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月7日

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  • Dilber Uzun Ozsahin,
  • Natacha Usanase,
  • Ilker Ozsahin

摘要

Background

Pancreatic cancer is the deadliest form of cancer with a low survival rate due to its late diagnosis. Hence, early detection and swift intervention are very crucial for its management. However, the current diagnostic markers lack sufficient precision, and the effectiveness of treatment options remains imprecise, emphasizing the need for more advanced approaches.

Main body

Artificial intelligence (AI) technology enables rapid detection of high-risk groups for pancreatic cancer using various techniques such as medical imaging, pathological examination, biomarkers, and other methods, facilitating early detection of pancreatic cancer. Simultaneously, AI algorithms may also be used to forecast the duration of survival, the likelihood of recurrence, the cancer metastasis, and the response to treatment, all of which can impact the prognosis. Moreover, AI is applied in handling cancer cases in oncology departments, pancreatic cancer in particular, and creating computer-assisted diagnostic systems.

Conclusion

The end-to-end application of AI in pancreatic cancer management calls for multidisciplinary collaboration among doctors, laboratory scientists, data analysts, and engineers. Despite its limitations, its powerful computational capabilities will soon be crucial for combating pancreatic cancer and other health conditions.