• How Artificial intelligence is Transforming Cancer Care
  • Javad Yaghmoorian Khojini,1 Fatemeh Mohammad-Rafiei,2 Seyed Mehdi Kalantar,3,*
    1. Master student of medical biotechnology/ Department of Medical Biotechnology/ School of Medicine/ Shahid Sadoughi University of Medical Sciences/ Yazd/ Iran
    2. Master student of medical biotechnology/ Department of Medical Biotechnology/ School of Medicine/ Shahid Sadoughi University of Medical Sciences/ Yazd/ Iran
    3. Department of Medical Genetics, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran


  • Introduction: Nowadays numerous anticancer curatives are available but choosing a cure for cancer continues to be a delicate task. Still, with the arrival of artificial intelligence (AI), there's a new stopgap for advanced cancer care. AI has the implicit to revise the way we diagnose and treat cancer. It can dissect vast quantities of patient data, identify patterns and trends, and develop individualized treatment plans as well as accelerate medicine discovery and development. AI-powered imaging is helping doctors to identify cancerous cells more directly and snappily, while machine algorithms are being used to prognosticate patient issues and present treatment opinions. AI has the implicit to revise cancer care by perfecting patient issues, reducing costs, and accelerating exploration and development. Still, ethical considerations must be taken into account to ensure that the benefits of AI are balanced against patient safety. This paper will explore the part of AI in cancer care.
  • Methods: We investigated Scopus and PubMed databases from 2018 through 2022 with a keyword combination of “cancer/AI applications” and “cancer artificial intelligence”. Based on the aim of the search, outcomes of interest included studies investigating the role of AL in diagnosing, treating, and preventing cancer.
  • Results: In cancer, AI can be applied to different types of data, including medical images, genomic and proteomic data, exploring electronic health records (EHRs), and medicine discovery and development. One of the most promising areas of AI in cancer is medical imaging. AI offers an excellent progressive occasion to medical imaging technology. It is grounded on computational models and bioinformatics- algorithms that can determine any abnormal cellular growth and natural changes in the body. AI- image analysis can be applied to different types of medical images, including CT, MRI, and positron emigration tomography (PET) to describe changes, shape, and texture that may not be visible. They can also be used to prognosticate treatment response and prognostic on changes in size and metabolic exertion. For illustration, algorithms have been used to describe lung nodes on CT with high delicacy and prognosticate response to chemotherapy. Another area of AI in cancer is genomics. Genomic data provides precious information about the molecular characteristics and can be used to prognosticate treatment response and prognostic. This analysis can be applied to genomic data, including gene expression, mutations, numbers of variations, and epigenetic variations that can be used to identify new remedial targets based on the molecular characteristics of cells. Several AI- genomic analysis tools have been developed for various types of cancer. For instance, algorithms have been used to prognosticate response to immunotherapy in carcinoma on the presence of specific mutations. AI- EHR analysis is another area that contains precious information about case demographics, medical history, and treatment issues that be used to prognosticate treatment response and prognostic. In this case, Machine algorithms have been used to prognosticate the threat of colorectal cancer on EHR data. AI is also making a significant impact in medicine discovery and development. Still, there are also ethical considerations that must be taken when using AI in cancer care. One of the main one is patient safety. AI algorithms must be designed to cover patient data and ensure that it isn't misused or participated without the case's satisfaction. In addition, there's a threat that AI algorithms may be make incorrect prognostications, which could have serious consequences for patients.
  • Conclusion: In conclusion, AI is an evolving field that holds great hope for cancer diagnosis, treatment, and monitoring. AI-tools have shown promising results in preclinical and clinical studies. The integration of multiple AI tools into the medical field is likely to give us the most accurate and individualized approach to cancer care.
  • Keywords: Cancer, AI, machine learning, Genomic data, cancer artificial intelligence