AI Article Synopsis

  • * The integration of artificial intelligence (AI), machine learning (ML), and deep learning (DL) into healthcare has shown significant potential in predicting cancer risks and improving survival rates.
  • * These technologies enable precise diagnosis and personalized treatment plans, ultimately improving patients' quality of life and transforming cancer care for conditions like diffuse large B-cell lymphoma (DLBCL) and various tumors.

Article Abstract

Cancer is one of the most devastating, fatal, dangerous, and unpredictable ailments. To reduce the risk of fatality in this disease, we need some ways to predict the disease, diagnose it faster and precisely, and predict the prognosis accurately. The incorporation of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms into the healthcare system has already proven to work wonders for patients. Artificial intelligence is a simulation of intelligence that uses data, rules, and information programmed in it to make predictions. The science of machine learning (ML) uses data to enhance performance in a variety of activities and tasks. A bigger family of machine learning techniques built on artificial neural networks and representation learning is deep learning (DL). To clarify, we require AI, ML, and DL to predict cancer risk, survival chances, cancer recurrence, cancer diagnosis, and cancer prognosis. All of these are required to improve patient's quality of life, increase their survival rates, decrease anxiety and fear to some extent, and make a proper personalized treatment plan for the suffering patient. The survival rates of people with diffuse large B-cell lymphoma (DLBCL) can be forecasted. Both solid and non-solid tumors can be diagnosed precisely with the help of AI and ML algorithms. The prognosis of the disease can also be forecasted with AI and its approaches like deep learning. This improvement in cancer care is a turning point in advanced healthcare and will deeply impact patient's life for good.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9717523PMC
http://dx.doi.org/10.7759/cureus.31008DOI Listing

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