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The vital contribution of artificial intelligence in cancer

Artificial intelligence (AI) is the intelligence displayed by machines that are created by humans.  Computer science, neurophysiology, cybernetics, psychology, and linguistics are all part of the field. The Dartmouth Conference in 1956 is thought to be the birth of AI. After decades of rapid advancement, the definition of AI has broadened, and it now encompasses artificial neural networks, deep learning, machine learning, and other technologies. Deep learning is a form of AI that can extract features from large volumes of data automatically. Furthermore, deep learning can recognise information in photos that the human eye cannot. Researchers are discovering new methods to use AI’s capabilities in the healthcare area as it continues to grow. AI has played a key role in the diagnosis, decision-making, and treatment of chronic diseases, particularly in cancer research.

Cancer detection

AI could help detect precancerous tumours in tissues, increasing the sensitivity of cancer screening procedures. Radiologists may be aided by AI-powered tools in visually assessing images and finding these concerning lesions. This technology saves radiologists time while also allowing them to detect small tumours that would otherwise go undetected. Over 13,000 images of colorectal cancer were collected from 8,803 patients and 13 independent cancer facilities in China, Germany, and the United States by the researchers. The researchers then created a machine learning software using images chosen at random by technicians. The algorithm can recognise images of colorectal cancer, which is one of the most common causes of cancer-related deaths in Europe and the United States, according to researchers. The team of researchers compared the machine learning technique to pathologists’ work after constructing a performance measurement tool. According to the study, the average pathologist scored around 0.969 for accuracy in identifying colorectal cancer, while the programme scored 0.98, indicating that the programme was somewhat more accurate than pathologists’ manual data. Researchers anticipate that the findings will motivate pathologists to employ more pre-screening technology in order to speed up diagnosis.

AI to forecast cancer progression

Cancer prognosis can be aided by AI. AI can detect pre-existing cancers and identify those who are at high risk of acquiring the disease before it starts.  This allows clinicians to keep a close eye on these patients and act promptly if the situation warrants it. Apart from cancer diagnosis, AI can also forecast how cancers spread and evolve, which could allow physicians to devise successful treatments for particular patients as well as influence the treatment process in the future.  Furthermore, early intervention increases the chances of a patient’s life since cancer is destroyed before it has a chance to develop a resistant immune response.

Drug development

Machine learning and other AI technologies can significantly improve the current mode of anticancer drug research. AI can quickly deduce how cancer cells gain resistance to anticancer treatments, which can aid drug development and dosage adjustments. The identification of tumour neoantigens and the efficacy of tumour immunotherapy are both improved by AI. It can assist radiologists in mapping target areas or planning radiation treatment plans automatically.  AI can regulate the usage of chemotherapy drugs and forecast their tolerance, allowing the chemotherapy regimen to be optimised.


Cancer is a chronic yet curable illness, when detected early and healthcare expenditures are affordable. This is alarming, as our healthcare systems are already overburdened. The pandemic has shown how, as new and emerging diseases emerge, healthcare systems’ reactions to chronic diseases may take a backseat. This is where cutting-edge technology such as AI can make a significant difference. While AI has made considerable progress in a variety of sectors, it has recently made inroads into healthcare, particularly in the medical oncology field.

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