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Using AI in the fight against cancer

A patient’s tumour size and stage, type, and intensity of radiotherapy, smoking status, weight and age were the most important factors.

Thousands of lives could be saved as Japan begins to actively promote the HPV vaccine -- which can prevent cervical cancer -- after a decade of misinformation and weak policy left inoculation rates dismally low, advocates say
Thousands of lives could be saved as Japan begins to actively promote the HPV vaccine -- which can prevent cervical cancer -- after a decade of misinformation and weak policy left inoculation rates dismally low, advocates say - Copyright AFP Kazuhiro NOGI
Thousands of lives could be saved as Japan begins to actively promote the HPV vaccine -- which can prevent cervical cancer -- after a decade of misinformation and weak policy left inoculation rates dismally low, advocates say - Copyright AFP Kazuhiro NOGI

Artificial intelligence, in the form of queried databases, is helping to tackle certain cancers. Algorithms have been developed to cross-reference a patient’s medical records, habits and genetic information to spot any early signs of cancer.

With cancer, the key problem is about the late diagnosis of cancer and the argument of using artificial intelligence is to identify those members of the population who are at greatest risk and to then bring them in earlier for screening.

A pilot has been developed in the U.K., where the focus is particularly with prostate, lung and bowel cancer, and then you can undertake procedures like surgery or administer treatment sooner in order to increase survival rates.

Similarly, a U.S. study, published in 2022, found that a machine learning algorithm trained to predict cancer outcomes zeroed in to finds the prostate on male patients and successfully outlines any cancer-suspicious areas without any human supervision.

What this type of technology seeks to do is, if clinicians can detect diseases early, then lives can be saved. It follows that earlier doctors can administer treatment, the greater the survival rate but also the reduction of burden on the health services going forward.

Many health systems have at their disposal enormous databases of information. Consequently, there are things like genetic profiles from patient records and screening people for lifestyle and getting those people sufficiently early in order to allow medical treatment to happen sooner.

There two waves of artificial intelligence, the first one is to interpret data bases, so the health services can build up a huge library of information about patients. This will be a repository of contains about their health status, their body mass, their genetic profile, and to plough through the millions of data points does take machines to do that and over time the technique and machine learning means that the more cases that the intelligence gets right, then the better it becomes going forward.

Such an approach has found, for example, that a patient’s tumour size and stage, type, and intensity of radiotherapy, smoking status, weight and age were the most important factors in the final model’s algorithm for predicting patient outcomes.

There are also types of artificial intelligence that can improve the detection of tumours and looking at things like magnetic resonance imaging (MRI) scans. These can aid with the detection of different types of tumours early.

The advent of such technologies will influence the roles people will be performing within future-state health services as well as the way they engage with technology. The digital transformation of health is an important subject in itself.

Written By

Dr. Tim Sandle is Digital Journal's Editor-at-Large for science news. Tim specializes in science, technology, environmental, and health journalism. He is additionally a practising microbiologist; and an author. He is also interested in history, politics and current affairs.

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