AI Can Tell Which Medical Treatment is Better: Research Explains

Artificial intelligence could tap into the enormous volumes of data not only on our bodily systems and genetic heritage, but also on various pharmaceutical substances — and help physicians design our treatments individually.

Why different treatments for different patients

How a new vaccine, antidepressant or chemotherapy drug is absorbed is unknown, claims Aalto. Likewise, how it affects and lives in particular person’s body remains open. To get through the data chaos and find answers, we would need to process superhuman volumes of information with even more superhuman speed. The last time medicine treated people as individuals, our cures were leeches, mercury, and herbal mixtures, while the most fortunate found pain relief from opium. As doctors engaged in trial and error, patients expired. Traditionally, the effectiveness of medical treatments is studied by randomized trials where patients are randomly divided into two groups: one of the groups is given treatment, and the other a placebo. Scientists showed that there may be other ways to evaluate treatment effectiveness.

Artificial intelligence is the other way

In the study published in the journal Healthcare Informatics Research, the researchers used the method to evaluate treatment effectiveness in obstructive sleep apnea — a potentially serious sleep disorder in which breathing repeatedly stops and starts. However, the method can also be applied to other treatments, researchers said. The study showed that in patients with sleep apnea, the continuous positive airway pressure (CPAP) treatment reduced mortality and the occurrence of myocardial infarctions and cerebrovascular insults by five percent in the long term. Similarly, associate Professor Tomi Laurila at Aalto University employs new carbon materials in the development of extremely sensitive sensors intended for measuring, for example, the concentrations of different neurotransmitters in the brain or the spread and effects of pharmaceuticals in the body.

How AI does it better

According to Professor Olli-Pekka Ryynanen from the University of Eastern Finland, the method opens up new and significant avenues for the development of medical research. “We can now predict the treatment outcome in individual patients and to evaluate existing and new treatment methods,” he said, adding that “with this method, it is also possible to replace some randomized trials with modelling.” Meanwhile, “machine learning enables us to model carbon structures up to 1000 times larger than with current quantum mechanical methods — and not lose precision,” Tomi Laurila thinks.

Doesn’t just detect carbon: “We have trained machine learning methods to identify carbon in particular, but we’ve also succeeded in teaching AI to detect other substances, like oxygen or hydrogen, as well,” says Laurila.

No waiting for test results: AI-utilizing analysis tools would screen blood for desired substances and immediately predict the bodily reactions they cause, eliminating the need to wait days or weeks for test results to come back from a lab.

AI still, however, trial and error

The biggest challenge for biological and medical measuring, he adds, is selectivity: “how to extract the right signals from all the noise.” Laurila notes that the experimental development of sensors is, a little like old-fashioned medicine, still largely based on trial and error. Combining machine learning and computational methods speeds up and rationalizes experimental research considerably — while also tailoring the sensors to directly detect the desired substances, pharmaceuticals, proteins and mediators.

Future: A digital twin for everyone

Even though the biggest upheavals AI will introduce to healthcare are yet to come, the most ambitious vision is clear. “The personalization of medicine will peak when we can create a digital twin for everyone,” says Academy Professor Samuel Kaski of Aalto University.

What the twin will do: A model would be created for people individually, combining biological data, clinical examination results, sensor data and information on the patient’s lifestyle, environment and bodily system.

Why it’ll be better: A digital twin could provide logical recommendations, evaluate the precision and reliability of its results as well as make an understandable case for its findings and rationale.

“Twins could also help in disease prevention. The model could, for example, give an assessment based on diet, exercise, various risk factors, and genetic disposition. Paired with a smart device, the twin could encourage people to do things that are beneficial to their health — in way that they would actually like to observe.” – India Today