The goal of precision medicine is to provide a personalized approach for the prevention, diagnosis, and treatment of disease. By taking an individual’s genetics, environment, lifestyle, and medical history into account, a unique and targeted treatment plan can be created.
The concept of precision medicine isn’t new though. An early example that we’re all familiar with is matching the blood type of a patient receiving a blood transfusion to that of a donor, rather than just choosing a donor at random.
This article looks at some of the ways AI is helping to transform precision medicine, with a particular focus on cancer treatment.
Precision medicine relies on global information gathered from vast amounts of complex data, and the quantity will continue to increase as personalized data from wearables, apps, and digital devices becomes ever more abundant.
AI has shown extraordinary potential to analyze these huge datasets quickly and accurately, and discover patterns that would be missed by the human eye. The resulting information, connecting patient data from around the world, has the power to revolutionize healthcare both for common and rare conditions.
Patient stratification is a critical challenge in any clinical trial – ensuring that the patient group being studied is diverse, yet reflective of the population that will benefit from the treatment being investigated.
AI can be employed to optimize patients into subgroups that represent those with a variety of genetic backgrounds and a particular pathogenesis, accurately separating patients that are more likely to respond to a treatment.
Clinical trials sometimes falter because the average response to a drug fails to meet the trial’s targets. If some people on the trial responded well to treatment, though, AI can find them within the existing trial data.
With the assistance of AI algorithms, it’s possible to automate everything from data entry to research analysis. As well as freeing up resources, diagnostic errors are reduced, and the most up-to-date research is made available to support clinician decision-making.
AI can also be leveraged to improve patient experience – from robotic nursing assistants and fall-prevention solutions, to intelligent speech and sentiment recognition for call centers.
Precision medicine is very much evident in cancer diagnosis and treatment. It can also help predict a genetic cancer risk, where a pattern can be seen within a family. Testing for the presence of an inherited gene can lead to, for example, a recommendation for regular or earlier screening, or even preventative surgery.
The same cancers don’t always respond to a treatment in the same way because they’ve occurred as a result of mutations to different genes. Melanoma can be caused by mutations in BRAF, NRAS, CDKN2A or NF1 genes, whereas breast cancer can be triggered by BRCA1, BRCA2 or other inherited genes mutating.
This is where precision medicine comes in. Biomarker testing of cancer cells identifies any gene or protein changes, and helps to anticipate how the body will break down, absorb, and utilize treatment drugs.
Several types of cancer, such as cervical, colorectal, and prostate, are curable if diagnosed and treated early, but mortality rates remain high because they lack effective screening and treatment methods.
AI can play a part in tumor screening, improving the detection rate of lesions, and making the screening method more effective. It also promotes the accuracy of diagnosis by helping to distinguish between true and false disease progression. And AI can calculate the advantages and disadvantages of each treatment option. So, through AI, tumors will be accurately diagnosed, staged, and classified, and patients will benefit from precision treatment.
One size doesn’t fit all in healthcare. How a disease or condition manifests itself in one person can be very different from how it looks in another. Each patient may have distinct symptoms and require different approaches to treatment in order to reach their healthiest outcome.
A personalized approach allows optimized screening and treatment for patients using their individual health profiles, rather than the blanket criteria currently used, such as age or sex. The potential for AI-powered precision medicine can’t be overstated.