It appears that artificial intelligence has taken another plunge into uncharted territory – this time, the human brain. A recent Harvard Medical School study demonstrates an AI tool that has the potential to revolutionize the treatment of brain lesions.

Researchers and neurosurgeons have been baffled by gliomas for decades; these annoying, prevalent brain tumors have stumped scientists. A particularly aggressive form of glioma has claimed the lives of notable figures such as Beau Biden and Senator John McCain.

Professor at Harvard Medical School and co-author of the study Kun-Hsing Yu remarked, “Every glioma has its own story, and hence needs a unique approach during surgery.” The difficulty lies in removing the glioma without damaging the adjacent brain tissue, which requires a plethora of information typically only accessible during surgery.

Traditionally, during an operation for brain cancer, doctors transmit a tissue sample to a pathologist for rapid analysis. In this high-stakes, race-against-the-clock scenario, the patient’s head is left exposed while the sample is examined. Within ten to fifteen minutes, the pathologist will provide a diagnosis.

Nonetheless, the current procedure is not without flaws. It’s a high-pressure atmosphere. Errors are possible, mainly when the quality of the slide is subpar. This is what we’re attempting to prevent,” Professor Yu explained.

Enter artificial intelligence, specifically machine learning, in which a computer system discovers patterns without being explicitly programmed. The Harvard team discovered that machine learning could accelerate and improve the glioma analysis process, thereby reducing the patient’s time under the scalpel.

Dr. Dan Cahill, a neurosurgeon at Massachusetts General Hospital, praised the precision of the AI tool, describing it as “remarkably superior” to conventional techniques for dissecting the molecular structure of a glioma.

Potentially, machine learning can also aid physicians in employing other cutting-edge brain cancer treatments. During surgery, injecting tumor-destroying drugs directly into the brain is a practical approach for treating aggressive gliomas. The artificial intelligence tool Yu and his team developed can rapidly determine a particular tumor’s invasiveness, aiding physicians in deciding whether to administer these direct injections.

Although this sounds like something from a science fiction film, it has not yet reached the operating room. The FDA must still approve the instrument before clinical trials can begin, which will not occur for several years.

Nevertheless, this is not an isolated occurrence; the use of AI in cancer treatment and detection is on the rise worldwide. In the United Kingdom, for example, researchers use AI to determine whether anomalous growths detected on CT scans are malignant. Kheiron Medical Technologies, a London-based startup, is using AI to assist radiologists in detecting breast cancer.

Kheiron’s co-founder Peter Kecskemethy states, “The key to cracking cancer is AI. It’s time to unlock the potential of this powerful tool.”

Indeed, the medical community appears to be inching closer to this reality. From expedited glioma treatment to accurate cancer detection, AI is gaining a stronger foothold in medical science. As we progress, we must not only defeat cancer but also thwart it.