Cancer Treatment gets less toxic with the implementation of Artificial Intelligence “Self Learning”

Cancer_Treatment_gets_less_toxic_with_the_implementation_of_Artificial_IntelligenceNovel machine-learning techniques are being developed by MIT Researchers to improve the quality of life for patients by reducing toxic chemotherapy and radiotherapy dosage for glioblastoma-that is the most aggressive form of brain cancer. Glioblastoma is a malignant tumor that develops in the brain or spinal cord and prognosis for adults is no more than five years. Patients must go through a combination of radiation therapy and multiple drugs to be taken simultaneously every month.
MIT Media Lab researchers developed a model that could make dosing less toxic but still effective which is powered by a “self-learning” machine-learning technique that implements the technique only to a specific level with the lowest possible potency and frequency of doses will not be affected that still reduces tumor sizes efficiently.
Here, the research model was developed using a technique called reinforced learning (RL), a method inspired by behavioral psychology, where the model learns to favor the desired outcome. This technique is implemented by Artificially intelligence that complete “actions” in an unpredictable, complex environment to reach the desired outcome that finishes the work in a simple manner
The researchers also warn not to go for this mechanism for a maximum number and potency of doses as there is a chance that it creates a few side effects. Researcher’s state that traditional RL models work toward a single mechanism with increasing toxic possibilities, whereas this advanced AI machine learning mechanism reduce the toxic nature that causes in the traditional way of treating the tumors. The researchers stated that they particularly designed the model to treat each patient individually in a single cohort manner and achieve significant results.


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