The future of oncology: Using machine learning to drive the development of personalized medicine

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Erica Brant

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Abstract





In recent years, healthcare has shifted towards treating each cancer patient as an individual, rather than one of thousands with a similar disease. With the advances that have been made in molecular profiling, it is no longer feasible to apply a broad cancer treatment when there is a plethora of tumour variability. In this day and age, there is a vast amount of healthcare data at our fingertips which can be targeted and analyzed using machine learning. Machine learning, a subset of artificial intelligence, allows physicians to quickly identify evidenced-based personalized treatments specific to each patient and the molecular profile of their particular tumour. In addition to identifying the optimal treatment dosage to maximize efficacy and minimize side effects, machine learning can standardize information received by patients regardless of their physical location and the physician that they see. Although machine learning has the potential to change how we treat cancer, research is a long way from actualizing this goal. With time, research, and investments, our colleagues of the future will be utilizing this technology in their everyday practice to tailor treatments to each individual, rather than offering the same blanket treatment to all.