More data almost always yields better results when it comes to the effectiveness of machine learning, and the healthcare sector is sitting on a data goldmine. As per the estimates by McKinsey, big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators. Let’s look at some of the applications of machine learning in pharma and medicine.
At the forefront of ML research in medicine are disease identification and diagnosis of ailments. The large players were some of the first to jump on the ML bandwagon, specifically in areas like cancer identification and treatment which needed it most. IBM Watson Health announced IBM Watson Genomics in October 2016, a partnership initiative with Quest Diagnostics, which aims to make strides in precision medicine by integrating cognitive computing and genomic tumour sequencing.
Berg, a Boston-based biopharma company is using AI to research and develop diagnostics and therapeutic treatments in multiple areas, including oncology. Google’s DeepMind Health announced multiple UK-based partnerships last year, including the one with Moorfields Eye Hospital in London, in which they’re developing technology to address macular degeneration in ageing eyes.
Oxford’s P1vital® Predicting Response to Depression Treatment (PReDicT) project is using predictive analytics to help diagnose and provide treatment in the area of brain-based diseases like depression, with the overall goal of producing a commercially-available emotional test battery for use in clinical settings.
That’s not where the vast possibilities of machine learning in pharma and medicines end. Keep watching this space to know more.