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.
Clinical Trial Research
Machine learning can help shape and direct clinical trial research owing to its immense potential. Applying advanced predictive analytics in identifying candidates for clinical trials could draw on a much wider range of data than at present, which includes social media and doctor visits to genetic information when looking to target specific populations. The result would be smaller, quicker, and less expensive trials overall.
Remote monitoring and real-time data access for increased safety can also have ML implementations. Monitoring biological and other signals for any sign of harm or death to participants is one example of it. There are many other ML applications for helping increase clinical trial efficiency according to McKinsey. This includes finding the best sample sizes for increased efficiency, addressing and adapting to differences in sites for patient recruitment, and using electronic medical records to reduce data errors.
Radiology and Radiotherapy
In 20 years odd years, radiologists won’t exist in anywhere near their current form. They might look more like cyborgs: supervising algorithms reading thousands of studies per minute. In a bid to ensure such kind of future, tech giant like Google’s DeepMind Health is working with University College London Hospital (UCLH) to develop machine learning algorithms capable of detecting differences in healthy and cancerous tissues to help improve radiation treatments. The collaborators are working on applying ML to help speed up the segmentation process and increase accuracy in radiotherapy planning.