Healthcare

Healthcare and related fields such as pharmaceuticals and life sciences, have also seen a gradual rise in the adoption and use of machine learning. A leading example has been IBM Watson. Developed in late 2000s, IBM Watson rose to popularity after it won the Double Jeopardy, a popular quiz contest in the US in 2011. Today, IBM Watson is being used for clinical research and several institutions have published preliminary results of success. (Source: http://www.ascopost.com/issues/june-25-2017/how-watson-for-oncology-is-advancing-personalized-patient-care/). The primary impediment to wider adoption has been the extremely high cost of using the system with usually an uncertain return on investment. Companies that are generally well capitalized can invest in the technology.

More common uses of data science in healthcare include:

• Epidemiology: Preventing the spread of diseases and other epidemiology related use cases are being solved with various machine learning techniques. A recent example of the use of clustering to detect the Ebola outbreak received attention, being one of the first times that machine learning was used in a medical use case very effectively. (Source: https://spectrum.ieee.org/tech-talk/biomedical/diagnostics/healthmap-algorithm-ebola-outbreak).
• Health insurance fraud detection: The health insurance industry loses billions each year in the US due to fraudulent claims for insurance. Machine learning, and more generally, data science is being used to detect cases of fraud and reduce the loss incurred by leading health insurance firms. (Source: https://www.sciencedirect.com/science/article/pii/S1877042812036099).
• Recommender engines: Algorithms that match patients with physicians are used to provide recommendations based on the patients' symptoms and doctor specialties.
• Image recognition: Arguably, the most common use of data science in healthcare, image recognition algorithms are used for a variety of cases ranging from segmentation of malignant and non-malignant tumours to cell segmentation. (Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3159221/).