4 Ways Machine Learning is Reinventing Healthcare

December 11, 2018 - 4 minutes read

In the upcoming years, McKinsey estimates that big data and machine learning use cases in pharma and medicine could generate a value of up to $100 billion annually. This estimate is based on improved decision-making, optimized innovation, and greater efficiency of research and clinical trials. For these reasons among others, it’s no surprise this innovation is moving at high speed with some of the biggest names in the industry putting forth great effort to reach some of the longest standing medical goals.

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A subset of artificial intelligence, machine learning enables healthcare professionals to leverage data to make predictions in a variety of departments (clinical, operational, financial, etc.). Opening up an array of new capabilities in the administering of care and healthcare administration, machine learning will benefit the entire healthcare sector.

Improved disease identification and diagnosis

Improving efficiency and accuracy of diagnoses for various ailments is at the very forefront of machine learning research in medicine. Efforts we’ve seen thus far, include the formation of IBM Watson Genomics, a partnership initiative between IBM Watson Health and Quest Diagnostics. This initiative focuses on high-need areas like cancer identification and treatment, to improve precision medicine by integrating cognitive computing and genomic tumor sequencing.

Alzheimer’s disease identification is also being improved with artificially intelligent technology. Today, AI-enabled robots can diagnose a patient with Alzheimer’s in less than one minute based on speech patterns and voice. While a human may have difficulty detecting these signs. AI systems are objective and quantifiable in their analysis, contributing to a 82 percent (and growing) level of accuracy.  

Personalized Care

Through the combination of individual health care data and predictive analytics, more personalized and effective treatments can be implemented into healthcare procedures. For instance, addiction specialists aiding patients in adjusting unhealthy behaviors can benefit from Machine Learning apps such a Somatix. Specifically for individuals trying to quit smoking, this technology uses recognition of hand-to-mouth gestures to help people better understand their behavior and formulate the best treatment method based on their actions.

Smarter health records

Better healthcare begins with better record keeping. Patient document classification reinvented with Machine Learning-based technologies is advancing the process of collecting and organizing individual patient records. Support vector machines for example, utilize advanced learning algorithms that analyze data and can vastly improve document organization, contributing to greater efficiency. Optical character recognition technology is also aiding in the efforts of digitizing records, by transforming sketched handwriting into digitized characters old records can be restored and combined into one concise file.

Intelligent predictions

Anticipating when an epidemic or other medical outbreak may occur can give healthcare professionals a running start at providing the best defense. Utilizing Machine Learning and AI technology, data gathered from satellites, historical information from the web, temperature, average monthly rainfall, and various other data points can provide insight where traditionally, it was not available to us. Support vector machines and artificial neural networks in combination with this data have in fact been used to predict malaria outbreaks. Going forward as the technology continues to improve, these capabilities will be of great benefit to our population’s wellbeing.

Machine learning has come a long way but still has a long ways to go. For every accomplishment this tool has reached, there are greater, more innovative visions predicted for the future. Optimizing the standard of patient care, generating more revenue and decreasing errors is underway in healthcare with machine learning…and we’re just getting started.

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