Healthcare-Machine Learning In Healthcare-B-AIM pick selects

October 8, 2020


Machine Learning In Healthcare

From playing a critical role in patient care, billing, and medical records, today’s technology is allowing healthcare specialists develop alternate staffing models, IP capitalization, provide smart healthcare, and reducing administrative and supply costs. Machine learning in healthcare is one such area which is seeing gradual acceptance in the healthcare industry. Google recently developed a machine-learning algorithm to identify cancerous tumors in mammograms, and researchers at Stanford University are using deep learning to identify skin cancer. Machine Learning (ML) is already lending a hand in diverse situations in healthcare. ML in healthcare helps to analyze thousands of different data points and suggest outcomes, provide timely risk scores, precise resource allocation, and has many other applications.

In this article I’m discussing some applications of machine learning in healthcare, and how they stand to change the way we visualize the healthcare industry in Today’s life and beyond.

Identifying Diseases and Diagnosis –

One of the chief ML applications in healthcare is the identification and diagnosis of diseases and ailments which are otherwise considered hard-to-diagnose. This can include anything from cancers which are tough to catch during the initial stages, to other genetic diseases. IBM Watson Genomics is a prime example of how integrating cognitive computing with genome-based tumor sequencing can help in making a fast diagnosis. Berg, the biopharma giant is leveraging AI to develop therapeutic treatments in areas such as oncology. P1 vitals’ Predict (Predicting Response to Depression Treatment) aims to develop a commercially feasible way to diagnose and provide treatment in routine clinical conditions.

Drug Discovery and Manufacturing –

One of the primary clinical applications of machine learning lies in early-stage drug discovery process. This also includes R&D technologies such as next-generation sequencing and precision medicine which can help in finding alternative paths for therapy of multifactorial diseases. Currently, the machine learning techniques involve unsupervised learning which can identify patterns in data without providing any predictions. Project Hanover developed by Microsoft is using ML-based technologies for multiple initiatives including developing AI-based technology for cancer treatment and personalizing drug combination for AML (Acute Myeloid Leukemia).

Medical Imaging Diagnosis –

Machine learning and deep learning are both responsible for the breakthrough technology called Computer Vision. This has found acceptance in the InnerEye initiative developed by Microsoft which works on image diagnostic tools for image analysis. As machine learning becomes more accessible and as they grow in their explanatory capacity, expect to see more data sources from varied medical imagery become a part of this AI-driven diagnostic process.

Behavioral modification –

Behavioral modification is an important part of preventive medicine, and ever since the proliferation of machine learning in healthcare, countless startups are cropping up in the fields of cancer prevention and identification, patient treatment, etc. Somatix is a B2B2C-based data analytics company which has released an ML-based app to recognize gestures which we make in our daily lives, allowing us to understand our unconscious behavior and make necessary changes.

Clinical Trial and Research –

Machine learning has several potential applications in the field of clinical trials and research. As anybody in the pharma industry would tell you, clinical trials cost a lot of time and money and can take years to complete in many cases. Applying ML-based predictive analytics to identify potential clinical trial

candidates can help researchers draw a pool from a wide variety of data points, such as previous doctor visits, social media, etc. Machine learning has also found usage in ensuring real-time monitoring and data access of the trial participants, finding the best sample size to be tested, and leveraging the power of electronic records to reduce data-based errors.

Machine learning is advancing healthcare into a new realm. It’s exciting to think about where it can go. Someday, it will be common place to have embedded machine learning expertise that analyzes not only what’s going on with patients in real time, but also what’s going on with similar patients in multiple healthcare systems, what applicable clinical trials are underway, and the efficacy, and cost of new treatment options. It may sound futuristic, but the analytic engine that can present all this information at the point of care is available now.





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