AI TRANSFORMING HEALTH CARE

AI TRANSFORMING HEALTH CARE 

Introduction

The future of ‘standard’ medical practice might be here sooner than anticipated, where a patient could see a computer before seeing a doctor. Through advances in artificial intelligence (AI), it appears possible for the days of misdiagnosis and treating disease symptoms rather than their root cause to move behind us. Think about how many years of blood pressure measurements you have, or how much storage you would need to delete to fit a full 3D image of an organ on your laptop? The accumulating data generated in clinics and stored in electronic medical records through common tests and medical imaging allows for more applications of artificial intelligence and high-performance data driven medicine. These applications have changed and will continue to change the way both doctors and researchers approach clinical problem-solving. 

Application: 

Machine Learning has made great advances in pharma and biotech efficiency. Some important applications include: 

Diagnose Diseases: Correctly diagnosing diseases takes years of medical training. Even then, diagnostics is often an arduous, time-consuming process. In many fields, the demand for experts far exceeds the available supply. This puts doctors under strain and often delays life-saving patient diagnostics.

Developing Drugs Faster: Developing drugs is a notoriously expensive process. Many of the analytical processes involved in drug development can be made more efficient with Machine Learning. This has the potential to shave off years of work and hundreds of millions in investments. 

Personalize Treatment: Different patients respond to drugs and treatment schedules differently. So personalized treatment has enormous potential to increase patients’ lifespans. But it’s very hard to identify which factors should affect the choice of treatment. 

Improving Gene Editing: This technique relies on short guide RNAs (sgRNA) to target and edit a specific location on the DNA. But the guide RNA can fit multiple DNA locations – and that can lead to unintended side effects (off-target effects). The careful selection of guide RNA with the least dangerous side effects is a major bottleneck in the application of the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) system.

Companies practicing AI in Health Care 

Google Health: The company aims to build products that support care teams and improve patient outcomes. Google Health is tapping into A.I.’s potential to help in cancer diagnosis, predicting patient outcomes, averting blindness, and more. 

IBM Watson Health: Healthcare set up as a service to bring A.I.’s helping hand to stakeholders within the healthcare sector from payers to providers. With the power of cognitive computing, Watson Health has aided several renowned organizations like Mayo Clinic with its breast cancer clinical trial and Biorasi to bring drugs to the market faster while slashing costs by over 50%. 

Oncora Medical: The Philadelphia-based start-up aims to help cancer research and treatment, especially in radiation therapy. One of its co-founders, David Lindsay, was doing clinical work as an M.D./Ph.D. student at the University of Pennsylvania, when he recognized that radiation oncologists had no integrated digital database that collected and organized electronic medical records. So he decided to build exactly that: a data analytics platform that can help doctors design sound radiation treatment plans for patients.

Market Trends

Conclusion 

AI is already helping us more efficiently diagnose diseases, develop drugs, personalize treatments, and even edit genes. But this is just the beginning. The more we digitize and unify our medical data, the more we can use AI to help us find valuable patterns – patterns we can use to make accurate, cost-effective decisions in complex analytical processes.