Analytics, Artificial Intelligence, Big Data and BI
Artificial Intelligence (AI) is transforming the healthcare and pharma industries by performing tasks that are typically done by humans, but at a fractional cost and in much lesser time. AI has several applications in these industries, whether it’s being used to discover links between genetic codes or to improve patient outcomes, to power process automation or even to maximize hospital operational efficiency.
Appropriately designed and implemented AI could be a boon to organizations
Let’s look at some of the key areas of application where AI is making significant impact -
- Applying deep learning (DL) to modernize radiology diagnosis - Some of the medical organizations are building deep learning platforms to analyse unstructured medical data (including radiology images like CT scans, MRI scans and X-ray scans, electrocardiogram, genomics) to provide doctors with better insight into a patient’s real-time medical needs.
- Using AI in patient screenings, diagnostic tests and blood samples to test for cancer- By deploying AI in general screenings, doctors aim to detect cancer in its most primitive stages and subsequently develop relevant therapy strategies. AI can also be used to diagnose other potentially life-threatening diseases at a very early phase. For example, an AI model using deep learning algorithms diagnosed breast cancer at a better rate than a team of pathologists.
- Inaccuracies/ wrongly diagnosing illness and medical errors accounted for many deaths across the globe-AI could be used for improving the diagnostic process. Partial medical histories and large caseloads on medical staff could lead to costly human errors. Not affected by such parameters, AI could predict and diagnose a disease at a faster rate than most medical professionals. As the world confronts the challenges of the COVID-19 pandemic, researchers are processing and analysing large amounts of data using AI/ ML algorithms.
- Doctors are using AI-enhanced microscopes to scan for harmful bacteria (e.g. E. coli) in blood samples at a faster rate than is possible using manual methods. The scientists used 22,000 images of blood samples to train the machine learning algorithms to identify such bacteria. The ML algorithms then learned how to identify and predict harmful bacteria in blood with 94%+ accuracy.
- Investments and progress in pharma industry/ new drug development initiatives are pulled back by skyrocketing development costs and research that takes thousands of person hours. Add to these costs, only a fraction of those drugs in pilot trials are successfully brought to market. Such pharma companies are taking notice of the efficiency, accuracy and knowledge that AI can bring to the drug development process.
- AI enabled automation platforms could be designed to power healthcare industry's most repetitive tasks, freeing up administrators’ time. The platform could automate processes like eligibility checks, billing, claims, data integration and reimbursements so operational staff can focus on providing better patient service. Such automation allows hospitals and physicians treat more patients daily.
AI and machine learning could be used to address a plethora of critical needs and requirements in healthcare and pharma industries. Such work is reinventing these industries through methods and algorithms that can learn, comprehend, predict and act to deliver better healthcare outcomes.