ARTIFICIAL INTELLIGENCE FOR HEALTH

AI for Health Care: Foreword

Artificial Intelligence (AI) and Machine Learning (ML) brought incredible changes in a number of industries. AI has emerged as a great transformative force, especially in the healthcare industry. The evolving use of AI apps in healthcare can break traditional boundaries and revolutionize medical diagnostics, treatments, and patient care.

The global healthcare AI market size was valued at USD 15.4 Billion in 2022 and is expected to grow with a CAGR of 37.5% in 2023-2030. The growing datasets are sufficient to show how AI is changing the healthcare industry.

Why Use AI for Healthcare?

AI uses machine learning (ML), natural learning processing (NLP), deep learning (DL), chatbots, and other AI software and tools, which enables the industry to streamline administrative tasks and enhance the health professional and patient experience.

AI in healthcare improves health diagnosis, medicine discoveries, and treatments and monitors patients’ health.

AI’s full potential in the area of health care is still being explored. But doctors already use it to read medical scans, help diagnose diseases, assist in treatment decisions, and help discover new drugs. Doctors and researchers are now using AI in the fight against COVID-19.

As AI-based technology sweeps through health care, hundreds of medical devices based on AI ML technology are now being marketed in the world.

The Potential of AI in Healthcare

The good news is that most large healthcare organizations are beginning to make use of some form of AI. However, we’re still early in the journey of learning how we can apply artificial intelligence to make healthcare better.

One of the primary use cases is using machine learning and AI to make predictions. Organizations are using AI to predict everything from emergency department volumes (to get a better handle on staffing and triage) to predicting which treatments might be most effective for women who develop breast cancer.

Healthcare teams are also using natural language processing to improve the interpretation of patient scans by augmenting the work of human radiologists.

When a radiologist looks at a scan, they’re typically looking for one thing, which is the reason you have that image done. But many times in the background, there's something else that can be seen. So as radiologists are dictating, natural language processes are being used to call out these secondary issues for follow-up, where previously those things might go unnoticed…so it's a preventive way of trying to get out ahead of a future health problem.

The biggest promise of AI in healthcare comes from changing clinical workflows. AI can add value by either automating or augmenting the work of clinicians and staff. Many repetitive tasks will become fully automated, and we can also use AI as a tool to help health professionals perform better at their jobs and improve outcomes for patients.

The healthcare organizations that will be the most successful are the ones that will be able to fundamentally rethink and reimagine their workflows and processes and use machine learning and AI to create a truly intelligent health system.

Why AI Healthcare Not Realized Yet?

It’s really about leaders understanding the capabilities of AI today, and then looking at how to apply it to add value. The value of AI doesn't come from the technology; it comes from changing clinical workflows and operational processes. AI adds value in only one or two ways: It adds value by automating the way work is done or augmenting the way work is done. Automation means highly repetitive work done by humans today is going to be done by a smart machine today or in the future. But the biggest part of healthcare today is augmentation…the idea of augmentation is, ‘How do we bring AI in behind the humans to make them better at something they care about?’

Senior leaders in the healthcare space don’t necessarily need to understand how AI works — they just need to grasp the power of AI and how it can help them provide personalized care for people more efficiently and compassionately.

For example, the government of Singapore is currently making use of machine learning and deep algorithms to help manage the health of people who are pre-diabetic. The government has mined the data of approximately five million citizens to identify people who are pre-diabetic, and then recruited people to volunteer to be part of a program where they receive personalized daily nudges about what they can do to take charge of their health and lower their blood sugar. This highly personalized advice has been highly successful at slowing participants’ progression from pre-diabetic to diabetic.