Artificial intelligence (AI), machine learning and deep learning have become entrenched in the professional world. AI-style capabilities are being embraced and developed globally (over 26 countries/regions have or are working on a national AI strategy) for many different purposes — from ethics, policies and education to security, technology and industry, the scope is broad and multi-faceted. If, like many others, you are unclear as to what this new terminology means, below is a diagram depicting the hierarchy of AI, machine learning and deep learning for you to consider. In healthcare, the opportunities are vast and significant. Just from a financial point of view, AI has the potential to bring material cost savings to the industry.
But where should you start, and where do the opportunities lie?
Where to start with AI
First, look at where money is invested — in other words, which start-ups are attracting investors and what is their focus. Rock Health (the first venture fund dedicated to digital health) shows that the top four areas for venture capital investment between 2011 and 2017 were research and development, population health management, clinical workflow and health benefits administration. More than $2.7 billion was invested over 6 years, across 206 start-ups.
Another venture capital and digital health community, Startup Health, which also keeps track of global investments, found that funding is doubling every year for companies which use machine learning technology to enhance health solutions. The companies that focused on diagnostics or screening, clinical decision support and drug discovery tools received the largest share of funding for machine learning in 2018 — i.e., $940 million.
Delving into AI’s opportunities
Perhaps the biggest opportunity lies in assisted robotic surgery, with a potential cost saving of US$40 billion per year. AI-enabled robots can assist surgical procedures by analyzing data from pre-op medical records and past operations to guide a surgeon’s instrument during surgery and to highlight new surgical procedures. The potential benefit to the healthcare organization and the patient from this approach is noteworthy: a 21 per cent reduction in length of hospital stay because robotic-assisted surgery ensures a minimally invasive procedure, thus reducing the patient’s need to stay in the hospital longer.
Surgical complications were found to be dramatically reduced, according to one study into AI-assisted robotic procedures involving 379 orthopedic patients. Robotic surgery has been used for eye surgery and heart surgery. For example, heart surgeons have used a miniature robot, called the Heart Lander, to carry out mapping and treatment over the surface of the heart.
Another valuable use of AI is in virtual nursing assistants. One example is Molly, an AI-enabled virtual nurse that has been designed to help patients manage their chronic illnesses or deal with post-surgery requirements. According to a Harvard Business Review article, assistants like Molly could save the healthcare industry as much as US $20 billion annually.
Diagnosis is another exciting development for AI, with some promising findings on the use of an AI algorithm to detect skin cancers. A Stanford University report found that deep convolutional networks (CNNs) performed as well as dermatologists in classifying skin lesions. Other exciting breakthroughs in AI-assisted diagnosis include a deep-learning program that listens to emergency calls, analyses what is said, tone of voice and background noises to determine whether the patient is having cardiac arrest. Astonishingly, a study from the University of Copenhagen found the AI assistant was right 93% of the time, compared with 73% of the time for human dispatchers.
A fourth potential use for AI lies in digital image analysis, which could help to improve future radiology tools. In one example, a team of researchers from MIT developed an algorithm to rapidly register brain scans and other 3-D images. The result reduces the time to register scans with accuracy comparable to that of state-of-the-art systems.
With so much potential to be gained from AI, healthcare organizations will need to enhance their skills in AI and related capabilities. Decision-makers need to inform themselves about the potential and what is required to achieve those objectives, and then ensure that their teams are properly trained. Culture change in understanding how AI can be used to solve current and future problems is paramount to the future of next-generation healthcare and life sciences organizations.
