How Artificial Intelligence (AI) is Democratising Clinical Diagnosis

The FDA evaluated data from a clinical study of retinal images obtained from 900 patients with diabetes at 10 primary care sites. The study was designed to evaluate how often IDx-DR could accurately detect patients with more than mild diabetic retinopathy. In the study, IDx-DR was able to correctly identify the presence of more than mild diabetic retinopathy 87.4 % of the time and was able to correctly identify those patients who did not have more than mild diabetic retinopathy 89.5 % of the time.

AI based systems are set to revolutionise healthcare by providing high quality clinical diagnostics at a far greater scale than before, democratising access to those in need, and at a cost that makes it valuable for all the stakeholders.

Digital Health Ecosystem

The promise of AI is clearly not lost on the industry. Rock Health, the first venture fund dedicated to Digital Health, suggests that H1 2018 saw 193 Digital Health firms raise funding worth US$3.4 billion across America alone, putting funding on track to beat 2017 records.

What’s more, regulators around the world are slowly but surely easing requirements for novel and cutting edge technologies to enter the market – two recent examples include the FDA’s Digital Health Innovation Action Plan and Early Feasibility Study (EFS) programme in the US.

A recent study by Ecosystm shows that the applications of AI technologies in the MedTech space are farreaching, with current focus areas including non-invasive diagnostics, remote health, clinical decision support and predicting patient risk. This would mean better flagging when serious illnesses and ailments are on the horizon, all the while minimising unnecessary consultations and reducing the workloads of already stretched healthcare professionals.

What is Holding AI Back Then?

From a technological perspective, senior executives – CIOs and Line of Business leaders– cite integration with internal systems, access to data and cyber security as key challenges. To combat this, countries such as Singapore, Australia and New Zealand are looking at ways to aggregate and analyse digitised patient data in the hope of generating insights to improve clinical outcomes. More work needs to be done to understand specific concerns of all stakeholders, and resolve those conflicts in a respectful and pragmatic manner.

That said, as pressures mount on the healthcare industry to achieve more with less, make no mistake that AI in healthcare is inevitable. We’re in a race against time – China, Hong Kong, Singapore, Japan, and India are just a few of the many countries across Asia that have cited shortages in healthcare manpower over the past year. As the elderly population increases, bringing with them a ream of age-related health issues, the situation is only going to get worse. So ready or not, it may well be the case that in a few short years, the healthcare industry will consider their AI-driven diagnostic tools as essential as their physical ones.

Closing themes:

AI is a great enabler to democratise disease diagnosis at a scale, cost and quality never been possible before

While still an emerging field. trust is slowly developing. Some of the recent use cases are a step in the right direction Regulatory authorities are supporting the development by enabling and encouraging innovation

Governments are removing roadblocks and barriers, in some cases, opening population health data sets to teach

AI systems

Early adopters among providers are embracing new technology, but with caution - impact on payers is still to be seen.