Pre-COVID-19, the healthcare industry was treading a slow but sure course toward digital transformation and AI. These days, it seems that healthcare (and many other sectors, for that matter) is sprinting in that direction - lungeing to keep up with the lightning pace of emerging tech and equipping providers and patients with vital tools for public health and safety

AI is changing the way we live, the way we do business, the way we take decisions. There isn’t a single sector or industry that is not being affected and transformed. So how do leaders need to evolve in order to stay at the top of the game?

The vast majority of the world’s health systems are not using their data and AI to make helpful predictions that inform decision- making, creating tremendous opportunity to use data and AI to help make more insightful healthcare decisions. But the challenge is in finding common, replicable use cases.

The greatest promise that AI presents to human health is the opportunity to shift to personalised models of care, moving us from discussion of Life Sciences to a focus on Life Services - a refocus from cure to prevention, driven by data and putting power in the hands of the individual. By ‘personalised’ I mean both the personalised nature of the service and also the need to get physically close to the individual (e.g. with sensors), delivering a sufficient value to each person receiving care to enable trust, and thus to persuade them to part with fairly intimate data in return.

AI and machine learning are groundbreaking innovations that will help translate the increasingly diverse datasets generated by next-generation biomarkers into improved patient outcomes. These innovative platforms utilise a wide range of inputs such as genomics and epigenomics, transcriptomics, medical imaging, digital pathology, wearable biometrics, chemical structures and metadata to generate valuable diagnostic insights or discover new therapies.

We are living in a world of increasing change and new realities. With the ongoing COVID-19 pandemic fuelling adoptions of new technologies, it is clear that we will not go back to the way we were. One of the prevailing technologies that is currently seeing its time to shine is AI – specifically, AI related to visual recognition of components within healthcare, like identifying breast cancer in mammography imagery or diagnosing health issues like cancerous polyps or other visual indicators.

It is undoubtable that AI has become ubiquitous to our daily lives, but it’s only now that we’re starting to see its true potential come to the fore in healthcare. I’m often asked about the most exciting innovations in healthtech and my response nine times out of 10 is AI.

Self-service has emerged with abundance in 2020, significantly accelerated by COVID. In every facet of our lives, we want and expect service from our providers, whether for technology, shopping, banking or many other applications., It’s always on - 24/7/365 - and always fast. Customers have the need for speed, convenience, and utility. It is time to take a customer-centric approach.

Learning from the past, if we can just meter our excitement and allow for this technology to be developed at a reasonable pace, with realistic goals, it’s possible that we could reach some of the heights we’d always hoped. We need to realise, once again, that nothing in medicine ever comes easy, and all of the intelligence in the world, artificial or not, won’t change that.

Through effective automation of processes and problem-solving, AI is pushing the boundaries of innovation across practically every industry imaginable, and none more so than healthcare, where its use is already relatively widespread. Spanning drug discovery, through automation of diagnosis to supporting patient engagement and adherence, the potential for impact in the sector is exponential.

There have been sweeping advances in the development and use of AI in healthcare - the main impact has been seen in hospital settings, where advanced algorithms have been increasingly augmenting clinical decision-making to aid the delivery of precision medicine. The development of similar technology for community or primary care settings lags behind, and the COVID-19 pandemic further highlights the limitations of a centralised model of care.

he NHS published its Long Term Plan in 2019, setting out its plan for changes to the current health structure, to support the transition towards a more efficient, effective model of public health. This plan includes a notable focus on digital health, including AI, machine learning and emerging technologies, as well as behaviour change - particularly with at-risk groups. We see this as a positive move towards embracing new technology that can improve the overall healthcare system, but there have been several barriers to uptake. The COVID-19 pandemic, however, has created a situation of urgent need for solutions that enable remote patient-clinician interaction and patient self-management - two areas that our digital therapeutics address.

As a practice area, Big Data surfaces relationships and patterns that are largely imperceptible to humans.

Our National Health Service (NHS) in the UK was founded decades before AI health technology became an established field with concrete, viable products. Over the more recent years, it has therefore been somewhat challenging to incorporate AI tech into our existing healthcare processes and clinical practice, and explain the efficiencies, benefits and cost savings to healthcare practices.