Kirsty Biddiscombe, UK Head for AI, ML & Analytics at NetApp, discusses the transformative power of AI in healthcare
As systemic problems deepen across industries, artificial intelligence (AI) adoption is rising across the board – most noticeably with, AI in healthcare.
Since the pandemic, the number of applications of AI in healthcare has exploded to hundreds of use cases and will continue to rise in the coming years. The integration of automation in the health industry has enabled the digitalisation of administrative tasks and unlocked the potential of faster, accurate patient diagnoses and fast- tracking drug development.
As this novel technology is proving its place at the heart of new solutions, the question rests on how AI is essential in the healthcare landscape and how it can alleviate burnout while contributing to treating patient cases.
Where are we at with AI in healthcare?
Looking at where we are today with AI in healthcare, there is a marked shift in how it operates. Despite being a humanised sector, there is growing interest in exploring the role of automation in an industry that depends on human contact and empathy.
It’s important to consider the fact that the UK population is ageing. Within the next 25 years, it’s predicted that the number of people over 85 will double to 2.6 million.
At the same time, there has been an increase in the proportion of people aged over 75 with long-term conditions, and their needs are likely to become more complex. This places pressure on the already overburdened healthcare system.
Adding complexity to this is the COVID-19 pandemic, which has accelerated the shift to digital health services, including online consultations, which would have taken a decade to develop organically.
In this context, the beneficial role of AI can be plotted against multiple fields of play in healthcare. A specific example of AI in genome sequencing is its application alongside the heel prick test on newborn children – which shows if they have a condition called Keratoconus. The condition, if left untreated, can lead to blindness in adulthood. By identifying it early, a reasonably simple procedure can be actioned on the child to mitigate the development of the condition and make a difference to the long-term prognosis. Incorporating AI is increasingly crucial as it can streamline healthcare operations, enabling patients to receive timely care.
Bringing AI capabilities to clinicians
Today, healthcare professionals are looking at how they can improve their practices if something like the recent pandemic happens again; they are prepared – and AI is one tool that can help drastically.
Allowing AI applications to interface with hospital systems paves the way for optimised ways to execute administrative tasks and overall better public health management.
For instance, the technology has allowed for the automation of appointment scheduling, and more seamless data input into the electronic health record (EHR). It has alleviated healthcare professionals from paperwork management, saving crucial time they are scrambling to find.
AI is also becoming indispensable in smarter hospital resource allocation and patient tracking. Bringing AI and automation into this mix can streamline operations and optimise resource allocation for prompter patient care.
AI to tackle stroke
A severe medical condition that happens when the blood supply to part of the brain is blocked, strokes are potentially fatal. This cerebrovascular disease represents a fight against time, as every second counts to treat the patients.
With the integration of technology and biomedical engineering solutions in the healthcare sector, AI in healthcare can aid in swiftly diagnosing strokes. This is crucial in reducing brain tissue loss caused by oxygen shortage resulting from the blood clot.
Automation in patient therapy
In addition, automation plays a vital role in selecting appropriate therapy and treatment methods for stroke patients. It’s leading to more accurate predictions of patient outcomes on a case-by-case basis that can be done earlier in the treatment process.
Computer Vision, for example, helps stroke patients swiftly identify stroke types by scanning the patient’s face. Specialists then visually review the images in real-time, to determine the type of stroke the patient is experiencing, thus identifying the best course of action.
What if Computer Vision accurately diagnosed strokes, incorporating patient records to optimise machine learning solutions for faster diagnosis without specialist availability? This could mean faster diagnosis for more patients. Fundamentally, and even in this case, AI is not pertinent without pertinent data: the models are only as good as the data.
This is why I believe AI, if done properly and developed adequately, is the only way we will be able to bridge the gap between perpetually rising demand for better healthcare services and empathy-centric methods of providing care to patients. The conception, development and adoption of AI in healthcare has already been life-changing for patients. It will be interesting to see how new techniques and initiatives can take this from life-changing to lifesaving in the future.
