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AI in healthcare: Beyond chatbots, a way to revolutionise patient care

Creative and innovative applications of AI open a variety of beneficial possibilities

In the healthcare industry, generative AI and other applications of machine learning have the potential to transform everything from the process of drug development to patient care.

When many organisations think about how they can use AI, the first idea that often comes to mind is to implement a chatbot. While this application is useful for guiding users through basic processes and addressing customer service issues, it barely scratches the surface of what’s possible.

Expert insight: “Using AI-powered personalised medicine could allow for more effective treatment of common conditions such as heart disease and cancer, or rare diseases such as cystic fibrosis. It could allow clinicians to…screen patients using their individual health profiles, rather than the current blanket criteria of age and sex. This personalised approach could lead to earlier diagnosis, prevention and better treatment, saving lives and making better use of resources.”

Mihaela van der Schaar, Director of the Cambridge Centre for AI in Medicine, University of Cambridge
By the numbers:  - In 2021, the artificial intelligence (AI) in healthcare market was worth around 11 billion U.S. dollars worldwide*
- Projections predict that it will be worth almost 188 billion U.S. dollars by 2030 (CAGR 37%)*
- 92% of Life Sciences CIOs and tech executives say that Artificial intelligence/Machine Learning is most likely to be implemented in their companies by 2026**
- AI projects deliver a 13% return on investment (ROI) for “best-in-class companies” leveraging the technology

* Data source

** Gartner®, Infographic: 2024 Top Technology Investments and Objectives in Life Sciences, By Jeff Smith, 27 October 2023.  GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

The power to personalise care — at scale

The general term “AI” (which encompasses the areas of machine learning and deep learning) has become a vastly misunderstood buzzword, but there is substance to the hype. Digital leaders of healthcare and pharmaceutical companies have already recognised that AI is here to stay, and many are finding a variety of innovative and transformative ways to apply it. Massive growth in research and development indicates that there are virtually no limits to the ways the technology can be applied to develop new treatments and personalise patient care. And on the business side of healthcare, AI-enabled systems are delivering savings in time, money, and resources.

For example, applications in clinical trials can speed up the development and approval of treatments.  AI-enabled Large Language Models (LLMs) can expedite the completion of repetitive regulatory requirements. Machine learning can assist in analysing and categorising massive amounts of structured and unstructured data, and extrapolate results with partial data.

A graph showing the use cases for large language models (LLMs)
Source: 2023 Gartner Life Sciences Research Panel Survey (November) GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Of course, the aforementioned chatbots can be beneficial as well, providing that the strategy for adoption is human-centric.  But beyond simple interactions, one of the most impactful applications of AI and machine learning is the ability to make healthcare more personalised.

With comprehensive, correct and clean patient data, AI-powered healthcare programs can customise interactions and care plans to every patient’s individual needs — without sacrificing the ability to scale up as populations / user bases grow. Beyond simply making individuals feel like their care is more personal to their medical situation, it can also lead to potentially life-saving benefits such as disease prevention and earlier diagnosis of issues.

Setting up AI for success

Along with the hype about the seemingly limitless potential of Artificial Intelligence and Machine Learning, there are quite a few warnings about ways it can go wrong. Specifically, Generative AI trained on LLMs has been known to “hallucinate” and produce false statements, and even the best-designed AI-enabled analysis systems can produce biased results. And in healthcare specifically, the danger lies in using data from an unreliable/not normalised source. If data ingested is not reliable, this can lead to poor performance and incorrect analysis.

To get real value from AI, organisations have to feed it the right data — good, clean data that enables them to leverage AI to deliver safe, unbiased analysis, and correct, appropriate, personalised care. We’ve all heard the saying “garbage in, garbage out”, but what does that really mean, and how can organisations effectively clean out the “garbage” data?

Data expert insight “Effectively managing and utilising the diverse types of data within healthcare organisations often requires adapting to various data formats. These data formats, which may not be natively supported by all data platforms, underscore the unique challenges in dealing with healthcare data. Moreover, the sensitive nature of healthcare data imposes stringent governance requirements, encompassing data sovereignty, retention and security policies. By acknowledging and navigating these complexities, healthcare organisations can develop solutions that are not only efficient for current needs but are also well-positioned to capitalise on the rapid advancements in artificial intelligence and other emerging technologies.” 

Cian Clarke, Solutions Principal, Nearform

Healthcare organisations of all types collect, create, and share a staggering amount of data. Without it, AI wouldn’t be of much use. But when that data includes duplicate entries and incomplete or incorrect information, any analysis or output is compromised. Data cleansing ensures that all information is accurate and consistent, and can also lead to increased shareability and interoperability among different systems. 

As part of a project to build a software platform for medical diagnostics company Renalytix, Nearform developed a system to optimise the company’s data validation. New processes and systems led to faster data input, and cleaner information for their groundbreaking AI-enabled patient biomarker analysis.

The next few years will bring additional advances

Creative and innovative applications of AI, coupled with clean, secure data, open a variety of beneficial possibilities in both the patient-facing, and business side of healthcare. Undoubtedly, the next few years will bring additional advances that will continue to deliver more personalised and effective outcomes for patients, and drive the growth of forward-thinking healthcare organisations and ambitious enterprises everywhere.

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