Vox sector special: five companies transforming healthcare with AI

Artificial intelligence has permeated almost every industry since its genesis, broadly helping businesses to improve efficiency, accuracy and decision-making. 

Though the disruptive effects of AI have been experienced by virtually every industry, AI’s capabilities when applied to healthcare arguably holds the most significance. Not only can technological advancements facilitated by AI in healthcare produce a more productive, thriving society, but the implementation of AI into healthcare systems can ultimately alter the outcome of life-or-death scenarios. 

Debatably still in its nascent stages, AI has the potential to further revolutionise the industry, revolutioninising aspects of healthcare systems such as drug development, medical imaging and patient care. The scope by which AI has benefitted and continues to benefit the healthcare industry is huge, entering into areas that were once only seen as the province of human expertise. 

The healthcare system has been confronted with crucial challenges in recent years, namely a scarcity of healthcare workers during the COVID-19 pandemic. Looking ahead, The World Health Organisation forecasts that there will be a shortage of 10 million healthcare workers by 2030. 

In the UK specifically, workforce shortages in the NHS are particularly prominent in the aftermath of Covid-19, which drove doctors and nurses to leave the sector, along with clinical staff from abroad choosing not to come to the UK following Brexit. 

All of these factors combined make the need for AI in healthcare increasingly urgent and explain a growing interest in understanding how AI can improve healthcare systems, ultimately improving time and cost savings. 

What is AI in healthcare?

In simple terms, AI in healthcare is the use of machines to analyse medical data, usually with the goal of predicting a particular outcome. The sheer volume of healthcare data available drives the necessity of AI to make sense of and use this data in a productive way. And the more data available, the more sophisticated and accurate machine algorithms can be. 

AI is getting increasingly better at supporting positive healthcare outcomes more efficiently, more quickly and at a lower cost. Its application in healthcare systems is far-ranging, with notable examples across the entire healthcare value chain, including diagnosis, treatment, drug discovery and administration. 

Early disease diagnosis is one area where AI’s application has generated remarkable outcomes, particularly for life-threatening illnesses such as cancer. For example, expansion into cancer detection through AI has shown that the technology can diagnose colon cancer more accurately than a trained pathologist. In one study, researchers gathered medical images of colon cancer from 8,803 participants from various independent centres and used them to train a machine learning program. After the development of a performance measurement tool, it was found that the average pathologist scored 0.969 for accuracy of colon cancer identification compared to 0.98 for machine learning. 

AI capabilities can also be used in early disease detection in the form of a blood draw, which is a desirable alternative to other procedures that are heavily invasive, such as spinal fluid draws or tissue biopsies. 

Such technology is also hugely impacting research and innovation in healthcare, by utilising quicker drug screening methods, substituting control groups in studies with artificial data, and making predictions about treatment responses for particular medical conditions.

Finally, its application into precision medicine - which takes into account a person’s genetic makeup, environment and lifestyle to deliver personalised care and treatment - is also helping clinicians better healthcare outcomes for patients. Considering a person’s genetic makeup is particularly crucial when performing organ transplants, where technologies can be used to predict whether or not the new organ will be rejected by the immune system. 

AI in healthcare and the market 

With AI proliferating throughout virtually every corner of the healthcare industry, the global market size has already grown exponentially, and is set to grow at an even quicker rate over the next seven years to reach $188 billion by 2030. 

                                                      Source: Statista  

A key factor for the growth of the market is the increase in the volume of healthcare data along with the rise in complexities of datasets driving the necessity for AI in healthcare. Furthermore, the use of AI in healthcare is being propelled by a combination of factors, such as advancements in computing power and reduction in hardware costs, an increasing number of cross-industry partnerships and collaborations, along with a growing disparity between the number of healthcare workers and patients. On top of this, the growing global geriatric population has played a significant role in driving the need for early disease detection and better comprehension of diseases during their early stage. 

In line with expectations for huge market growth, recent survey data collected revealed that healthcare industry professionals believe that their healthcare companies will prioritise AI as the main investment target over the next two years. At present, the data highlighted that digital media was the current investment priority - however, the percentage of healthcare companies prioritising investing in this technology is expected to drop by 16% over the next two years, with artificial intelligence projected to take the top spot. 

                                                     Source: Global Data 

Above all, AI in healthcare can generate an immense positive social impact. With areas of impact spanning diagnostics, clinical support decisions and care delivery, to name a few, AI has already demonstrated its capabilities in improving healthcare outcomes for patients across the globe. 

5 company’s investors should follow 

The influence of AI companies on the healthcare sector is already being felt by millions of people. Here are five companies that are revolutionising the industry today:

Intelligent Ultrasound

Intelligent Ultrasound (IUG)Follow | IUG is helping to transform the healthcare landscape by leveraging AI through its innovative ultrasound technology. The company uses AI algorithms to make ultrasound simpler to use, and easier to learn by providing guidance and support to medical professionals while they are scanning. Consequently, Intelligent Ultrasound’s AI technology can assist clinicians by leading them to more accurate diagnoses, meaning that patients can receive appropriate treatment in a timely manner. 

Among their AI-product offering is ScanNav assist, software that is embedded in ultrasound scanners that provides real-time guidance to clinicians and is used to act like a scanning assistant comparing images to standard criteria, in turn reducing operator variability and subsequently improving patient outcomes. 

In 2020, Intelligent Ultrasound was targeted by med-tech giant GE Healthcare to provide software for their Women’s Health ultrasound system Voluson SWIFT. The success of the partnership led to Intelligent Ultrasound signing an extension to their agreement in January 2022, utilising the software in a new segment of automated ultrasound image analysis, outside of the original agreement. 

Intelligent Ultrasound’s other AI-powered product, ScanNav™ Anatomy Peripheral Nerve Block is a tool that helps doctors perform ultrasound-guided nerve blocks more accurately. When using an ultrasound machine, doctors can use ScanNav™ to see a color overlay on the ultrasound image that highlights important anatomical structures.

On the financial front, the clinical AI-related revenues of Intelligent Ultrasound are anticipated to have increased by more than 220% compared to the prior year, albeit from a low base.  The sales surpassed the market's expectations, which indicates that their AI-related products are continuing to gain momentum. 

Kooth 

Digitalised healthcare services offer an alternative that can help to ease the number of people entering a hospital setting, thereby alleviating strain on healthcare systems. 

Kooth (KOO)Follow | KOO is a UK-based company contributing to this, by providing mental health and wellbeing support to people throughout the country through its online web-based digital platform. The company aims to create welcoming communities and services for everyone, targeting adults, young people, employers and students alike. 

So, how does artificial intelligence play a role in this? The sensitive data collected from users on Kooth’s digital platform (if the user permits this) is anonymised, and as a result, Kooth can ethically use this wealth of data gathered. Using this information, Kooth then leverages artificial intelligence to improve its service and to support users of the platform in achieving the best possible outcomes for their mental health. The company notes that artificial intelligence is key to improving their services, covering projects such as early recognition of risk, matching service user needs to the most appropriate practitioner, and cutting waiting times for support. 

The company’s CEO is explicit in their vision for the future of personalised digital mental healthcare: “it will look … like Netflix; guiding you to relevant content, self-help programmes, and one-to-one support based on what's helped people like you in the past.” The use of AI will be crucial in the company achieving this vision. 

From a financial perspective, the company demonstrated strong progress in its 2022 annual trading report, reporting that revenue is expected to be between £19.6m to £20.2m, driven by 15% growth year-on-year. The company’s recent expansion of the platform in the US demonstrates Kooth’s worldwide applicability, and the adoption of AI to improve its healthcare services has been felt by millions of people across the UK and US to date. 

LungLife AI 

LungLife AI (LLAI)Follow | LLAI is a diagnostic company focused on the early detection of lung cancer from a simple blood draw enhanced by artificial intelligence. 

Their revolutionary LungLB® test stamps out the disadvantages of conventional diagnostics methods. For example, a CT scan is excellent at picking up lung nodules, but not so great at identifying if the lung nodule is potentially cancerous. By the time the patient returns to the hospital to see if the nodule has grown in size, and the nodule happened to be a tumor, then the patient may have a significantly reduced choice of cures. 

Another common diagnostic option is to perform a biopsy, but this is an invasive procedure that has a 1 in 5 chance of leading to an adverse event (such as a lung collapsing). What’s more, 40% of all lung biopsies return a no lung cancer diagnosis.

LungLife AI’s LungLB® test is well-positioned to bring certainty into early-stage diagnosis confirming for clinicians whether to refer on to biopsy where the LDCT Scan reveals suspicious nodules. The company has paired this simple blood draw with artificial intelligence to help with decision-making in the management of lung nodules and lung cancer. 

Data released in February of this year confirmed that evaluating potentially cancerous nodules using LungLB® was projected to be cost-effective in US healthcare system, demonstrating yet another way that AI can help to solve cost and efficiency issues within the healthcare sector. LungLife AI’s LungLB® is set to be commercialised in the US in H2 2023. 

Poolbeg Pharma 

On average, it takes over 10 years to get a drug from phase 1 to regulatory approval and costs over $1bn. These figures are in tension with the fact that new drugs are continually required by healthcare systems to address a range of unmet medical needs across diverse therapeutic areas and pharmaceutical industries. 

AI can be instrumental in speeding up the drug discovery process and allows for greater success rates. One trailblazing healthcare company using AI for the drug discovery process is Poolbeg Pharma (POLB) Follow | POLB.

Poolbeg Pharma is a UK-based clinical-stage infectious disease pharmaceutical company, aiming to develop multiple products faster and more cost-effectively. The company’s utilisation of AI to improve this discovery process is twofold: identify drugs that are more likely to succeed; and improve clinical trial design by identifying patients most likely to benefit. AI can tilt the odds of drug development by identifying products that are more likely to succeed in the clinic.

The company is using AI to identify more drug targets for two common diseases that affect populations around the globe, human influenza and respiratory syncytial virus (RSV). To reach outcomes in a quick and cost-effective manner, Poolbeg has joined forces with AI-focused companies CytoReason and OneThree Biotech. 

The partnership with CytoReason was announced in November 2022, aiming to use their tailored AI model to unlock clinically meaningful insights into influenza infection and recovery through analysis of Poolbeg's human challenge trial data. 

Poolbeg’s focus on human influenza drug discovery comes at a time when the threat of influenza is significant - estimated to be around an eighth of the world's population affected by this illness. The partnership addresses the key issue of time in the drug discovery process, as Poolbeg plans to source drugs with existing Phase 1 safety data that can immediately continue clinical development to rapidly generate early human proof of concept data for influenza treatment.

Poolbeg also partnered with AI-led company OneThree Biotech in February of 2022 to identify new drug targets and treatments for Respiratory Syncytial Virus. Working together, the collaboration aims to build a tailored AI analysis model, using human challenge trial data to identify disease-relevant cell signalling pathways which could lead to novel drug targets. In December 2022, Poolbeg announced that the collaboration had identified a number of promising drug candidates. Again, Poolbeg notes that the analysis will prioritise drugs with existing Phase I safety data, majorly reducing time and costs. 

Both partnerships are industry firsts, with the OneThree Biotech partnership marking the first time AI analysis has been undertaken on RSV human challenge trial data and equally, the collaboration with CytoReason marking the first time AI has been used to analyse influenza human challenge trial data. In this sense, Poolbeg is yards beyond its competitors and demonstrates how AI can be incredibly beneficial in speeding up the otherwise timely and costly drug discovery process. 

Verici Dx

Verici Dx (VRCI)Follow | VRCI is a US-based genetic diagnostics company, using AI to assist with analysing and interpreting large data sets arising from RNA sequencing, which in turn can assist in predicting if the patient’s body will reject or tolerate a transplanted organ. 

At the moment, huge efforts are being made among the healthcare community to investigate novel biomarkers that enable transplant clinicians to identify patients at risk. Biomarkers can indicate whether the patient will have an adverse immune response to a new organ post-operation. 

The company has a focus on kidney transplants, as 1 in 7, or 15% of adults, in the US suffer from chronic kidney disease. The loss of kidney function - an inability to filter waste products from the blood and convert them to urine - is the most common reason for needing a kidney transplant. But this is a risky procedure, with rejection rates of donor parts around 35%-50% caused by the immune system ‘attacking’ the transplanted kidney. 

This is where Verici Dx comes in, developing advanced clinical diagnostics in kidney transplants based upon the patient’s own biological systems. The company launched their immuno-diagnostic, Tutivia, earlier this year, designed for all patients and covering all types of rejection, so that rejection rates can be diminished, in turn reducing healthcare costs. 

The success of Verici Dx’s products lies in the ability to accurately foresee how the body will respond to a new organ, and employing artificial intelligence to make sense of large data sets, makes this possible, learning from past data encountered to enhance the accuracy of its data predictions.