- Recently, Peter Arduini, CEO of GE Healthcare, proclaimed that the software development business “is central to our growth strategy”
- Although AI is in its infancy, AI technology has become embedded in all aspects of care journeys: from diagnosis to recuperation at home; from prevention to improved lifestyles
- Notwithstanding, many established MedTech leaders still advocate the production of physical devices for episodic surgical interventions marketed by B2B business models in wealthy regions of the world
- Jenson Huang, a key opinion leader from the AI industry recently stressed how rapidly AI technologies have advanced over the past decade and predicts that AI “will revolutionize all industries” over the next decade
- If Huang is right and more MedTech leaders bet their future growth on innovative AI driven strategies, healthcare systems will be soon re-imagined
On 16 February 2023, a Wall Street Journal article announced, “GE Healthcare Makes Push into Artificial Intelligence”. The company, spun-out of General Electric (GE) in January 2023, is now an independent enterprise traded on Nasdaq, and Peter Arduini, its Chief Executive, says that the software development business “is central to our growth strategy”. In the first instance, GE Healthcare is planning to apply artificial intelligence (AI) and machine learning (ML) techniques to masses of disparate data generated by hospitals during patients’ therapeutic journeys, to enhance hospital services, improve patient outcomes and reduce healthcare costs.
Arduini is right. However, to fully appreciate the future potential impact of AI technologies on the medical technology industry and healthcare systems, we need to engage with key opinion leaders (KOL) from the AI industry. One such leader is Jenson Huang, a Taiwanese-American electrical engineer, founder, president and CEO of Nvidia, a semiconductor company launched in 1993. Today, it is a world leading, Nasdaq traded AI technology enterprise with a market cap of ~US$509bn, annual revenues of ~US$27bn and >26,000 employees. To put this into a perspective: if AI was the mid-19th century gold rush in the US, then Nvidia would be the producer of pickaxes for the hundreds of thousands of prospectors drawn to Sutter's Mill in Coloma, California. But before engaging with Huang, let us get a better understanding of the state of healthcare systems, AI and ML.
In this Commentary
This Commentary discusses Arduini’s proposition that AI big-data driven software strategies, which aim to enhance patient outcomes and reduce healthcare costs, are key to the growth of medical technology companies. This raises a question whether traditional MedTechs, producing physical devices, and marketing them with B2B business models will create sufficient growth and value over the next decade to satisfy their investors. Although AI technologies are in their infancy, they have already entered many areas of healthcare and are well positioned to play a significant role in future, re-imagined healthcare systems. The Commentary describes AI and ML, provides a brief history of AI, outlines its recent uptake in healthcare and notes how AI technologies have been used by both agile start-ups and giant techs to develop ‘big ideas’ with the potential to disrupt the medical technology market. We briefly describe six start-ups that have leveraged AI to enter the MedTech market and by doing so, increased the competitive pressure on traditional enterprises. Although AI technologies have only recently been introduced to healthcare systems, they are embraced by the FDA and feature in many aspects of patients’ therapeutic journeys: from diagnosis and treatment to recovery and rehabilitation at home. The Commentary takeaways suggest that the actions of industry leaders like Peter Arduini will have a significant impact of the shape on healthcare systems over the next decade.
Healthcare in crisis
Healthcare systems throughout the world are in crisis and experiencing large and rapidly growing care gaps,which we have described in previous Commentaries. These are created by growing shortages of health professionals and a vast and rapidly growing demand for care from aging populations; a significant proportion of which present with chronic lifetime diseases, such as heart disorders, diabetes, and cancer, that require frequent physician visits and more resources to treat. Such care gaps result in millions of people having difficulties gaining prompt access to health services, which delay diagnosis, worsen patient outcomes, and increase treatment costs.
Addressing such issues requires re-imagining healthcare systems. Commercial enterprises have a role to play. Like GE Healthcare, agile start-ups and giant techs have embraced new and evolving AI technologies to create innovative offerings that provide solutions to care gaps predicated upon patient-centric, AI big-data strategies. However, many traditional medical technology companies have not developed software offerings and continue to focus on the production of physical devices, and B2B business models to support episodic hospital-based surgical interventions.
Brief history of AI
AI refers to the development of computer systems that can perform tasks, which typically require human intelligence, such as decision making and natural language processing. The technology is based on the premise that machines can learn from data, identify patterns, and make recommendations with minimal human intervention. ML algorithms [instructions carried out in a specific order to perform a particular task] build mathematical models based on sample data, referred to as "training data", to make predictions or decisions without being explicitly programmed to do so.
AI has been around since the 1950s. The term was coined by computer scientist John McCarthy in 1956 at the Dartmouth Workshop in Hanover, New Hampshire, USA. In the early days of AI, scientists focused on building computers that could think, reason, and solve problems like humans. In the 1960s and 1970s, AI research concentrated on developing more advanced algorithms and techniques for programming computers to solve tasks. This resulted in expert systems, which used knowledge-based decision making to solve complex problems. In the 1980s, AI shifted towards ML, which allowed computers to learn from experience by enabling them to recognize patterns and make decisions based on data. In the 1990s, AI developed methods for robots to interact with their environment and learn from experience. This led to autonomous robots that can navigate and perform tasks in the real world. Today, AI research is focused on creating more intelligent and autonomous systems and is used in a wide range of applications, and increasingly in healthcare.
AI and healthcare
AI’s use in healthcare can be traced back to the 1970s, when researchers developed expert systems that could diagnose and treat certain medical conditions. Early AI healthcare applications were limited by the availability of data and the dearth of computer power. In the 1990s, as computing power increased and the internet became more widely available, AI began to be used more extensively in healthcare. One of the early applications was in radiology, where it was used to interpret medical images. Other applications included decision support systems for medical diagnoses and treatments, and natural language processing systems for medical documentation. In the 2000s, the use of AI continued to expand, with the development of ML algorithms that could analyze large datasets to identify patterns and make predictions. These were used in a variety of healthcare applications, including personalized medicine, drug discovery and medical imaging.
Today, AI benefits a wide range of healthcare applications from faster diagnosis to the prediction of pandemics, from clinical decision support to digital therapeutics. The aspiration of AI driven solutions and services in healthcare is super-human performance, free from errors and inconsistencies, and scalable to provide expert-level care across entire health systems. AI technologies have the potential to provide services that improve the accuracy and speed of medical diagnoses and treatments, monitor conditions, assist with recovery, support medicine regimens, facilitate personalized healthcare and reduce costs for providers. These functions are relevant in the context of attempts to narrow care gaps, but they require vast amounts of computing power, which most companies do not have in-house.
This is where cloud computing, and Nvidia's new solution come in. Dubbed "DGX Cloud", Nvidia’s offering is an AI supercomputer accessible via a web browser. The company has partnered with various cloud providers, including Microsoft, Google, and Oracle to develop the service, which provides enterprises easy access to the world’s most advanced AI platform and allows them to run large, demanding ML and deep learning workloads on graphic processing units (GPUs) to generate and implement ‘big ideas’.
New entrants to the medical technology market - agile start-ups and giant techs - often have ‘big ideas’; innovations with the potential to inspire stakeholders and disrupt the industry. By contrast, traditional MedTechs who do not employ AI strategies tend to have a dearth of big ideas and mainly focus their R&D spend on incremental improvements to their legacy devices. By contrast, new entrants have accelerated the use of AI, ML, and data analytics to help diagnose diseases earlier and monitor patients remotely. Further, they have championed wearable devices like Fitbits and Apple Watches that help people track their health metrics and allows them to make smarter decisions about their wellbeing. This is helping to transform the modality of healthcare from ‘diagnosis and treatment’ to ‘prevention and lifestyle’.