By Kevin Kruczynski, Investment Manager, GAM Investments.
The healthcare stars shining through the AI revolution
It is not a novel observation that technological evolutions often appear to take more time than initially anticipated. When they do materialise, they tend to occur rapidly. This is attributable to the way technological advancements typically compound, with each new development enabling even grander achievements.
The current euphoria around Artificial Intelligence (AI) is reminiscent of this. Over the last year or so, the stars have aligned as advancements in algorithms, graphics processing unit (GPU) technology and data accessibility, enabled the launch of Chat-GPT. This brought AI into the limelight, and has sparked intense debate among investors, businesses and the broader public over its application and use. However, it is often forgotten that many organisations have been tinkering with innovation behind the scenes for decades to cultivate and harness its potential applications.
Healthcare – an area for early AI adoption
Healthcare has long been touted as a likely area for early AI adoption, as it offers the promise to enhance the quality,effectiveness and availability of care. It is a setting that creates a tremendous volume of data, which can be employed to train AI systems to improve diagnosis, treatments and prevention.
Healthcare professionals regularly make complex decisions based on incomplete information. Therefore, AI models offer great opportunities to analyse extensive data sets and offer valuable insights to assist the decision-making process.
Rudimentary forms of AI have existed for decades, such as Stanford’s MYCIN, an early AI tool developed in 1972 fordiagnosing blood infections. Computer-assisted detection systems using AI algorithms to scrutinise medical images for potential irregularities emerged during the 1980s and 1990s. This led to significant changes in radiography. AI has also been developed to plan and guide radiotherapy treatment, with the aim of minimising damage to healthy tissue andimproving patient outcomes.
Source: Delveinsight
Given this context, it is unsurprising that disruptive healthcare companies within our portfolio use AI to improve theirproduct offering, while helping to widen their technological moats, or a company’s technological competitive advantage.
Which healthcare companies stand out in the AI revolution?
Intuitive Surgical, the leading provider of robotic surgical systems, has embedded AI to improve the accuracy and efficiencyof its Da Vinci systems for years. The AI-powered image guidance has paved the way for surgeons to see more clearly during surgery with data overlayed across the field of view, and AI-powered decision support tools helping to make betterdecisions.
Its smart staplers can measure tissue compression and make automatic adjustments to the stapling process to ensure optimal placement, reducing the risk of complications. The system can monitor the patient during a procedure and update the operating plan to preempt problems. With two decades of usage data, and over 12 million procedures alreadyperformed and recorded on its systems, Intuitive’s data gathering and AI model training capability significantly outperformsany of their emerging peers.
Omnicell, a market-leading provider of medication management solutions, uses AI to improve the accuracy andefficiency of its medication-dispensing systems. For example, analytics tools help hospitals track medication inventory levels, identify potential drug shortages and support reducing the $600bn medical maladherence problem. This can reduce the current considerable amount of time spent managing medication shortages.
Dexcom, the leading maker of continuous glucose monitoring devices, uses AI to improve the accuracy of glucose readingsand personalise alerts and alarms to help patients understand and make better-informed decisions to manage diabetesrisks.
Oxford Nanopore develops nanopore-sequencing technology that is faster, cheaper, and more portable than traditional DNA-sequencing methods. It uses AI to make its technology more accurate, efficient, and powerful. Its AI data analysisalgorithms have helped to improve its system accuracy from 85% back in 2015 to above 98%.
Source: Precedence Research
This improvement has made nanopore sequencing a more powerful and versatile tool for various applications. For example, Nanopore is now used to diagnose diseases, monitor environmental changes and develop new drugs.
