Early successes show AI’s capacity to improve the efficiency of drug discovery and hospital workflows, lowering healthcare system costs and enhancing access to treatments.
Healthcare system cost inflation is becoming a defining political challenge for rapidly ageing societies in North America, Europe and Asia. Having risen steadily in recent decades, around 10% of global GDP is now allocated to healthcare; in the US, it is almost 17%.
Could artificial intelligence (AI) be the miracle cure? Signs are encouraging that it could transform the efficiency of the drug development process, accelerate innovation and enable more drugs for more therapeutic areas to successfully reach the market. It is also already being used to enable hospitals to operate more efficiently and effectively.
Accelerating drug development
Drug discovery and development is notoriously inefficient: it typically takes a decade from inception for a product to reach the market. Assuming a 20-year patent on new molecular entities, pharmaceutical companies then have about 10 years to recoup their investment and turn a profit.
If AI models can accelerate the preclinical stages of drug development, it could transform the industry’s economics. Blockbuster drugs generate billions of dollars in annual revenues: two or three additional years of sales under patent protection could materially enhance returns on investment.
While there have not yet been any fully AI-designed drugs approved by the US Food and Drug Administration (FDA), multiple standalone AI drug development companies have succeeded in advancing AI-generated drugs into clinical trials, and Big Pharma has been quick to ink partnerships with them.
AI seems particularly adept at the target validation and lead optimisation parts of the drug discovery process – essentially interrogating vast datasets to find the right biological target in the body for a specific drug molecule to bind to.
Most large pharmaceutical companies now integrate AI into their drug development process. Among those at the vanguard is Eli Lilly, which has launched an AI platform that offers access to drug discovery models trained on its proprietary molecule datasets.
Focusing clinical trials
The end-to-end costs of successfully developing a new drug have increased from an average of US$1.3bn to US$2.2bn over the past decade. Over two-thirds of costs are incurred at the clinical trial stage, when a drug goes into human testing.
If AI can improve the speed and success of trials – currently only around 10% of drugs pass this stage – the industry’s research and development costs per drug could tumble. Savings should ultimately enable more drugs to be brought to market, widening patient access and improving health outcomes.
The leading clinical research organisations (CROs), which partner with the pharmaceutical industry for drug research, trials and commercial support, are fully embedding AI into their workflows.
In designing a clinical trial, AI is being used by the likes of IQVIA, a US-listed CRO, to identify the sub-group of patients most likely to respond to a drug. It can help define inclusion and exclusion criteria, optimise site selection and dosing, and draft filing submissions.
Regulators are supportive of the industry’s use of AI to improve efficiency. Indeed, the FDA now uses AI within the regulatory review process: its own large language model, Elsa, aims to streamline scientific evaluations, accelerate clinical trial protocol reviews and identify drug manufacturing sites that need inspecting.
Streamlining hospital workflows
Hospitals represent the largest area of healthcare spending in the US, comprising 31% of total expenditure in 2023. Financial savings in hospital administrative and operating processes are being targeted through the application of AI to automate revenue cycle management, managing suppliers and patient scheduling.
As well as yielding efficiencies for hospital operators – who often have fine profit margins – AI can be used to help improve patient care and working conditions for healthcare professionals. Burnout risk is a systemic concern, with a forecast shortage of more than 10mn healthcare workers globally by 2030.
HCA Healthcare, the largest hospital operator in the US by revenues, has worked with Google to develop a ‘Nurse Handover’ app that uses AI to analyse electronic health records and create concise summaries for nurses. By mitigating the need for time-intensive verbal handovers at shift changes, nurses’ administrative burdens are reduced and more time can be devoted to patient care.
Healthcare system costs are meanwhile being lowered – alongside better patient outcomes – through the integration of AI in medical technologies. US surgical robotics company Intuitive Surgical, for example, incorporates AI across its product suite, enabling better visualisation for surgeon training and learning, and helping hospitals better manage their fleets of robots.
Leveraging AI’s transformational potential
AI undoubtedly has the potential to drive a step change in healthcare sector productivity, from unlocking drug discovery to more efficient hospital workflows.
Despite this promise, the pace of progress could be relatively slow. Regulators will be thinking about the implications of overreliance on AI, potential biases in patient care from training data, and where liabilities lie if something goes wrong. These risks undoubtedly need to be managed.
Overall, though, early applications of AI indicate how it can demonstrably advance the quality and accessibility of healthcare. Though disruptive for some business models, opportunities are likely to be created for innovative companies that leverage AI’s transformative potential.
By Jelena Boskovic, CFA®, Impax Global Social Leaders Fund
