The March 30, 2024 issue of The Economist includes a special section on Health and AI, with six articles on the topic. “Artificial intelligence (AI) is generating excitement and hyperbole everywhere, but in the field of health care it has the potential to be transformational,” said the lead article, “The AI doctor will see you … eventually.” “In Europe analysts predict that deploying AI could save hundreds of thousands of lives each year; in America, they say, it could also save money, shaving $200bn-360bn from overall annual medical spending, now $4.5trn a year (or 17% of GDP).”
Healthcare is a system of coupled systems, encompassing medical and pharmaceutical research; the delivery of healthcare to patients by a variety of practitioners, including hospitals, physicians, nurses, and pharmacists; and the insurance companies and governments that pay for healthcare. It’s a highly regulated industry, which creates major barriers for the sort of innovative start-ups that have been the engine of transformation in other sectors. In advanced economies, a handful of companies, — e.g., Cerner, Epic, Athena, — support large healthcare providers with proprietary platforms that don’t interoperate with each other, making it difficult for smaller companies and startups to develop innovative AI tools and applications as has been the case in other industries.
There’s considerable hope that AI could help us better deal with the inherent complexity of the healthcare sector. “AI systems can enhance diagnostic accuracy and disease tracking, improve the prediction of patients’ outcomes and suggest better treatments,” notes The Economist. “It can also boost efficiency in hospitals and surgeries by taking on tasks such as medical transcription and monitoring patients, and by streamlining administration. It may already be speeding the time it takes for new drugs to reach clinical trials. New tools, including generative AI, could supercharge these abilities.”
However, “although AI has been used in health care for many years, integration has been slow and the results have often been mediocre.” Among the major reasons for the slow progress are the concerns about accuracy and trust necessary to protect patients’ safety. “Improving accuracy and reducing bias in AI tools requires them to be trained on large data sets that reflect patients’ full diversity.” But, while there’s lots of available health data, the data is highly fragmented making it difficult to use properly.
“Despite important gains in the last two decades, made possible by significant investment by payers, providers, and the federal government in electronic health records (EHRs), … the promise of digital health remains illusory,” said a paper published by the US National Academy of Medicine in June of 2022, “The Promise of Digital Health: Then, Now, and the Future.” “The ability to use interoperable digital technology to improve the effectiveness, efficiency, equity, and continuity of care remains substantially conceptual.”
Health care is controlled by strict rules. In many countries regulatory authorities are struggling to keep up with the rapid pace of AI-based innovations, and may lack the expertise to approve new AI-based health tools. Not only is health data fragmented, but so are the healthcare systems and regulations in different countries, making it harder for countries to work together and learn from each other.
“A less complex international regulatory system would also help create a market in which small companies can innovate,” notes The Economist. “Poorer countries, with less developed health infrastructure, have much to gain from introducing new tools, such as an AI-powered portable ultrasound device for obstetrics. Because the alternative to an AI tool is often no treatment at all, they may even be able to leapfrog the entrenched health systems of rich countries.”
The continuing rise in healthcare costs, especially in the US, is another serious problem. According to the Centers for Medicare & Medicaid Services, healthcare spending in the US is projected to grow from around $4.7 trillion in 2023 (18% of GDP) to over $ $7 trillion in 2031 (20% of GDP).
“AI promises to cut medical costs by assisting or replacing workers, improving productivity, reducing errors and flattening or reducing spending, all while improving care.” This is desperately needed, but it will prove very hard to achieve for a number of reasons. Given the aging populations in most countries, the world could lack 10 million healthcare workers by 2030. New technologies may already account for about half of the annual growth in health spending and are likely to continue to increase costs and complexity. Moreover, leveraging innovations to save money is tricky because health systems prioritize improving care over reducing costs, and “redesigning processes to make efficient use of AI is likely to be resisted by patients and medics.”
“These obstacles are formidable but the potential benefits of using AI in health care are so vast that the case for overcoming them should be obvious,” notes The Economist. Let me briefly discuss a few of these benefits.
AIs will make health care safer and better
Powerful AI technologies are increasingly being used in a variety of research applications. “They can improve the choices researchers make about how exactly to edit genes; they are phenomenally good at making sense of big data from disparate sources; they can suggest new targets for drug development and help invent molecules large and small that might work as drugs against them.” However, the deployment of healthcare innovations beyond research labs has been heavily constrained by the scarcity of workers and knowledge. AI can provide considerable support on both these fronts.
“Various sorts of diagnosis in which AI is playing a role look ready to be transformed,” thus significantly improving the productivity of health-care systems. For example, patients will be able to access health information and monitor their follow-up treatments via AI-based chatbots and personal health monitors.
Less developed countries have the most to gain. AI can help turn simple equipment like stethoscopes into more capable smart tools. AI can help turn our smartphones into Star Trek-like tricorders, with the ability to measure heart rate, temperature, respiration, blood oxygen saturation and other bodily functions all at once. In addition, AI can deliver reliable, expert guidance to health-care workers around the world in their native language.
Artificial intelligence has long been improving diagnoses
Imaging is one of the areas where AI has shown the most promise over the past several years. “Of the 500-plus AI algorithms approved by the FDA, 75% are radiology-focused and 85% are imaging-focused,” said a recent article in Stanford Medicine Magazine. “In radiology, imaging technologies such as X-ray and MRI are used to diagnose patients, and the field has produced one of the few robust and consistent datasets in medicine.
In 2018 Stanford Medicine established the Center for Artificial Intelligence in Medicine and Imaging (AIMI), bringing together 50 faculty across 20 departments to conduct research on the use of AI techniques in clinically important imaging problems that would benefit patients. “AI can be, in some ways, superhuman because of its ability to link disparate data sources,” said Dr. Curtis Langlotz, professor of medicine and radiology and director of AIMI.
Dr. Langlotz’ lab has developed AI algorithms that are able to detect and classify disease on medical images by finding linkages between imaging and genomic information that humans couldn’t possibly make. More recently, his lab developed natural language processing methods that analyze the clinical reports from radiologists to create annotated image training sets in order to provide real-time decision support systems to help radiologists improve accuracy and reduce errors.
Can artificial intelligence make health care more efficient?
“Economists think technology has been responsible for between 25% and 50% of growth in health expenditure in OECD countries over the past 50 years, growth which has seen the sector’s share of GDP grow relentlessly. In many of those countries it has achieved much. And yet, after decades of costly effort, stories still abound of incompatible IT systems, confidentiality breaches and paper records that need to be held on to in parallel to electronic health records. Is there any reason to think that AI can really sort this out?”
According to The Economist, high administrative costs account for 30% of the reasons the US spends considerable more in health care than comparable countries. Those trillion-dollar opportunities are attracting the attention of America’s tech giants who believe that large language models (LLMs) and other powerful AI tools are well suited to the job. “The fact that the biggest companies in AI see health care as a place to compete is a genuine cause for optimism.”
One such AI application is Google’s Med-Palm2, an LLM specifically designed to provide high quality answers to medical questions and to summarize health information during patient handoffs and staff shifts. Nuance, a company acquired by Microsoft in 2021 uses AI-based voice transcription to help doctors with administrative tasks like creating clinical notes and electronic health records.
“No one should think that what AI offers in terms of greater efficiency can be taken as a given,” said The Economist in conclusion. “Getting the most out of AI will require institutions which find change hard to undertake a lot of it. It will find regulators under proper pressure to ensure safety facing new challenges in terms of the scope of the technology and the speed at which it changes. And it will need economic incentives that realize the technology’s potential to save costs and lives. But if people can bring these shifts and reforms about, the machines will pay them back bountifully.”
Your delineation of areas of improvement is stimulating and they are experiencing rapid advancement. However, my "spidey sense" leads me to remember the fundamental fact that healthcare is a complex adaptive system. And, as the texts have taught us, changes in complex adaptive systems can often have unintended consequences. Caveat emptor!
Posted by: David Matthew | May 27, 2024 at 12:11 AM