Healthcare costs are rising faster than GDP in most countries and this is completely unsustainable. Urgent action is needed to mitigate this trend. The AI & IoT revolution promises to reduce costs throughout the entire healthcare pipeline. From automating simple customer care to improved diagnostics, high-throughput image-based diagnosis all the way to personalized medicine and drug discovery. The coming decade will see enormous progress in the reduction of healthcare sector costs using these emerging technologies.
Over the course of the past two decade’s healthcare spending as a percentage of GDP has increased from 8% to nearly 10% in OECD22 countries. Healthcare spending is an essential component for society but if these costs increase faster than economic growth, simple logic deduces that large systemic problems will arise. This can take many forms such as a reduction in the quality of care, a reduction in accessibility by less privileged parts of society, increased waiting times, or some combination of these. This is obviously very undesirable for society, patients, and the medical sector. (Figure 1)
To make matters even more stressful, Western countries are facing a tsunami of aging population demographics. In 2022, nearly 10% of the population was aged 65+, by the year 2050, this is projected to increase to over 16% of the global population. As aging is the number 1, independent risk factor linked to non-communicable diseases, we can expect the increase in the elderly population to come with a host of chronic illnesses on the population level. (Figure 2)
To compound these already worrisome trends, The World Health Organization projects that there will be a shortage of over 18 million healthcare workers globally by 2030.
Overall this paints a rather bleak picture. However, the revolution in digital diagnostic tools, novel biomarkers, and Artificial Intelligence makes this author feel confident that we will not only overcome these challenges in the Western healthcare sector but improve care for patients across the board.
According to a Nature Digital Medicine review by Mesko et al. 2020, Artificial Intelligence is expected to profoundly alter the practice of medicine. Furthermore, this new field of medicine is growing in an exponential fashion if we analyze PubMed citations. (Figure 3)
According to this author, AI can be defined as follows: “A.I. is an interdisciplinary field spanning computer science, psychology, linguistics, and philosophy, among others. According to its simplest definition, artificial intelligence (A.I.) is intelligence demonstrated by machines. It is sometimes also described as “machines that mimic cognitive functions that humans associate with the human mind, such as learning and problem solving”
According to a review by Benjamens et al. 2020, there were over 29 FDA-approved AI algorithms, spanning radiology, cardiology, ophthalmology, endocrinology, internal medicine, emergency medicine, and oncology. (Figure 4)
Since this publication, the total list of tools has exploded. A complete list can be found via the Medical Futurist at the following URL: https://medicalfuturist.com/fda-approved-ai-based-algorithms/
With a special focus on radiology and pathology that are being revolutionized. In these fields alone there are over 392 registered AI-based tools.
The long-term potential of AI doesn’t stop with ultra-specialized products. Just a few months ago, DeepMind a subsidiary of Alphabeth announced the creation of Med-PalM M. This system is described in detail in an article by emerging technology specialist Peter Xing.
This novel Multimodal Generative AI model simultaneously understands clinical language, imaging, and genomics. In total this system can perform 14 diverse biomedical tasks, outperforming human counterparts in many of these tasks as well. (Figure 6)
Driven by sensors, data gathering advanced laboratory diagnostics, and Artificial Intelligence. The concept of designing personalized Digital Twins of patients is a near-term possibility. With the potential set to profoundly affect all facets of the entire healthcare sector, be it in the realm of individual human suffering, workflow of medical doctors who are currently in growing scarcity, and even all the way to drug discovery, potentially leading to billions in savings for society.
In the simplest terms, using real-time highly accurate health measurement tools both from sensors and from wet-lab tests, clinicians are able to create a highly accurate digital surrogate of any patient.
These concepts have been extensively reviewed by several authors (references 5 to 8). This technology is already reaching clinical relevance with various case studies already having been conducted.
The potential of this technology is immense, from highly personalized medicine, to truly preventive analytics to next-generation drug discovery. Reviewed by this author in Longevity Report.
The Western healthcare system faces systemic challenges frightening its very core and social access. But on the horizon the dual Revolutions in Artificial Intelligence and Digital Twin based health surrogates are set to revolutionize every aspect of medical practice, creating not only a beacon of hope for current issues plaguing the sector but a bright tower of light. These new technologies will usher in an unprecedented period for medicine, leading to improved disease management, patient care, and quality of life for millions of healthcare participants.
1. Meskó, B., Görög, M. A short guide for medical professionals in the era of artificial intelligence. npj Digit. Med. 3, 126 (2020). https://doi.org/10.1038/s41746-020-00333-z
2. Benjamens, S., Dhunnoo, P. & Meskó, B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. npj Digit. Med. 3, 118 (2020). https://doi.org/10.1038/s41746-020-00324-0
5. Björnsson, B., Borrebaeck, C., Elander, N. et al. Digital twins to personalize medicine. Genome Med 12, 4 (2020). https://doi.org/10.1186/s13073-019-0701-3
6. Sahal R, Alsamhi SH, Brown KN. Personal Digital Twin: A Close Look into the Present and a Step towards the Future of Personalised Healthcare Industry. Sensors (Basel). 2022 Aug 8;22(15):5918. doi: 10.3390/s22155918. PMID: 35957477; PMCID: PMC9371419.
7. Kamel Boulos MN, Zhang P. Digital Twins: From Personalised Medicine to Precision Public Health. J Pers Med. 2021 Jul 29;11(8):745. doi: 10.3390/jpm11080745. PMID: 34442389; PMCID: PMC8401029.
8. Sun T, He X, Li Z. Digital twin in healthcare: Recent updates and challenges. Digit Health. 2023 Jan 3;9:20552076221149651. doi: 10.1177/20552076221149651. PMID: 36636729; PMCID: PMC9830576.