Artificial Intelligence becomes an essential part of future healthcare

In many developed countries, healthcare has become an unsustainable business in recent years, partly due to the aging population and prevalence of chronic diseases. In the US, healthcare spending grew 4.6 percent in 2019 alone, amounting to US$3.8 trillion, or 18 percent of the nation’s Gross Domestic Product (GDP). However, this increase in spending did not translate into better patient care, nor has it reduced resource scarcity and imbalance in the healthcare industry. How can we transform the healthcare sector to make it more efficient and sustainable? Artificial intelligence (AI) will play a critical role in the future of healthcare.  

The Seven Revolutions in Healthcare That Will Impact Your Life – Part 7

(Missed the previous one? You can read Part 6 here – Robots Become Reliable Assistants for Healthcare Professionals and Patients)

From fee-for-service to fee-for-value

In the current healthcare system, doctors and other healthcare providers are paid for the number of patients seen or procedures performed. This fee-for-service model means that healthcare providers are rewarded for volume rather than for value.

What is the biggest barrier to practicing medicine today?


“‘Production pressure’ – the requirement to see more patients in less time because of the misconception that the value of a physician is determined by the number of patients he/she sees”– Lucian Leape, physician and professor at Harvard School of Public Health, leader of patient safety movement.1


Value-based healthcare rewards healthcare providers based on the quality of care they provided. The implementation of AI can greatly improve the value of healthcare providers by making sense of medical data, automating routine procedures, and improving efficiency and effectiveness.

AI will reshape radiology

Machine learning is great at recognizing patterns, which has translated into fast progress in analyzing medical images.

While AI played no role at all in radiology as recently as 2015, 30 percent of radiologists had adopted the technology by 2020, according to a study by the American College of Radiology.2

One of the diseases where machine learning has proven its value in early diagnosis and prognosis is dementia, the leading cause of disability and dependency among the elderly. Diagnosing dementia in an early phase is a challenge due to the lack of symptoms and visible changes in brain images at the preclinical stage. By studying patterns in thousands of brain scans from dementia patients, scientists in the UK have developed an algorithm that can detect early signs of dementia in brain scans that are not visible even to radiologists. The algorithm has reduced the diagnosis procedure from several scans and tests across several weeks to just one single scan. 

Data experts believe that AI, rather than replacing radiologists Altogether, will automate redundancies, prevent mistakes, and optimize how radiologists practice, which will ultimately lead to better patient outcomes.3

AI and big data are advancing precision medicine

All humans are different from one another due to genetic, environmental, and lifestyle factors. But in conventional medicine, patients with the same disease are typically given the same standard treatment. This is often the reason for unreliable outcomes. In precision medicine, medical decisions are tailored to a subgroup of patients. Multidimensional datasets are used to train algorithms to identify subgroup patients with similar biological and other characteristics. Precision medicine offers clinicians the opportunity to prepare tailor-made preventative or therapeutic interventions. It has already led to promising results in AI-powered prognosis for cancer and cardiovascular disease.4

In 2018, Chiba University set up the first AI center in a medical school in Japan. The center uses AI to analyze genomic and clinic data such as gene expression, metabolism, gut microbiome, environmental exposures, and lifestyle factors. By doing so, researchers are able to predict the efficacy of treatments and future outcomes. In one of their studies, researchers used machine learning to identify a group of early-stage ovarian cancer patients who would respond poorly to a particular treatment beforehand. This finding gave the clinicians the opportunity to design a new treatment approach for the subgroup of patients.


“Predictive algorithms can help identify disease groups that haven’t been recognized by clinicians, as well as guide the selection of personalized treatment options for these patients.” – Eiryo Kawakami, professor of artificial intelligence medicine at Chiba University.5


Translate AI from labs to real-life patient care

To laypeople, the notion of an AI healthcare solution may sound like a complex one. But Professor Sebastien Ourselin, Head of the School of Biomedical Engineering & Imaging Sciences at King’s College London, says the new approach will make his work easier. Ourselin and a team of data scientists and clinicians at AI Centre for Value-Based Healthcare are working together with The National Health Service (NHS) and other partners to deploy AI solutions into real hospitals in the UK.

“AI is just a way to make sense of all of those data by training models which will hopefully be able to save us time in making the diagnosis, prognosis and be able as well to increase the effectiveness of the treatment.” – Professor Sebastien Ourselin, Head of School of Biomedical Engineering & Imaging Sciences at King’s College London6

The transformation of AI solutions from labs to real patient care is not an easy task. Medical data in the real world is often unstructured, comprehensive, and filled with various terms, abbreviations, and misspellings. The strategy of the AI Centre for Value Based Healthcare is to first convert static snapshots of clinical data into real-time, actionable analytics, then build an infrastructure to link data together and train the algorithm. Eventually, with the help of AI, actionable models are formulated that can be deployed in real hospitals. The NHS plans to deploy the first prototype in ten hospitals later this year. The full deployment of AI solutions will be carried out in the next two years.

Do you think we will see AI-powered healthcare solutions in real hospitals soon? Search “Future of healthcare” on the Supertrends Pro app and tell us about your thoughts on AI healthcare solutions:

This blog concludes our series on the future of healthcare. Thank you for following our ideas on what will happen in the future of healthcare and what it may mean to your life. Take advantage of the Supertrends Pro app’s free trial to make your voice heard on the “future of healthcare”. The final timeline will be revealed in November. Scroll down to the bottom of this page and sign up for our newsletter so you won’t miss it!


[1] Pittman D., 10 Questions: Lucian Leape, MD. MedPage Today. 12 January 2014. https://www.medpagetoday.com/PublicHealthPolicy/GeneralProfessionalIssues/43757

[2] Siwicki B., Mass General Brigham and the future of AI in radiology. Healthcare IT News. 10 May 2021. https://www.healthcareitnews.com/news/mass-general-brigham-and-future-ai-radiology

[3] Siwicki B., Mass General Brigham and the future of AI in radiology. Healthcare IT News. 2021.

[4] Uddin, M., Wang, Y. and Woodbury-Smith, M. Artificial intelligence for precision medicine in neurodevelopmental disorders. npj Digit. Med. 2, 112 (2019). https://doi.org/10.1038/s41746-019-0191-0

[5] Nature research custom media, Chiba University. Advancing precision medicine using AI and big data. Nature portfolio. Accessed on 27 August 2021. https://www.nature.com/articles/d42473-020-00349-9

[6] Ourselin S., The future of healthcare with artificial intelligence. 26 June 2021, Future of Healthcare (Webinar). NewScientistLive. https://app.konf.co/event/sJ0Vy6Kn/session/4499

Health, Artificial Intelligence


Jiqing Hansen

Having worked passionately for 15+ years in Medicine, I felt that I yearned to do something a little bit different, something that satisfies my curiosity and creativity, maybe something that helps to inform me and others what our world will look like in the future. That's when I took on the challenge of being the editor & expert relationship manager at Supertrends. I love the fact that I can still be in touch with my academic background when I am trying to understand and reach out to the experts in the most exciting fields. I also love the diverse and enthusiastic team at Supertrends. The best of all, I get to have a peek into the future, and I am at the position of helping many others to get the opportunity to look into the future.

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