AI & Machine Learning

AI in Healthcare

The world has an ageing population. It is expected that the percentage of the population over 60 will nearly double from the 2015 rate of 12% to 22% in 2050 (WHO, 2022). And in Scotland, the over 90 population is expected to double from the 2019 number of 41,927 to 83,335 by 2043 (Scottish Government, 2021). Our population over 60 is also expected be higher than the world average, with 25% being aged 65 or older by 2040 (Scottish Government, 2022). This drastic change in demographics will have a significant impact on healthcare. According to the World Health Organisation (WHO) (2022) “All countries face major challenges to ensure that their health and social systems are ready to make the most of this demographic shift”.

While life expectancy has been increased, health has not improved – in the UK, the burden of healthcare has become managing long-term conditions (cancer, heart disease diabetes, etc.) rather than infectious diseases (British Council).

One approach that is expected to have a significant role in healthcare going forward is artificial intelligence (AI).

Uses of AI in Healthcare

There are many areas of healthcare where AI can be implemented. These include prediction, early detection, treatment and research.

Prediction and Early Detection

Using statistics and analysis of imaging, AI can help predict individual patient risks and treatment response. This has been used by clinical radiologist Professor Declan O’Regan of MRC London Institute of Medical Sciences at Imperial and Professor Daniel Rueckert, head of the Department of Computing and leader of the Biomedical Image Analysis group, in MRIs of the heart. They are using this AI to identify the early stages of heart failure and to gain a greater understanding of the genetic effects on the heart, and in researching treatments (Imperial College London).

For many medical conditions, early detection and treatment have a significant impact on the effectiveness of the treatment. In a study of colon cancer identification, the machine learning program scored 0.98 compared to the average pathologists score of 0.969 (Echelon Health, 2022).

AI uses data to learn, and AI using big data can be used to identify patterns and correlations that can be used to improve healthcare (Mehta, Pandit & Shukla, 2019).


Diabetes management is an area of healthcare where AI has made an impact. There are wearable insulin pumps that continuously monitor glucose levels, and AI can use this data to more accurately predict the effects of food and insulin on blood sugar levels (Echelon Health, 2022). The use of AI in medicine includes triage, diagnosis, prediction, decision support and recommending treatments (Ibrahim, Liu & Denniston, 2021).

Precision medicine is set to be transformative to treatment. Precision medicine is the prediction and usage of treatments on the specific disease of a specific patient, based on data and processes such as genome sequencing (PwC). The challenges of precision medicine include the large amount of medical data, the cost of drug development and the lack of trained specialists (NHS England, 2020). It is here where AI can make a difference. AI’s ability to process large amounts of data allows it to assist in precision medicine.


“AI is helping [the] industry to accelerate drug development, cut costs and gain faster approvals while reducing errors” (NHS England, 2020). AI can process large amounts of data and identify patterns fast and accurately making it incredibly useful in medical research. It has been used in drug discovery, designed and created data mines in clinical trials, and used in experimentation (Jain, 2022).

Future of AI in Healthcare

The use of AI in healthcare is growing. While currently primarily in high-income countries, AI has the ability to make a significant positive impact on healthcare in resource-poor areas and improve global health (Wahl et al., 2018).

AI and Doctors

The way AI is used in healthcare is of course an important consideration. While AI is used to make predictions about patients and analyse data and imaging, decisions about patient care remain with their doctor. While there is the consideration of the desire for human interaction and compassion in healthcare (Kellogg Insight, 2023), this is not the only reason to not turn to a completely AI focused healthcare system.

A doctor has a greater ability to understand and question their patient. The correct course of treatment may not always be the one that is technically most effective. There are considerations other than the data that a doctor is able to take into account as they take a holistic view of patient care. These include the patient’s ability to take medications as prescribed or the goals of the patient (which may not be to extend life as long as possible) and the social and emotional position of the patient.

With an ageing and growing population, the demands on healthcare are increasing and there are existing areas where healthcare needs are not met. AI can be used, not to replace healthcare workers, but to work alongside them to meet the need for healthcare (Imperial College London) and enhance patient experience.


There are challenges which the use of AI in the medical field present. There is concern around biases that could be perpetuated by AI, including ageism, based on current prejudices and flawed assumptions (WHO, 2022). It is therefore important that such potential for biases are thoroughly investigated and eradicated in the design of AI technologies.

There is also the issue of generative AI. Generative AI is AI technologies that can produce content such as text, images and audio with the aim that they are indistinguishable from content produced by humans (BCG). Generative AI poses ethical issues in healthcare. Medical education and journals could be impacted by the submission of work created by AI, impacting the credibility of the institutions (Zohny, McMillan & King, 2023). There is the risk that generative AI is used to publish large numbers of journal articles, drawing from existing studies, with ethical analyses that are useless or misleading, damaging trust of research publications (Zohny, McMillan & King, 2023).

The effect on patient trust in their clinician is also a risk presented by AI. Hatherley (2020) argues that while the European Union may be pursuing ‘Trustworthy AI’ in their High Level Expert Group on AI, this is an impossible task based on conceptual misunderstanding. Instead of trustworthiness, for which agency of the actor is required, AI should instead be reliable (Hatherley, 2020). Patient trust is reserved for human clinicians, though they can be supported by AI technology.


To conclude, AI has the potential to revolutionise healthcare. A revolution that is required by a growing and ageing global population.

If you want to read more about AI, then check out some of our recent blogs: How Can ChatGPT Help Custom Software Development?, AI in Business: What AI Can Do in 2023 and Key trends in Artificial Intelligence.

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