Artificial Intelligence In Medical Diagnosis: How Is AI Supporting Diagnostics?

Artificial intelligence (AI) promises to be a major trend throughout healthcare, and one of the most significant ways it will impact the medical industry is in the field of diagnostics.

Whether you’re a patient or a medical practitioner, you can increasingly expect to see AI being used in a number of ways. From generative AI language models (also known as chatbots) appearing during triage, to general practitioners using AI to diagnose, or even crunching patient data to look for early signs and symptoms, it’s likely that AI will play a big role in advancing the field of medical diagnostics this year.

In this article, we’ll look in more depth at some of the ways AI is expected to impact the field of medical diagnostics, and some of the pros and cons of this step change.

How will AI change medical diagnostics?

The introduction of AI to medical diagnostics has begun to revolutionise the healthcare sector.

With advanced machine learning algorithms being rapidly integrated into healthcare systems worldwide, medical practitioners will soon be able to process vast quantities of patient data to look for trends that will not only improve the accuracy of their diagnoses, but help them to spot wider – and perhaps previously undiscovered – connections, leading to earlier diagnoses.

The end result will be improved outcomes for patients, a reduced burden on healthcare workers and perhaps even the discovery of new diagnostic methods.

Here are a few ways we can expect to see AI becoming integrated with the diagnostic process:

1/ Generative AI and self-diagnosis

Self-diagnosis is where individuals attempt to diagnose their own illness based on their symptoms, usually by consulting an online resource. Search engines and social media have historically played a huge role in self-diagnosis, and as many as one-third of people in the United States have used the internet to diagnose their own illnesses.

Self-diagnosis can have benefits to the healthcare sector – if patients are able to accurately diagnose their own symptoms, it can help to reduce the burden on general practitioners and lead to faster, better outcomes.

However, one of the major disadvantages of using the internet for self-diagnosis is that patients are often prone to mis-diagnosing their illness, either by misunderstanding the connection between a symptom and the disease it’s linked to, over-emphasising the role of one symptom, or missing a symptom entirely.

Confirmation bias also plays a large role: if a patient is convinced they have a specific illness, they may be inclined to omit or fabricate symptoms in order to match the diagnostic criteria. As a result, around 34% of all self-diagnoses are mis-diagnosed, which can create complications further down the line.

This is where Artificial intelligence steps in. New generative AI chatbots will have access to a huge collection of medical literature, as well as the ability to build comprehensive understandings of symptoms and crunch data extremely fast to come up with possible diagnoses. This will allow patients to describe their symptoms and receive instant feedback, helping them self-diagnose with more accurate results.

2/ Processing big data for predictive analytics

The healthcare sector already accounts for over one third (33%) of all the data in the world. This data is growing at an exponential rate, and faster than in any other sector. Indeed, any single hospital in the USA produces around 137 terabytes of new data per day. With the pool already so vast that it would be frankly impossible for human knowledge workers to crunch into meaningful insights.

Thankfully, AI allows for the automated handling of healthcare data, both in terms of processing, and reporting. Through supervised learning and the creation of deep neural networks, healthcare practitioners are already training AI to recognise and respond to healthcare data to improve diagnostics, by analysing huge tranches of data, searching for trends within those data sets, comparing data against other population-wide and historical data sets, and cross-referencing results against decades’ worth of medical literature. Where these processes might have taken a human weeks or even months to achieve, AI can produce results in minutes.

AI is already being employed across a number of diagnostic methods, and not only in the form of processing words and numbers, but even in the field of medical imaging research (including X-rays, CT scans and MRIs). For instance, by analysing the build-up of plaque in a patient’s arteries across sets of computed tomography angiography (CTA) images, researchers at Cedars Sinai have created an AI model that is able to detect patients at risk of heart attacks.

Elsewhere, researchers are experimenting with AI in big data analysis to create diagnostic models for conditions such as breast cancer, dementia, diabetes and kidney disease. The desired outcome is that these AI models will one day be able to automatically detect patients’ risks of various illnesses and start treating them before those conditions become a medical emergency. As well as potential cost savings, these preventative treatments could help to save millions of lives per year.

3/ Remote patient monitoring

Another way AI is impacting the diagnostic process is through remote patient monitoring. At the moment, triage is largely dependent on patients presenting before a healthcare professional while displaying symptoms. This can often lead to errors – for instance, if the symptoms present at the time are not consistent with the diagnosis, if the patient is asymptomatic, if the severity of symptoms is misinterpreted leading to a more or less urgent response than required, or if a diagnosis is missed altogether.

These errors and misdiagnoses can, in turn, lead to wasted time, effort and money. Misdiagnoses are believed to cost the US healthcare industry around US$100 billion per year.

One part of the solution may lie in AI-powered remote patient monitoring, allowing patients to be monitored over time in order to keep track of changes in their health. Remote patient monitoring could pave the way towards more accurate diagnoses by tracking the development, changes and severity of symptoms over a sustained period of time using a variety of AI-augmented tools, including wearable devices, sensors and patient-reported information.

Not only could this system be used to catch symptoms that may otherwise be missed, it offers the potential for doctors to spot symptoms earlier, leading to faster diagnoses and potentially better patient outcomes. Better still, in the search for one diagnosis, medical professionals may be able to spot other diagnoses, saving the patient from having to attend triage multiple times.

4/ New diagnostic research

Artificial intelligence can now enable healthcare practitioners to identify new diagnostic models. This could apply both to never-before-identified illnesses or variations of existing illnesses, and to new diagnostic frameworks for well-known illnesses.

AI’s ability to process huge segments of data will allow for medical experts to spot new patterns and trends developing across a population. This could lead to many interesting benefits. For instance, for virulent diseases, AI will be able to track the spread of these diseases and allow experts to identify how the illness moves from person to person, how quickly it can spread, time to incubation and appearance of first symptoms, and so on.

This methodology was used to good effect during the recent COVID-19 pandemic. AI helped to model disease clusters, predicting the likely spread of the illness throughout a given population, and thus informed healthcare experts as to what would be the best possible response. This led to development of AI-influenced contract tracing (identifying likely exposures), monitoring and early diagnosis (the ability to work backwards to identify first symptoms) and telemedicine responses (used to inform the likelihood of probable diagnosis without needing to refer individual patients to a healthcare practitioner, thus reducing workload and burden).

What are the benefits of using AI to support diagnostics?

Artificial intelligence will bring new, streamlined ways of working to the practice of medical diagnostics.

As we’ve seen, AI has the potential to:

• Speed up the diagnostic process, easing the pressure on the medical professionals involved in triage
• Allow for earlier diagnosis, both by spotting symptoms that may otherwise fly beneath the radar, and through patient monitoring, which enables illnesses to be identified even before a patient presents at triage
• Improve the accuracy of diagnoses, by comparing symptoms against a huge compendium of medical literature and big data gathered from other sources to raise suggestions that can be confirmed by a professional
• Model trends across a population by analysing huge data tranches and identifying patterns
• Reduce the burden on healthcare workers, leading to cost savings and freeing up experts’ time and resources for more urgent cases

What are the downsides of using AI to support diagnostics?

AI will have a profound impact on the healthcare sector, helping to improve both the efficiency and the quality of medical diagnostics and hopefully producing better outcomes for patients.

However, the rapid development of AI and its integration into the healthcare sector is not without its challenges, some of which include:

Potential for large-scale inaccuracies

Artificial intelligence is a learning model, and much of this learning comes from human-generated data. Indeed, AI itself is programmed by humans. This brings about the risk of inaccuracies, both in the fundamental make-up of AI, and in its ability to process data. AI is also unable to discriminate between good data and bad data, running the risk that even a minor inaccuracy could have massive consequences if AI takes it as fact.

In terms of diagnostics, AI could return large-scale misdiagnoses, prescribe incorrect treatments, or process its own learnings incorrectly. Given the scale that AI works at, the cost of a single bad decision could have far-reaching consequences if left unchecked.

Ethical considerations

As AI becomes ever more integrated into our healthcare system, humanity must reckon with the ethical consequences this may have. For one thing, it is already well-documented that AI exhibits signs of racial and gender bias. But perhaps even more concerning is the fact that artificial intelligence is not capable of human empathy.

This could have a particularly big effect in the field of diagnostics, as while AI may be able to understand a diagnosis from a medical point of view, it will not have the capacity to understand the psychological and emotional impact this diagnosis will have on the patient. We must be careful not to outsource so much of the diagnostic process to AI that we end up discarding one of the most important elements, which is the patient-doctor relationship.

Adapting to global change

Finally, we must remember that the use of AI in medical diagnostics represents a paradigm shift for the global healthcare sector. A lot of work needs to be done to prepare both staff and patients for this step change, through training, public awareness campaigns and complete transparency between medical workers and patients.

The successful integration of AI should not be measured by its ability to save time and costs, but rather by its impact on society, the benefit it creates for individual people, and how widely it can be socially accepted.

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