The Future Impact of Artificial Intelligence in Medical Practice
By: Nigel Whittle
Head of Medical & Healthcare
5th December 2018
We are all aware of the challenges facing healthcare in general, and the NHS in particular. Shortage of funding, increased demands for services, rising costs of innovative drug treatments, the needs of an ageing population. All these issues limit the efficacy of our healthcare service.
There is only so much that can be done with improved efficiency. But what if we could improve diagnosis, so that diseases are detected and treated earlier? In almost every disease, early diagnosis would allow cheaper and more effective treatment, with improved patient outcomes. But of course accuracy is important too, so that scarce resources can be targeted to the right patients.
And that is what makes clinical diagnosis so difficult, requiring skilled and knowledgeable practitioners, whether the local GP or the Harley Street physician. These skills, developed by years of medical training, allow effective diagnosis of disease based not just on clinical information, but also on the patients past history, social background, age and ethnicity.
But at the end of the day, the clinician is simply processing data. And this is the primary strength of Artificial Intelligence.
So which areas are likely to be most impacted by AI?
UK hospitals generate a staggering 50 petabytes of data every year, of which the vast majority comes from medical imaging. But more than 97% of that data is unused or unanalysed, perhaps because it is unusable, or redundant, or simply swamping the capacities of the clinicians. But AI-powered medical imaging systems can now reliably produce scans that help radiologists identify subtle patterns, helping them treat patients with emergent conditions more quickly. Will this lead to the disappearance of radiology as a clinical profession? Perhaps a more likely outcome is that radiologists will be able to allocate their time more effectively, to work closely with patients with the most serious or complex conditions.
Similarly, cancer diagnosis can be made more accurate through the use of AI systems running scans linked to complex recognition algorithms. When cancer is detected early, treatment is more likely to be successful. But too often, cancers are diagnosed at a late stage when they’re much harder to treat. But AI systems are beginning to take on some of the workload: for example, an algorithm has been developed to diagnose skin cancer more accurately than dermatologists (95% compared with 87%). But in doing so, we must remember that AI systems are not infallible, and the relationship between the patient and the doctor is important so that false negatives are not dismissed out of hand.
In another example, researchers at Imperial College London are working with DeepMind Health to develop AI-based techniques to improve the accuracy of breast cancer screening, using a database of 7,500 anonymised mammograms to develop screening algorithms that can spot early signs of breast cancer whilst reducing over-diagnosis.
But perhaps more interestingly, could there be ways to detect hidden clues in people’s lives that point to cancer? As we generate, collect and share more data than ever before, some of which may be relevant to our health, is there a way to gather this information and help detect diseases such as cancer earlier? And even if it is possible, is it something that we would allow big data systems and corporations to do?
Currently, there’s no easy way to diagnose Alzheimer’s Disease: no single test exists, and brain scans alone can’t determine whether someone has the disease. But alterations in the brain can cause subtle changes in behaviour and sleep patterns years before people start experiencing confusion and memory loss. Artificial intelligence could recognize these changes early and identify patients at risk of developing the most severe forms of the disease, allowing clinicians to target drug and behavioural therapies most effectively.
The role of the doctor
It is clear that managing patient data is a core component of the healthcare delivery process, and AI systems will increasingly play an important role in this process. AI is capable of processing larger amounts of data and at a faster rate than human clinicians, is capable of achieving a higher level of accuracy and is not subject to fatigue or burnout.
Which naturally raises a question, what will be the future role of the doctor?
No matter is strengths, AI lacks human sensitivity; clinical applications still require human expertise in the interpretation of data and recommendations. As the role of the physician evolves in the era of AI, the humanity of healthcare delivery will remain critical, and rituals (‘the bedside manner’) that may have been lost in the rush for efficiency savings may emerge with a new-found focus on the patient at the centre of treatment.