Tim Wooller, Principal – Industrial Design, Sector Lead Healthcare, PDD, explores how AI can help treat diseases such as cancer and stroke
The use of Artificial Intelligence (AI) in healthcare is driving a revolution, automating administrative tasks, and analysing large patient data sets to reach diagnoses more quickly and accurately than ever before. This frees up more time for healthcare providers to concentrate on life-saving tasks and patient treatment for global diseases like cancer and strokes.
Many on the frontline of the healthcare industry agree that AI can bring major benefits. Statista’s recent global research found that 60% of those in the pharma and healthcare industry stated that AI helps to improve quality control, 44% said it helps with customer care, and 42% said AI technology helps with diagnosing diseases and monitoring patients.
In fact, the healthcare industry is increasingly using AI within its everyday practices, as well as looking for new ways to expand its capabilities. For example, AI has had a strong hand in diagnosing and treating cancer and can be used to improve results in patients. According to the National Cancer Institute, the integration of AI technology in cancer care could improve the accuracy of a diagnosis, easily identify patterns that are often missed by humans and speed up disease detection massively. It can also help the advancement of stroke care, as the National Optimal Stroke Imaging Pathway (NOSIP) considers AI as a vital force for interpreting brain imaging during the initial stages of assessing a stroke.
AI to help cancer detection
AI-based computer programs have been used to help doctors interpret mammograms for over 20 years, but evidence shows that technology in this area is quickly evolving. Scientists are developing advanced AI tools and cancer imaging to aid screening tests for different types of cancer, including breast cancer, to help reduce the heavy workload that comes with older, legacy devices.
AI technologies can also improve drug research by quickly analysing what causes cancer cells to become resistant to anti-cancer drugs. For example, machine learning models have been able to predict the drug sensitivity of patients with ovarian, gastric, and endometrial cancer. AI can also refine and accelerate the optimisation process of combined chemotherapy by using a screening system based on deep learning and algorithms which can predict the tolerance of breast cancer to chemotherapy treatment.
AI in stroke care
Another area where AI is proving incredibly beneficial is in the treatment of neurodegenerative diseases such as strokes and Alzheimer’s. AI can be used to quickly analyse a patient’s brain scan and deliver results to doctors within minutes, with the process of imaging also beneficial as specialists can review significant amounts of data and pick up any signs of a suspected stroke. Data analysis is challenging and very time-consuming, which is why hospitals have invested in AI to help speed up this process.
Additionally, AI can help make the diagnostic process faster, which is essential for stroke interventions where time is of the essence. AI algorithms can inform clinical decisions and interpret imaging to create a diagnosis and deliver treatments for neurodegenerative diseases faster than ever before. For example, Northumbria Healthcare NHS Foundation Trust is using AI to analyse images from stroke patients’ brain scans with great success and also using that technology to treat other neurological and chronic conditions such as epilepsy.
Brainomix, an Oxford-based company who have developed software using AI called e-Stroke is also helping physicians analyse brain scans and make accurate treatment decisions. e-Stroke has been deployed in multiple hospitals in the UK. It generates brain scan results within 1-2 minutes, which are then automatically processed through the AI software, hugely speeding up the diagnostic process.
The future of AI in cancer and stroke care
The rise of AI in healthcare has had a large effect on the research of diseases such as cancer, strokes, and Alzheimer’s, where the chances of treatment success are massively dependent on the speed of diagnosis and treatment.
As with all technological advances, however, wider adoption of AI in healthcare will take some time. There will be a noticeable transition where patients will still favour clinicians and trained doctors over technology for some time – human judgement is often perceived as more trustworthy when it comes to nuanced diagnoses and treatment plans. The biggest challenge of any implementation will not be about the new tech itself but about explaining why it should be trusted and demonstrating the real-world benefits, so it can be adopted at scale.
It’s impossible to dispute that AI advancements are a positive force for the healthcare industry. It will help save lives, reduce workloads and streamline processes. It’s exciting to imagine what the next five or ten years will look like as the sophistication of AI accelerates, and the barriers to adoption are reduced. AI is definitely here to stay, and the earlier it is accepted, the quicker we can begin to see advancements in the healthcare industry.
