The heart and soul, thought processes and decision-making of The Doctor were a culmination of his Artificial Intelligence programming, which, when at the time the show was created, was still somewhat considered as science fiction.
However, today, AI is a technology which is increasingly present in many areas of our day-to-day lives. Our homes are becoming more and more “intelligent”, and companies, regardless of their size, are being completely transformed thanks to AI.
And while the possibility of an EMH is still a long way off, the fundamental workings of AI in healthcare are already clearly evident in several present-day scenarios.
Many of these use cases have sought to implement ways in which AI could be used to solve the varied challenges faced by clinicians as well as how Big Data could be integrated into helping them make better decisions. This is even more relevant now in the face of the current pandemic.
Take, for example, hospital intensive care admissions due to Covid-19. Thanks to data prediction modelling, clinicians now know that a percentage of all coronavirus related admissions will eventually end up in ICU. This allows them to know exactly when to prepare for spikes in admissions so that their units do not suddenly become overloaded. Equally, when decisions to both implement and reduce quarantine periods are made by imposing governments, these are also based on data.
Indeed, the very idea of lockdowns and quarantines present new challenges to healthcare in themselves. Where patients are unable to access first-line medical services in person because they’re told to stay at home or where clinicians need to prioritise patients due to the immediate needs of more critical cases, having the right resources and being able to make the right decisions are ever balanced against time and necessity.
DeepHealth is a European funded initiative which addresses these needs and helps clinicians in their daily work. It uses high-performance computing to improve biomedical applications and applies forefront features based on deep learning, artificial intelligence and computer vision to deliver new efficient tools for diagnosis, monitoring and disease treatment across 3 health areas: neurological diseases, tumour detection and early-stage cancer prediction, as well as digital pathology and automated image labelling.
Additionally, designing care plans appropriate to a patient’s particular needs and unique lifestyle also presents new obstacles.
ADLIFE is one example of a solution to that problem. Designed to improve the quality of life of patients living with multiple advanced long-term conditions such as Chronic Obstructive Pulmonary Disease (COPD) and/or Heart Failure, ADLIFE guides patients through intelligent, digital, integrated, and personalized care.
Through a combination of innovative digital implementations, the solution is focused on the early detection of care needs by being able to dynamically tailor personalized care provision. This enables care teams, patients, and clinicians to improve and better maintain health in patients with these diseases as well as enhancing patients' and clinicians' autonomy by encouraging them to participate in informed decision-making by providing them with personalised care plans.
The physical needs of patients often overshadow the well-being of another important need – the care of our mental health. This is where another new AI-based development comes in. Smart Remote Treatment is a personalised solution which aims to improve treatments in patients diagnosed with bipolar disorder. Based on the patients’ lifestyle (physical activity, sleep quality, etc.) and biological features (gender, weight, etc.) SRT guides clinicians into a personalized prescription plan to balance the patient’s Lithium intake thus preventing the likelihood of remission as well as increased risk of relapse, recurrence or even hospitalization.
This is achieved through connected patient treatment into a real-time solution by wearing an activity-monitoring wristband and using a skin patch for both Lithium-in-blood monitoring and drug administration which is connected to a graphical interface where clinicians can show patients how their lifestyle affects their treatment and follow up with a patient-empowerment strategy where individual decisions about improvements to the treatment can be made.
While the aforementioned projects are still in development, the value and impact that AI brings to healthcare are clear. And while we are only just starting to scratch the surface of its possibilities and benefits as an invaluable medical tool, we are already on our way to having our very own data-based doctor.