The coronavirus pandemic has become a catalyst for change in many industries but no more so than in healthcare.
In a matter of weeks, we saw routine consultations cancelled or moved almost exclusively online.
The approach to digital software and equipment in healthcare provision is undergoing a significant shift, and the increasing use of intelligent computer systems has enhanced the flow and collection of patient data.
While it has been widely discussed for several years, Artificial Intelligence (AI) has moved from being an abstract concept of science fiction to an applicable tool in mainstream healthcare services.
However, it is not just one piece of software: it is an umbrella term that encompasses many technologies.
Some AI is already embedded in the health and care sector, but more needs to be done to realize its full potential and make a significant difference through infinite possibilities.
In this article, we answer the questions: what is AI? What is the immediate relevance to healthcare?
We also explore the benefits and the possible implications of AI in healthcare.
What is AI?
AI is a collection of technologies that enables software to replicate human intelligence functions, which means computers can process information similarly to the neuronal network of the human brain.
This includes specific functions, for example, converting speech to text or visually recognizing and organizing objects or images.
Many of the standard AI functions have relevance within the healthcare environment and can support a variety of pathways.
AI has even been recognized right at the top.
The NHS launched The NHS AI Lab in 2019, which aims to support and develop AI in healthcare: a huge step that highlights AI’s ability to transform healthcare and represents the start of a health tech revolution.
How is AI applied to support healthcare?
Like the human brain, AI can analyze large amounts of complex information, which has created numerous opportunities to enhance healthcare pathways.
AI is transforming and personalizing healthcare and removing the burden of administrative tasks, allowing clinicians to spend more valuable time with patients.
Machine learning is one of the most used forms of AI.
Here, technology can teach itself to change in response to new information.
Its use is broad and varied, but one application is mapping clusters of multiple long-term conditions.
This application is utilized in clinical research to help determine whether a specific type of patient is likely to acquire a particular condition and helps inform future care planning.
AI analyzes and interprets sizable clinical data sets collected from electronic health records (EHR) providing valuable insights into disease trajectories.
EHRs help combine all rich patient data into one place, including the patient status, the medical care received, and the disease progression.
You can read more about EHR here:
A more complex version of this form of AI is deep learning. A typical application of this is in the field of radiology.
AI can detect abnormalities in medical imaging beyond what can be perceived by the human eye.
The AI software, which fits seamlessly with existing radiology systems, can analyze images from thousands of X-Rays, CT scans, MRIs, and other medical imaging equipment, categorizing them into abnormal and normal with immense speed and accuracy to reduce the rates of a misdiagnosis.
AI can be used to identify pulmonary diseases, cancers, strokes, skeletal abnormalities, and traumatic brain injuries and shows tremendous potential for wide-scale screening purposes.
The adoption of AI into radiology brings considerable benefits to the patients and the care provider.
It can support the skilled radiology workforce to identify urgent cases to provide optimum care for the patient.
The time to treatment is reduced, improving the management of the patient pathway, and the rapid diagnosis may improve outcomes for the patient.
The improved movement through the radiology department also leads to a reduction of costs associated with outsourcing and administration.
AI can help with patient adherence to health and treatment plans and improving AI technology capabilities may lead to more personalized care.
Messaging alerts with targeted content that promote health and wellbeing responding to actual-world data provided by EHR, smartwatches and phones, and other software is a promising field.
What are the implications of AI in healthcare?
It is important to remember that AI is best served as a support mechanism for clinicians, rather than as a replacement.
Radiologists, for example, do much more than simply interpret medical images; the AI only enhances their ability to do so.
AI, for instance, cannot discuss outcomes with patients or vital information such as procedures and results.
There will always be a need for an empathic clinician, and we may even see new roles created as AI becomes more mainstream,
Ethical implications of AI use in healthcare are also a concern due to permission and data protection, transparency and accountability.
That is why healthcare organizations must work closely with specialist health tech companies to ensure safe and secure data transmission and establish and monitor a framework to limit implications.
Advances in AI technology allow us to reconsider how effective healthcare should look.
AI will not replace clinicians, and the benefit of the doctor-patient relationship should always be maintained, but AI will continue to advance and develop.
One of the greatest challenges may be encouraging the continued adoption into the day to day running of healthcare.
Health and care organizations can overcome this through effective partnerships with AI tech providers to enhance healthcare services to provide optimum care for the patients and the public.