While artificial intelligence has been playing a major role in industries for decades, it is only just beginning to take center stage in healthcare. With innumerable issues to overcome, like the ever-increasing aging population and the growing rates of chronic disease, the requirement of advanced, cutting-edge technologies is only going to increase.
There are numerous ways in which AI is transforming the healthcare industry and listed below are just a few examples:
Radiology – Automate image analysis and diagnosis tools that can highlight areas of interest on a scan to a radiologist to increase efficiency and reduce human error are now being used. Additionally, fully automated solutions that can instantly and automatically analyse a scan without human oversight in clinics that are short of man-power are also being developed. There have also been recent demonstrations of improved tumour detection on MRIs and CTs which can go a long way in improving cancer prevention efforts.
Drug Discovery – AI is being leveraged to collect information from the vast databases of information on already existing medicines and identify new potential therapies which can then be redesigned to target critical threats. This greatly improves the speed, efficiency and success rate of the drug development process and also lowers the associated costs.
Patient Risk Identification – AI can help clinicians identify at-risk patients by analysing tremendous amounts of patient-related data and provide real-time support to clinicians. Currently AI tools have been helping assess readmission risks and detecting patients that have an increased likelihood of returning to the hospital within 30 days of being discharged. Interestingly enough, recent work has shown that AI has the ability to predict risk of cardiovascular disease by analysing still images of a patient’s retina.
Primary Care/Triage – Many organizations are working on direct to patient solutions that can triage and give advice via a voice or chat-based interaction. This provides fast and scalable access for basic questions and medical issues and will help in avoiding unnecessary trips to the GP and reduce the increasing demand on primary healthcare providers. Also, for a subset of conditions, it offers
basic guidance that wouldn’t otherwise be available for populations who reside in hard-to-reach areas. While the concept is straightforward enough, these solutions still require considerable independent validation to prove patient safety and efficacy.
Over the next couple of years, the world will see a rise in hybrid models, where healthcare providers are supported by AI in diagnosis, treatment, and identifying risk factors, but still retain ultimate responsibility for the patient’s care. This set-up will lead to the faster adoption of AI by healthcare providers and the increased acceptance by patients, resulting in measurable improvements in patient outcomes and operational efficiency.