The applications of artificial intelligence and machine learning in the healthcare sector are limitless. The ones we’ve discussed in the article are those which are very popular nowadays.
During this pandemic, it became critical for people to self-assess whether they are infected with the virus or not. People started taking appointments with general practitioners over a video or voice call. It helped the healthcare professionals to screen the patients from remote locations. Telemedicine apps like Mdlive made the fight against Covid-19 possible by allowing practitioners to schedule and conduct video conferencing appointments via mobile devices. The purpose was to limit the footfall at hospitals or clinics and reduce the person-to-person interaction.
However, it was nearly impossible for healthcare professionals worldwide to consult every patient personally. To address this issue, apps like Livi made use of AI-powered cognitive healthcare agents, which can help patients in risk-assessment over a web chat or voice call. These AI-powered solutions are capable of analysing symptoms and other risk factors based on the guidelines of WHO or CDC (Disease Control and Prevention). This has helped millions of people to the virus without putting additional load on the healthcare infrastructure.
Top 4 applications of Machine Learning and Artificial Intelligence in HealthcareIt has been seen that AI and ML technologies have enormous possibilities in the healthcare sector. Here I’m breaking down the applications of AI and ML, which are being used to revamp the healthcare infrastructure.
Automated ResearchWhen it comes to disease research and treatments, it is not possible to overstate how broad the world of Transforming Healthcare is. For a century, universities and labs around the world have been researching the cure and prevention for existing as well as potential diseases. Connecting and synchronising all of the research can potentially result in making the research faster and more meaningful.
As the time passes, more and more research is being published and it becomes tough for the medical practitioners and pharma companies to stay tuned with them. Systematic reviews are conducted by bringing data together from several different studies. But, these reviews are painstakingly labour intensive and often take years to compile the data and summarise.
In December 2018, Cochrane community, British international charitable organisation for medical research findings, in collaboration with Microsoft, conducted a project named ‘Project Transform’. The project was aimed at conducting systematic reviews using AI technology to boost the process of systematic reviews.
The medical researches can also leverage the potential of Machine Learning technology to analyse trail reports by automating the literature search using ‘text mining’. The AI technology can help in identifying, categorising, and inspecting thousands of randomised trials to find the appropriate ones for systematic reviews. By using the AI and ML technology, the Project Transform by Cochrane community realised 60-80% reduction in their research efforts.
One of the biggest breakthroughs of AI in drug research and development occurred in 2007 when the medical researchers used a robotic software named ‘Adam’, which had researching functions of yeast, to analyse billions of data sets in public domain in order to hypothesize about the function of 19 genes in the yeast. The software algorithm predicted 9 new hypotheses which were accurate.
Examples of using AI for medical research and development:
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