In this blog post
The Healthcare Industry is going through a quiet revolution. Factors like disease trends, doctor demographics, regulatory policies, environment, technology etc., are forcing the industry to turn to emerging technologies like AI, to help adapt to the pace of change. Here, we take a look at some key use cases of AI in Healthcare.
The application of Machine Learning (ML) in Medical Imaging is showing highly encouraging results. ML is a subset of AI, where algorithms and models are used to help machines imitate the cognitive functions of the human brain and to also self-learn from their experiences.
AI can be gainfully used in the different stages of medical imaging- in acquisition, image reconstruction, processing, interpretation, storage, data mining & beyond. The performance of ML computational models improves tremendously as they get exposed to more & more data and this foundation on colossal amounts of data enables them to gradually better humans at interpretation. They begin to detect anomalies not perceptible to the human eye & not discernible to the human brain!
What goes hand-in-hand with data, is noise. Noise creates artifacts in images and reduces its quality, leading to inaccurate diagnosis. AI systems work through the clutter and aid noise-reduction leading to better precision in diagnosis, prognosis, staging, segmentation and treatment.
At the forefront of this use case is Radio genomics- correlating cancer imaging features and gene expression. Needless to say, this will play a pivotal role in cancer research.
Drug Discovery is an arduous process that takes several years from the start of research to obtaining approval to market. Research involves laboring through copious amounts of medical literature to identify the dynamics between genes, molecular targets, pathways, candidate compounds. Sifting through all of this complex data to arrive at conclusions is an enormous challenge. When this voluminous data is fed to the ML computational models, relationships are reliably established. AI-powered by domain knowledge is slashing downtime & cost involved in new drug development.
Cybersecurity in Healthcare
Data security is of paramount importance to Healthcare providers who need to ensure confidentiality, integrity, and availability of patient data. With cyberattacks increasing in number and complexity, these formidable threats are giving security teams sleepless nights! The main strength of AI is its ability to curate massive quantities of data- here threat intelligence, nullify the noise, provide instant insights & self-learn in the process. The predictive & Prescriptive capabilities of these computational models drastically reduces response time.
Virtual Health assistants
Virtual Health assistants like Chatbots, give patients 24/7 access to critical information, in addition to offering services like scheduling health check-ups or setting up appointments. AI-based platforms for wearable health devices and health apps come armed with loads of features to monitor health signs, daily activities, diet, sleep patterns etc. and provide alerts for immediate action or suggest personalized plans to enable healthy lifestyles.
AI for Healthcare IT Infrastructure
Healthcare IT Infrastructure running critical applications that enable patient care, is the heart of a Healthcare provider. With dynamically changing IT landscapes that are distributed, hybrid & on-demand, IT Operations teams are finding it hard to keep up. Artificial Intelligence for IT Ops (AIOps) is poised to fundamentally transform the Healthcare Industry. It is powering Healthcare Providers across the globe, who are adopting it to Automate, Predict, Remediate & Prevent Incidents in their IT Infrastructure. GAVS’ Zero Incident FrameworkTM (ZIF) – an AIOps Platform, is a pure-play AI platform based on unsupervised Machine Learning and comes with the full suite of tools an IT Infrastructure team would need. Please watch this video to learn more.