What if we told you that the next time you took a selfie, your smartphone would predict that you are likely to develop acne in the next couple of days? Sounds intriguing, right? Well, that’s where we’re all collectively heading to.The tech world is full of ambitions. Through our ideas, innovations, and goals, we are moving ahead as a society. This is especially true with respect to the evolution of healthcare AI, where some of the most plaguing concerns are being tackled and fixed with the help of technology.Today, we are on the brink of rolling out machine learning models that can accurately predict the onset of hereditary diseases and the time a tumor would turn cancerous. We are working on prototypes for robot surgeons and VR-enabled training centers for doctors. Even at the operational levels, we have optimized bed and patient management, remote care, dispensing of medications, and more and automated tons of redundant tasks through AI-powered systems.As we continue to keep dreaming of better ways to deliver healthcare, let’s explore and understand some of the key aspects in the evolution of healthcare and how technology, especially data science and its wings, is helping in this phenomenal growth.This post is dedicated to bringing out the significance of data in the development of healthcare systems and modules, some prominent use cases, and the challenges stemming from the process.The importance of Data in Healthcare AINow, before we begin to understand some of the more complex use cases and implementations of AI, let’s realize that the average healthcare and fitness apps you have on your phone are powered by AI modules. They have undergone years of training to accurately analyze, prescribe and infer your data and visualize it into insights.
It could be your mHealth app that lets you virtually get consultations from a physician or book an appointment with them or an app that retrieves results on probable health concerns based on your symptoms and well-being, AI is embedded in every healthcare application today.Scale this requirement further and you will have advanced systems that require data from multiple sources such as computer vision, electronic health records, and more to perform complex tasks. Remember the breakthroughs in oncology we mentioned earlier, such solutions require massive volumes of contextual data to produce accurate results. For this, annotators and experts have to source data from scans and reports such as X-Rays, MRIs, CT scans, and more and annotate every single element they see on them.Healthcare professionals have to work on identifying different concerns and cases and label them so machines could understand them better and process more accurate results. So, all results, diagnoses, and treatment plans stem from data and the precise processing of it.With data being at the heart of healthcare, let’s acknowledge that data is paving the way for a healthier tomorrow.