Here's How can AI and Machine Learning can Evolve Healthcare Sector AI can be a trusted doctor if we equip it to constantly learn by reliving and absorbing doctors' repetitive tasks

Smart health has become the norm of the day. If you have a headache – you can connect with a specialist, get diagnosed, have a prescription written, and order medicine online to your home. There is a plethora of apps to choose from, each aimed at solving your real-time, situation-driven problems with a promise of leading a happy and healthy life. What if we can take it a notch higher and know more how your body is changing and what you might be prone too? What precautions should you take to dodge a possible health attack?

Today, the healthcare world is awash with technological innovations driven by software and mobility. The innovations currently are focused on reducing the cascading gaps of affordability and accessibility. This has certainly increased the reach of healthcare to every nook and corner, by streamlining doctor networks, pharmacies and diagnostics at a click of a button. While this smart connectivity has helped solve day-to-day worries, the ability to understand and implement the advancements, especially in artificial intelligence and machine learning, has been rudimentary.

AI-assisted robotic surgeries, virtual nursing assistants, digital consultations, image analysis, medication management, healthcare system analysis. The AI and ML medical terminologies used are grandiloquent and couched in the jargonized doctorial fashion, which is quite intimidating. Often overused, while communicating no meaning, AI and ML are the buzzwords used across segments to sell products. In healthcare, what does this really mean?

What is ML?

ML is a subset of AI. It's a set of algorithms that improve automatically through experience. What ML essentially does is that it studies large data and examines and analyses for common patterns. For example, if you are prone to gastritis and start recording your daily food intake; over time, the ML model will look at each of the food types you are consuming, in the diverse food groups, and find common patterns to make indications on the types of food that is causing gastritis.

ML can be used to merge an individual's data with other data sources to predict the probability of developing a disease, which can be addressed through timely actions. This innovation has proved to be beneficial in the diagnosis procedure, monitoring chronic conditions, assisting in various surgeries, drug discovery, etc.

What is AI?

AI is the science to make computers behave like the way humans do. Natural language processing, intelligent agents, computer vision, chatbots, and voice recognition are all symbolized by AI. This is still vague and the developments in the space are being studied to understand it's true potential.

In the healthcare sector, AI range of technologies enables machines to sense, act, comprehend and learn the administrative and healthcare functions that can be further implemented in training and research purposes.

The AI and ML implication

AI needs to learn every day to remain intelligent and sustainable. It is based on deep learning, a machine-learning algorithm used for a range of applications including speech and image recognition. This teaches machines to interpret complex patterns and structures that are observed in real-life data. AI models built from these groups helps in identifying affected genes and predict a probability of developing a certain disease for any individual. AI tools can also be used by healthcare providers to measure and analyse pathology or radiology results quickly and accurately. Thus, freeing up their time to attend to many patients.

The human element

Ultimately, it's we humans who train the machines to work and identify differences to asses common conditions more accurately like human doctors. Not only this, but AI will also help in maintaining data privacy. By gathering individual patient data, the system can learn each person's habits and preferences, and coordinate accordingly. With this, doctors and nurses will be relieved of these coordination tasks, allowing them to focus on the aspects of their work that require a human touch. This, in turn, means more time interacting with patients and staff, faster and more efficient delivery of equipment throughout the facility, and ensuring a clean, secure and healthy environment.

Conclusion

In today's world, we hardly get time to keep a backup of data. The arrival of such technologies will stir a new modus operandi and lure us into a habit of maintaining, recording and tracking daily health and routine. This will set the idea of receiving treatment from a "robot". Can AI really understand us as individuals, the way our family doctor does? Yes, it can! Care providers need to recognize and protect the importance of the human experience while embracing the potential of AI.

Hospitals should work together to understand the balance between staff and technology by effectively applying a suite of AI solutions. AI can be a trusted doctor if we equip it to constantly learn by reliving and absorbing doctors' repetitive tasks. It is up to us to derive the efficiency and effectiveness of the automated healthcare system we intend to build. And allowing doctors the time to develop targeted treatment and therapies to solve the most critical cases.