Can you think of visiting your doctor with an ache or some problem and she begins to feed all your symptoms into her system soon after listening to you? Once she does this, in no time you get to see a sheet on the latest research generated by her system, which she might need in order to identify your problem and cure you.
Can you imagine a computer being able to help the radiologist detect problems found in a MRI or X-ray that are too minute for a human to notice? How would you feel if you see a computer that looks at your medical history and is able to compare it with a recent research and suggesting a method of treatment specifically tailored to your needs?
I am sure we all are aware of the fact that robots can never replace doctors entirely. It has been observed recently that Machine Learning (ML) and Artificial Intelligence (AI) are transforming the healthcare industry massively, resulting in improving outcomes and changing the way doctors think about providing health care.
According to Forbes, analysts at the International Data Corporation (IDC) predict that 30 per cent of providers will use cognitive analytics with patient data by the end of the year 2018. CBI Insights has identified that about 22 enterprises have initiated development of new programs for imaging and diagnostics.
It has turned out to be a promising field where machine learning can be introduced because all the algorithms related to deep learning are getting proficient at recognizing patterns — which is much of what diagnostics is about. Another survey by analysts at Frost & Sullivan predicted that ‘by 2025, Artificial Intelligence enabled systems would be a part of everything from population health management to answering of specific patient queries by digital avatars’.
The data generated by the healthcare sector can broadly be classified into structured and unstructured data, both of which require different artificial intelligence approaches. In the healthcare industry, machine learning techniques are useful for analysis of imaging and genetic data. Similarly, Natural Language Processing methods can be used to extract information from clinical notes, which can then be analyzed by machine learning techniques.
The major forces that help bring artificial intelligence to the forefront of healthcare and life sciences is the quick commercialization of machine learning and big data analytics. Both these forces are set to modify how the healthcare vertical detects and cures diseases. A diagnostic system that uses deep learning methodology and one that is powered by AI would assist physicians identify diseases and simultaneously improve the speed and precision of diagnosis.
Huge advancements were seen in the last decade in the amount of data that was generated and collected on daily basis about everything we do. Advancements were also noticed in the human ability to use technology to analyze and understand it. Big Data is the intersection of these trends and it is also helping companies in all sectors to become more proficient and help generate better results. Healthcare sector isn’t very different, big data can be used to forecast epidemics, find cure to diseases and improve life quality.
With world’s population increasing at a rapid rate, there are major changes in the treatment delivery methods, and a lot of decisions behind these changes are being driven with the help of analysis of the data generated. Big data analytics helps doctors to understand a lot about a patient’s health, as early in the patient’s life as possible – this includes picking up warning signs of the patient’s illness at an early stage that makes the treatment simpler than if it had been spotted later.
