Healthcare has witnessed unprecedented growth in the last decade. With the emergence of big data, akin to most industries, healthcare has adopted data analytics and processing to counter healthcare challenges and patient health issues. This has significantly bolstered the HIT Infrastructure and invited many innovations within its realm. However, unstructured data doesn’t always produce tangible outcomes and cannot be easily quantified. Therefore, extracting meaning out of this data chunk has to be espoused by other technologies that can boost the backend process, as well. While such questions surround the healthcare ecosystem, artificial intelligence (AI), machine learning, and natural language processing (NPL) are the go-to technologies for healthcare organizations that offer the right mix of quality and ROI.
Artificial intelligence, in the recent years, has emerged as a perfect enabler of the healthcare industry. In fact, as per few surveys, AI can attribute to a total saving of $150 billion in the U.S. healthcare market by 2026. Powered by a plethora of innovative products such as virtual nursing assistant, fraud detection systems, robot-assisted surgery, and others, AI not only can reduce costs but also enhance productivity. Next in line is machine learning that is currently being used in the quantitative analysis of three-dimensional radiological images. This empowers clinicians with comprehensive data of radiological images, identifying anomalies that escape human eye. Natural language processing also works along the same lines and processes a vast number of natural languages in order to deliver effective and precise results.
The solutions mentioned above are still operating on a surface level and haven’t penetrated more in-depth into the ecosystem. However, if time and effort are invested meticulously in these projects, the day isn’t far when self-operating healthcare will be a reality.
