The situation in the past two years has emphasized a need to have robust and scalable healthcare systems supported and complemented by technology. We have seen how technology has been harnessed to provide access to health infrastructure and medical resources, connecting citizens of the country to what may be called their digital health identities. Even before the Coronavirus crisis, there was a growing trend of healthcare and patient data becoming available digitally, ready for computation and analysis. When applied to such healthcare data, structured or unstructured data, Artificial intelligence has excellent use. Even though it is foreseen that artificial intelligence will not wholly replace human health professionals, it will undoubtedly play a huge role in assisting them in activities like screening for anomalies and diagnosis. In a significant way, Artificial Intelligence can use sophisticated algorithms to derive inferences from healthcare data. These inferences will help provide real-time medical procedures, navigate complications, etc.

Predictive modeling can help predict outcomes of treatments and help successfully walk through them. Robots are already being used in surgeries, and quite successfully at that. Diagnostic activities make good use of image processing, which helps process scores of diagnostic and medical testing images. Artificial intelligence is also being used to detect cancer at its earliest stages at this stage of diagnosis. Artificial intelligence is also being used in researching and developing new treatments and drugs. While the potential for AI to independently conduct all aspects of the drug development process is still limited, AI significantly aids in individual stages of the drug development process. It speeds up clinical trial processes by creating more efficient methods of subject recruitments. It also optimizes the process of drug selection by quickly eliminating those candidates most likely to fail clinical trials. Some real-world applications of artificial intelligence and machine learning in medicine include IBM Watson Genomics, a significant application of cognitive computing to generate insights from sequencing tumors and thus help develop specific treatments.

Another example that further emphasizes the utility of data-driven approaches using these novel concepts and AI and cognitive computing technologies is Google DeepMind. It is a novel move at creating timely response systems that alert healthcare providers about any anomaly or aberration in the patient's condition so that they may provide immediate support to the patient, in many cases, which might be vital for the survival of the patient. The critical portion one looks at while considering the future of cognitive computing and artificial intelligence in healthcare is the ability of pattern detection, identification, and analysis. Understanding a patient and a human physician does and using data-driven methods to arrive at conclusions beneficial to the patient's well-being again form the basis of the future of cognitive computing and artificial intelligence in healthcare. While there might be doubts about whether such cognitive computing solutions can fully replace human healthcare personnel, they can undoubtedly be utilized to assist, and complement the efforts of such personnel, now and in the future.