Intelligence has been an integral component of every aspect in all most all arenas of life. In the healthcare industry, the degree to which it has been impacted is comparatively low and the progress is in smaller steps when compared to those made in other fields. This can be attributed to several challenges and hurdles faced in healthcare systems. Adding to this, intelligence is not justified in beyond proof of concept studies. Recent years however have embraced hybrid models that involve incorporation of intelligence from AI systems, besides leaving the ultimate responsibility of disease identification/outcomes in the hands of the clinician as a means of critical intervention. Current and future applications in health care have a greater potential to have an impact on patients, clinicians, and the pharmaceutical industry. Growing number of studies have indicated the successful implications of intelligence in areas including patient stratification, decisions at triage and prediction of severity levels of disease. In a nutshell there is a huge scope for the artificial intelligence to prepare for the digital future of healthcare. Adoption of Machine Learning approaches which are an integral part of AI for predictive modeling of healthcare and medical data has been a trend in the recent days, though the clinicians continue to use a subjective approach for the same. Machine Learning techniques basically utilize data driven approaches to explore the nature of the clinical data thus exposing hidden relationships that results in deriving valuable inferences. Intelligence in healthcare paves a innovative way of using technology to transform clinical workflow and patient care pathways.