**1. Introduction**

Artificial Intelligence (AI) is making significant inroads into healthcare, as in many other walks of life. Its contribution to clinical decision making, improved outcomes, image interpretation especially in radiology, pathology and oncology, data mining, new insights generation and elimination of human errors creeping in healthcare delivery is noteworthy. Yet there are physicians as well as patients who are wary of its role and its implementation in routine clinical practice.

Data is key for successful AI and machine learning (ML) and more is not always better. Statistics and artificial intelligence need to analyse large data sets to discover useful information and the data should be accurate, appropriate, and clean. Care of the data in healthcare at its generation—the point of care and its value in AI and the healthcare provider's role cannot be overemphasised. Is AI a totally computer scientists' dominion? What is the doctor's role in it? Are they just the beneficiary.

Any discussion on AI and its role in healthcare brings into consideration the issues like hype and hope associated, who are the stakeholders, healthcare personnel and the patients' views and their acceptance, data at its generation—at the point of care, the future of AI in healthcare and how would the curriculum planners in medical education train the medical students, who are the future healthcare providers. Also needed is a deliberation on the issues that are common to Information Technology (IT) like cybersecurity, ethics and legal aspects, privacy, and transparency. This also brings into review various issues the developers of AI solutions in healthcare need to bear in mind. The gaps in the current knowledge and databases also need a thought. Do the AI database and the EHR cover the global scenario adequately. Many areas in the world do not follow the EHR. Are the ethnic and regional differences in health and disease well represented in the Database?
