Advances in the arena of Artificial intelligence (AI) have begun to lay the groundwork for the implementation of AI in clinical decision-making. The next generation of data collection and analysis technologies will generate new needs for clinicians: they need to incorporate increasing volumes of patient data with several new sources of information in order to make precise and appropriate decisions for their patients ‘ well-being. In the area of surgery, as robot-assisted surgery enters medical practice, AI could potentially enhance the complex procedures performed by camera-assisted mechanical arms by evaluating the problem space and guiding the robots through the optimal intervention sequence.

Health systems will also greatly benefit from the application of AI. Combined with AI, large medical data will help to build risk-based outcome predictions. Also there are significant gains in efficiency and productivity to be achieved by enhancements to the AI-powered process, including reporting and back-office administration such as coding, billing and scheduling. At the same time, there is also a need to bridge the educational gap for clinicians who may not have a good understanding of AI or willingly accept it.

This approach will build and optimize a hybrid workforce by slowly offsetting roles that machines do best and replacing them with duties best suited to humans.Today, we are beginning to see budding projects that harness data channels obtained from the mentioned sources, along with the computing power to influence patient experiences and outcomes, across a range of pilots and proof-of-concept worldwide. In relation to addressing administrative challenges, data quality is critical to successful collaboration between AI and human health care contribution. Despite continued efforts to resolve this concern at multiple levels throughout every advanced health care system, health data today remains largely unstructured.

AI has the potential to improve the quality of care by improving workflows and reducing medical errors; enabling the development of more sustainable value- or quality-based care; and has the potential to reverse the decline in face-to-face treatment between clinician and patient, where physicians and nurses can be released for increased patient interaction.

Nevertheless, the degree to which Artificial Intelligence can be scaled and the level to which the use of patient data and decision-making algorithms will be seen. Due to clinicians ‘ skepticism about their mode of operation, the non-transparency of medical algorithms today that restrict their clinical take-up. Consequently, explainable or transparent AI is one emerging area of research working to counter these concerns and models for continuously validating and verifying these algorithms will be a necessity.