• In areas where there is a huge gap in essential skilled personnel, particularly in developing countries, artificial intelligence (AI) could offer assistance in those lacking areas of healthcare.

• Standardization of treatments, which are replicated not only in specialized centers, but in any healthcare institution, is another huge benefit of AI, so that the practices implemented in these centers could be produced with the help of artificial intelligence.

• The quality/effectiveness of treatment delivered is another area that could benefit significantly from the use of artificial intelligence. Artificial intelligence allows us to perceive correlations invisible to the human eye between the effectiveness of the treatment and the selected treatment regimen. These correlations can be used in the future to select the optimal treatment in patients with similar symptoms.

• There are a variety of ethical implications around the use of AI in healthcare. Healthcare decisions have been made almost exclusively by humans in the past, and the use of smart machines to make or assist with them raises issues of accountability, transparency, permission and privacy.

• Another issue to address given today's technologies is transparency. Many AI algorithms – particularly deep learning algorithms used for image analysis – are virtually impossible to interpret or explain.

• Mistakes will undoubtedly be made by AI systems in patient diagnosis and treatment and it may be difficult to establish accountability for them. 

• There are also likely to be incidents in which patients receive medical information from AI systems that they would prefer to receive from an empathetic clinician. 

• Machine learning systems in healthcare may also be subject to algorithmic bias, perhaps predicting greater likelihood of disease on the basis of gender or race when those are not actually causal factors.

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A subset of artificial intelligence (AI) and computer science, machine learning (ML) deals with the study and use of data and algorithms that mimic how humans learn. This helps machines gradually improve their accuracy. ML allows software applications to improve their prediction accuracy without being specifically programmed to do so. It estimates new output values by using historical data as input. Machine learning is one of the most common forms of AI; in a 2018 Deloitte survey of 1,100 US managers whose organizations were already pursuing AI, 63% of companies surveyed were employing machine learning in their businesses. 

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Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that enables machines to understand and communicate in natural language, like humans do. It involves using algorithms and techniques such as machine learning, deep learning and text analytics to interpret and analyze natural language content from audio recordings, documents, images or other sources.  

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