Aritificial Intelligence in Medicine

ARTIFICIAL INTELLIGENCE IN MEDICAL TECHNOLOGY

What is ARTIFICIAL INTELLIGENCE?

The term AI was devised by John McCarthy, an American computer scientist, in 1956. AI or artificial intelligence is the stimulation of human intelligence processes by computer systems. These processes include learning (the attainment of the information and rules for using the information), reasoning (using the rules to reach estimated or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.

Today, it is an umbrella term that incorporates everything from robotic process automation to actual robotics. AI can perform tasks such as identifying patterns in the data more efficiently than humans, enabling businesses to gain more understanding out of the data.

ARTIFICIAL INTELLIGENCE IN MEDICAL TECHNOLOGY

Medical artificial intelligence mainly uses techniques of computer to perform clinical diagnoses and to suggest relevant treatments. AI has the capability of identifying meaningful relationships in a data set and is widely used in many clinical situations to diagnose, treat, and predict the results. The purpose of this issue is to demonstrate the capability of several intelligent approaches exploited in the medical informatics technologies and applications.

Artificial intelligence in health care uses algorithms and software to approximate human understanding in the analysis of complex medical data. The primary aim of health related AI applications is to analyse prevention or treatment techniques and patient outcomes. AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine and patient monitoring and care, among others. Medical institutions such as The Mayo Clinic, Memorial Sloan Kettering Cancer Center and National Health Service, multinational technology companies such as IBM, Google, have created solutions for the usage of AI in various industries.

Medical technological improvements which have occurred during this half century period have enabled the growth of healthcare related applications of AI, which include:

• Improvements in computing power resulting in faster data collection and data processing.
• Increased volume and availability of healthcare data from personal and healthcare related devices.
• Growth of genomic sequencing databases.
• Widespread implementation of electronic health record systems.
• Improvements in natural language processing and computer vision, enabling machines to replicate human perceptual processes.

Common setbacks of artificial intelligence in healthcare include a lack of data exchange, regulatory compliance requirements, and patient and provider adoption. AI has come across all of these issues, narrowing down the areas in which it can succeed.

The most popular use of artificial intelligence in healthcare is in IBM’s smart cloud, where Watson lives. The Watson platform has been used within healthcare while dealing with patient risk assessment, oncology and payers.

Examples of companies using artificial intelligence in medical field:

I. IBM:

IBM’s Watson oncology is in development at Memorial Cancer Centre and Cleveland Clinic. IBM is also working with CVS Health on AI applications in chronic treatment.

II. Microsoft:

Microsoft has been working extensively on medical research with their keen interest on cancer related issues. It analyses medical research to predict the most effective cancer drug treatment options for patients. Other projects include medical image analysis of tumour progression and development of cancer cells.

III. Intel:

Intel capital recently invested in startup Lumiata which uses AI to identify patient’s risk and develop care options.

The capabilities of AI do not stop at understanding human commands and knowing what type of response is needed. For example, AI has been used in many advanced use cases in oncology to help detect abnormalities in X-rays and MRI’s, in genomics to perform complex processing and in precision medicine to provide assistance in creating highly customised treatments for individual patients.

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