Big Data, Artificial Intelligence (AI) in Healthcare

- Overview

Artificial intelligence (AI) in healthcare is the use of complex algorithms and software in other words AI to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. Specifically, AI is the ability of computer algorithms to approximate conclusions without direct human input. 

What distinguishes AI technology from traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user. AI does this through machine learning algorithms and deep learning. These algorithms can recognize patterns in behavior and create their own logic. In order to reduce the margin of error, AI algorithms need to be tested repeatedly. AI algorithms behave differently from humans in two ways: (A) algorithms are literal: if you set a goal, the algorithm can't adjust itself and only understand what it has been told explicitly, (B) and some deep learning algorithms are black boxes; algorithms can predict with extreme precision, but not the cause or the why. 

Medical and technological advancements occurring over the last decade that have enabled the growth healthcare-related applications of AI include:

• Improvements in computing power resulting in faster data collection and data processing
• 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[21][22]
• Enhanced the precision of robot-assisted surgery
• Improvements in deeplearning techniques and data logs in rare diseases

[Vancouver, Canada]

- AI in Medicine

We are currently struggling to find the right information either about lifestyle or therapeutic decisions. Medicine is a field in which technology is much needed. Our increasing expectations of the highest quality healthcare and the rapid growth of ever more detailed medical knowledge leave the physician without adequate time to devote to each case and struggling to keep up with the newest developments in his (or her) field. Due to lack of time, most medical decisions must be based on rapid judgments of the case relying on the physician's unaided memory. This could change with AI. 

AI is a pervasive trend that is rapidly accelerating thanks to vast amounts of data and progress in both algorithms and the processing capacity of modern devices. AI has the ability to interpret and analyse a lot of information quickly, which is very promising in the field of medicine where more and more digital data is being generated. Tasks such as the development of new drugs, the sequencing of DNA, the use of implants and smart patches, the remote monitoring of patients and the carrying out of epidemiological studies with thousands of patients are some of the fields that could benefit from this technology in the near future. 

Potential applications for AI in medicine include, for example, AI-assisted robotic surgery; Virtual Nursing Assistants; Workflow and administrative tasks; Guidance for decisions about the best medication to treat an individual with conditions such as Alzheimer's disease or depression; Rapid processing of thousands of medical images, to enhance diagnoses; Algorithms to identify individuals who might benefit from genetic testing for a predisposition to certain cancers; Predictions of risk for heart infection in people with implanted heart devices; etc..  

- Machine Learning (ML) in Medicine

Machine Learning (ML), referring to computer algorithms that can learn to perform particular tasks on their own by analyzing data, is the science of getting computers to act without being explicitly programmed. ML is an approach to achieve AI. Like a human, a ML application learns by experience and/or instruction. By applying the advanced ML capabilities, patients and healthcare providers benefit from more rapid and thorough analysis to translate DNA insights, understand a person’s genetic profile and gather relevant information from medical literature to personalize treatment options for patients. Deep Learning (DL), a technique for implementing ML, has enabled many practical applications of ML and by extension the overall field of AI. 

- Potential AI Applications in Medicine

Various specialties in medicine have shown an increase in research regarding AI: Radiology, Screening, Psychiatry, Primary Care. Disease Diagnosis, Telehealth, Electronic Health Records, Drug Interactions, Creation of New Drugs, Etc.. 

Potential applications for AI include, for example, Guidance for decisions about the best medication to treat an individual with conditions such as Alzheimer's disease or depression; Rapid processing of thousands of medical images, to enhance diagnoses; Algorithms to identify individuals who might benefit from genetic testing for a predisposition to certain cancers; Predictions of risk for heart infection in people with implanted heart devices.