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Artificial Intelligence (AI) in Medicine

(Photo: Princeton University, Office of Communications)


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 Artificial Intelligence (AI). 

Artificial Intelligence (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.

AI in medicine is a new research area that combines sophisticated representational and computing techniques with the insights of expert physicians to produce tools for improving health care. AI scans data and uses statistical methods, probability theory, and machine and deep learning to find patterns that are difficult for the human mind to see. One of the most fertile grounds to take advantage of AI in medicine is the acquisition and interpretation of images for diagnosis - such as ultrasound, computerized tomography (CT) or magnetic resonance imaging (MRI). The results, in the form of digital images, must be interpreted by the doctors, who with their training and expertise can extract useful information to reach a diagnosis. As the number of images that are acquired - and their quality, sensitivity and resolution - increase steadily, researchers are working to develop technologies to help radiologists assess these images more quickly, accurately and effectively.This high-level computing augments physicians' knowledge to help doctors make predictions and treatment recommendations that are personalized for individual patients.  

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 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.



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