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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). AI is transforming the world of medicine. 

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. 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. Three trends drive the DL revolution: more powerful GPUs, sophisticated neural network algorithms modeled on the human brain, and access to the explosion of data from the Internet. Thanks to DL, AI has a bright future. 

Computer vision is a subdomain of AI that deals with how computers gain high level understanding through acquiring, processing and analyzing digital images and video. With Deep Learning (DL), a lot of new applications of computer vision technologies have been introduced. For example, we may use computer vision technologies to process medical images. These technologies help doctors detect malign changes such as tumors and hardening of the arteries and provide highly accurate measurements of organs and blood flow. Some medical startups claim they’ll soon be able to use computers to read X-rays, MRIs, and CT scans more rapidly and accurately than radiologists, to diagnose cancer earlier and less invasively, and to accelerate the search for life-saving pharmaceuticals. Hospitals and imaging centers that can interpret images faster and more accurately with the use of fewer radiologists. 

"Data is the new oil.” If data is the new oil, AI is the new internal combustion engine, converting data into insights, predictions, and recommendations that boost productivity and augment decision-making. AI can spot trends and patterns that we would not otherwise see. After all, AI is not a solution. It is a capability that can be packaged into solutions that can increase their effectiveness, often dramatically. Success in any specific AI application is dependent on integrating context (data). You can't simply just install AI software tools and solve problems. 

Healthcare providers will also share knowledge they glean from treating patients. This is key. In the era of Electronic Health Records (EHR), it is possible to examine the decision outcomes made by doctors. By enabling researchers at the institutions to mine a much larger store of data, they can more easily spot patterns and identify best practices. When it comes to effectiveness of ML, more data almost always yields better results—and the healthcare sector is sitting on a data goldmine. McKinsey estimates that big data and ML in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators. 

The list below is by no means complete, but provides a useful lay-of-the-land of some of ML’s impact in the healthcare industry: Scaled Up/Crowdsourced Medical Data Collection, Disease Identification/Diagnosis, Diagnosis in Medical Imaging, Personalized Treatment/Behavioral Modification, Treatment Queries and Suggestions, Drug Discovery/Manufacturing, Clinical Trial Research, Radiology and Radiotherapy, Smart Electronic Health Records, Epidemic Outbreak Prediction, Robotic Surgery, and Automatic Treatment or Recommendation.


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