ARTIFICIAL INTELLIGENCE IN HEALTHCARE
- Amruta Bhaskar
- Jan 9, 2020
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Artificial intelligence is a computer science that uses algorithms, heuristics, pattern matching, rules, deep learning and intellectual that is computing to approximate conclusion without direct human effort. Researchers can solve complex problems by using AI which would be difficult or almost impossible for humans to solve. As AI can classify meaningful relationships in raw data, it can be used to support diagnosing, treating and expecting outcomes in many medical situations. AI has the potential to be applied in almost every field of medicine including drug development, patient monitoring, and personalized patient treatment plans.
Artificial intelligence in medicine goes as far back as 1972 with Stanford University’s MYCIN. It is an AI prototype program used for treating blood infections. Early AI research continued at large US institutions including MIT-Tufts, Pittsburgh, Stanford, and Rutgers. In 1980 Stanford continued its medical AI work with the Stanford University Medical Experiment Computer-Artificial intelligence in medicine project.
While AI was being “the next big thing” for decades, it widespread practical uses to the only beginning of the 2000s. It has drawn more than $17 Billion in investments since 2009 and will likely grow to $36.8 Billion by 2025.
AI is patterned after the brain’s neural networks. It uses various layers of non-linear processing units to “teach” itself how to understand data-classifying the record or making predictions. AI can manufacture electronic health record data and unstructured data to make predictions about patient health. For instance, AI software can rapidly read a retinal image or flag cases for follow up when many manual reviews would be too cumbersome.
AI can be used in a variety of ways in medicine, such as:
Annotator for clinical data: Around 80 percent of healthcare data is unstructured, AI can read and understand this unstructured data. It can process natural language which allows reading clinical text from any source. AI helps to categorize and code any medical and social concepts.
Insights for patient data: Artificial Intelligence can identify the problems in patients both, structured and unstructured text by historical medical records, which summarizes the history of their care around those problems and it can provide a cognitive summary of patient records.
Patient similarity: AI can identify a measure of clinical similarity between patients. This allows researchers to create dynamic patient cohorts, rather than static cohorts. It also enables an understanding of which care path works better for a given group of patients.
Medical insights: researchers can find information with AI technologies in unstructured medical literature to support hypotheses that help in the discovery of new insights. AI can read via a complete set of medical literature, such as Medline, and identify the documents that are semantically related to any combination of medical concepts.
Previously, the widespread use of AI in medicine, analytic models in healthcare could only limit variables in well-cleansed health data. With AI, neural networks can process masses of raw data and it also helps to know how to organize that data using the most important variables in predicting health outcomes.
Nowadays, AI technologies such as IBM Watson are being used at Memorial Sloan Kettering Cancer Centre to support diagnosis and create management plans for oncology patients. Watson is completing these plans by effectively synthesizing millions of medical reports, patient records, clinical trials and medical journals. Watson’s results are regularly “out-diagnosing” medical residents in certain situations. By using AI technology IBM has also partnered with CVS Health for chronic disease treatment using AI technology. AI is also used to analyze scientific documents to find new connections for drug development.
At present, AI is used in medicine include patient care in radiology. AI can search and quickly interpret billions of data points in both text and image data within the patient’s electronic medical record. It can do this using other patients similar cases and across the most up-to-date medical research.
AI can abstract unstructured data from peer-reviewed literature in genomics to continually grow its knowledge base. It provides various information and clinical content that is up-to-date based on the latest approved therapeutic options that include targeted and immunotherapy options, professional guidelines, biomarker-based clinical trial options, genomic databases and relevant publications.
Artificial intelligence is important in medicine as it can potentially optimize the care trajectory of chronic disease patients, it suggests precision therapies for complex illnesses and improves subject enrolment into clinical trials.
AI in medicine is important as keeping abreast of mountains of data, medical data is expected to double every 73 days by 2020. It can make sense of the irresistible amount of clinical data, genomic data and social causes of health data to find the best path for each patient. By providing relevant background, AI can empower physicians to see quickly interpreting billions of data points- in both text and image data to identify contextually relevant information.
AI can assist by analyzing structured and unstructured patient data and presenting visions for the physician’s consideration. This helps the physician to communicate accurately.
Using new technologies of software in AI in medicine offer promise for addressing the challenge of cognitive solutions that are exactly designed to integrate and analyze big data sets. AI software can understand various types of data such as lab values in a structured database or the text of scientific publications. This software is trained to understand technical, industry-specific content and to use advanced reasoning. IBM is one of the pioneers that has developed AI software specifically for medicine. IBM AI (Watson) technology is used worldwide by more than 230 healthcare organizations.
AI can examine huge amounts of data and turn that information into functional tools that can assist both doctors and patients. The enlarged edition of AI into everyday medical applications might improve the efficiency of treatments and lower costs in different ways.
There are 8 ways of transformation of AI and Robotics such as Training, Keeping well, Research, Early detection, End of care, Diagnosis, Treatment, Decision making. AI is not only one technology but rather a collection of them. Most of the technologies have immediate consequences to the healthcare field, but the specific processes and tasks they support differ widely.
Natural Language Processing of human language has been a goal of AI research since the 1950s. This field, NLP, includes applications such as speech recognition, text analysis, translation and other goals related to language. NLP is based on machine learning and has contributed to a recent increase in the accuracy of recognition.
Physicals robots are well known, around the world more than 200,000 industrial robots are installed each year. They perform pre-defined tasks like lifting, repositioning, welding or assembling objects in places of factories and warehouses, and delivering supplies in hospitals. Nowadays, Robots have become more collaborative with humans are more easily trained by moving them through the desired task.
According to David B. Agus, MD, a professor of medicine and engineering at the University of Southern California Keck School of Medicine and Viterbi School of Engineering, AI is here to fundamentally change medicine so it’s important to separate fact from science fiction.