ARTIFICIAL INTELLIGENCE IN MEDICINE
Artificial Intelligence (AI) is a wide-going part of software engineering worried about building keen machines equipped for performing undertakings that generally require human insight. Computer-based intelligence is an interdisciplinary science with numerous methodologies; however, progressions in AI and profound learning are making a change in outlook for all intents and purposes in each tech business area. (builtin, 2019)
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There are three types of AI: narrow AI or weak AI, which has a thin scope of capabilities, strong AI or general AI comparable to human abilities, and artificial superintelligence, which is more fit than human. (Brodie O'Carroll, 2017)
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How does AI work?
Artificial Intelligence works by joining a lot of information with quick, iterative handling and insightful algorithms, permitting the software to gain consequently from examples or highlights in the information.(SAS)
AI is an extensive study with many theories; however, we will look at machine learning and deep learning as they are more relevant to medicine. Machine learning is an artificial intelligence (AI) technology that gives programs the ability to learn and develop from experience automatically without being programmed directly. Machine learning focuses on creating computer programs that can learn for themselves and view and use data (Expert System Team, 2020). Deep Learning is a machine learning subfield that discusses algorithms inspired by brain structure and capacity called artificial neural networks (Jason Brownlee, 2019) .
Application of AI in Medicine
Data is filtered through a cascade of multiple layers in deep learning models, with each subsequent layer using the output from the previous one to inform its performance. If they process more data, deep learning models can get more and more detailed, ultimately learning from previous results to improve their capacity to create connections and links. Deep learning is loosely based on how biological neurons communicate with each other in the brains of animals to interpret results. Each subsequent layer of nodes is triggered as it absorbs stimulus from its neighboring neurons, analogous to the way electrical signals pass through the living cells.(HEALTH IT ANALYTICS, 2018)
The primary care doctors will use AI to take their notes, evaluate their discussions with patients, and explicitly input the necessary details into EHR programs. This software can capture and interpret patient data and present it and insight into patients' medical needs to primary care doctors. It takes years to look for and grow therapeutic agents against a particular illness by clinical trials and costs a gazillion dollars. To cite a recent example, AI was used to screen existing drugs that could be used to tackle the evolving threat of the Ebola virus that would otherwise have taken years to process. We will adopt the latest definition of "precision medicine" with the aid of AI. There is an equal number of critics as advocates in this modern age of AI-augmented practice. The increased use of technology has limited the number of career openings that many doctors are worried about while creating and practicing physicians. Machines can translate human actions analytically and objectively, but machines cannot master such human qualities such as rational thought, organizational and communication abilities, emotional intelligence, and imagination. (Dr. Amisha, 2019)
Artificial intelligence will remove essential data from a patient's electronic impression. From the start, this will spare time and improve effectiveness, yet following satisfactory testing, it will likewise straightforwardly direct patient administration. Take the case of a counsel with a patient with type 2 diabetes; presently, a clinician invests critical energy perusing outpatient letters, checking blood tests, and finding clinical rules from various separated frameworks. Interestingly, AI could consequently set up the main dangers and activities given the patient's clinical record. Therefore, it could change over the discussion's recorded exchange into a synopsis letter for the clinician to support or alter. Both of these applications would spare ample time and could be actualized rapidly because they help clinicians instead of supplanting them. They will be granted more responsibility as these systems become well validated. AI may calculate the statin starting threshold for the patient with type 2 diabetes on an individualized basis due to the nuisances of the patient's background rather than a rigidly specified 'one-size-fits-all' algorithm. The study needed for this 'personalized medicine will only be feasible by intelligently summarizing vast amounts of medical knowledge through AI. (Varun H Buch, 2018)
References
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Brodie O'Carroll. (2017, October 24). Artificial Intelligence.
What are the 3 types of AI? A guide to narrow, general, and super artificial intelligence. Retrieved from https://codebots.com/artificial-intelligence/the-3-types-of-ai-is-the-third-even-possible
builtin. (2019). Artificial Intelligence.
What is Artificial Intelligence? How Does AI Work? Retrieved from https://builtin.com/artificial-intelligence
Dr Amisha. (2019). Allocation of Pysician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties. Ann Intern Med, 2328–2331.
Expert System Team. (2020, May 6). What is Machine Learning? A Definition. Retrieved from https://www.expert.ai/blog/machine-learning-definition/
HEALTH IT ANALYTICS. (2018, November 30). What Is Deep Learning and How Will It Change Healthcare? Retrieved from https://healthitanalytics.com/features/what-is-deep-learning-and-how-will-it-change-healthcare
Jason Brownlee. (2019, August 14). What is Deep Learning? Retrieved from https://machinelearningmastery.com/what-is-deep-learning/
SAS. Artificial Intelligence
What it is and why it matters. Retrieved from https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html#:~:text=AI%20works%20by%20combining%20large,or%20features%20in%20the%20data.&text=Cognitive%20computing%20is%20a%20subfield,human%2Dlike%20interaction%20with%20machines.
Varun H Buch, I. A. a. M. M. (2018). Artificial intelligence in medicine: current trends and future possibilities. British Journal of General Practice, 143-144.