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Full information : https://bit.ly/3c6dWlY Asked 20 years ago whether self-driving cars or identification by retinal scanning would be feasible, there likely would have been a collective “Dream on!”. And yet, these are not only our present day reality, they represent only the icing on the cake. Reference : https://pubrica.com/services/data-analytics-machine-learning/ Why Pubrica? When you order our services, we promise you the following – Plagiarism free, always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts. Contact us : Web: https://pubrica.com/ Blog: https://pubrica.com/academy/ Email: [email protected] WhatsApp : +91 9884350006 United Kingdom: +44- 74248 10299
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USES OF ARTIFICIAL INTELLIGENCE (AI) IN MEASURING THE IMPACT OF RESEARCH
An Academic presentation byDr. Nancy Agens, Head, Technical Operations, Pubrica Group: www.pubrica.comEmail: [email protected]
In brief
Basics of AI
AI in Medical Research
AI in Pathology.
What’s on the Horizon
Conclusion
Outline
Today's Discussion
Asked 20 years ago whether self-driving cars or identification by retinal scanning would be feasible, there likely would have been a collective “Dream on!”. From Siri to Alexa and Tesla, interactions with machine- based artificial intelligence or AI permeate in our daily lives. Netflix and
Amazon serve as our most loyal personal shoppers, always knowing just what else we may wish to view or purchase. To get to an answer, we can assess how AI has impacted medical research, what strides have been made in converting research & development into commercialized AI-
based technology.
In Brief
AI is essentially a branch of computer science whereby data are collected and algorithms created based on patterns found across the data using deep machine learning.
The output may be a diagnostic, prognostic or disease prediction that appears as if a human had analyzed the data and determined the output, all at a fraction of the time it would take a human to complete.
For AI to be reliable, it is absolutely critical that the data numbers be high and of sufficient breath and quality to avoid skewed, biased results that are not generalizable.
The AI field has matured wherein the scope and quality of training data, augmentation of data and enhanced computational power have resulted in ever more precise output.
Basics of AI
AI in Medical Research
Several areas of medicine have been particularly amenable to AI based on the sheer volume of data readily available: radiology, ophthalmology and pathology.
The data are derived from the vast numbers of patient-derived images and recordings that these medical segments collect to make diagnoses: from X-rays to CT scans, MRI imaging, retinal imaging and tissue histology images.
Another compelling example is AI applications in radiology, specifically breast mammography diagnostics.
Based on 100,000 breast mammogram images, Google’s health research arm very recently announced that their AI-trained software resulted in 5.7% fewer false positive and 9.4% fewer false negative rates than trained radiologists
AI in Pathology
AI in pathology has also made strides on the research and development front, particularly in cancer diagnostics given its extensive dependence on tissue morphology.
The push for some form of automatized assistance comes partially from interobservervariability in the analysis of H&E stained tissue and the sheer volume of images.
Advances in AI-research by Philips led the FDA to grant approval of their IntelliSite Pathology Solution, the first ever whole slide review imaging system to be marketed.
While pathologists are still required to review and interpret the images, they can do so from digitized images rather than tissue samples.
What’s on the Horizon
The market has been bullish on AI medical R&D being translated into commercial products.
The appetite for AI-based medicine continues to increase at a rate of 40% and is expected to top $6.6 billion by 2021.
With funding supporting AI R&D and a marketplace appearing ready to adopt, discussions abound over the implications of AI for physicians in the workforce.
Just take a look at the IDX-DR case: opthalmologist are no longer required to screen for diabetic retinopathy in instances where the IDX-DR screening tool is used.
There may likely be some shifts in the physician workforce, but the optimist in me believes that AI can be leveraged to create new opportunities for physicians.
By relegating more of the routine, repetitive workload to AI, it could importantly provide precious time back to physicians staving off physician burnout, a true modern day symptom afflicting many overworked providers.
This could ultimately translate into more face time with patients -- “yes, the doctor is in.”
Conclusion
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