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Uses of artificial intelligence (AI) in measuring the impact of research – Pubrica

Full information : https://bit.ly/3c6dWlY<br>Asked 20 years ago whether self-driving cars or identification by retinal scanning would be feasible, there likely would have been a collective u201cDream on!u201d. And yet, these are not only our present day reality, they represent only the icing on the cake. <br>Reference : https://pubrica.com/services/data-analytics-machine-learning/<br>

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Uses of artificial intelligence (AI) in measuring the impact of research – Pubrica

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  1. USES OF ARTIFICIAL INTELLIGENCE (AI) IN MEASURING THE IMPACT OFRESEARCH An Academic presentationby Dr. Nancy Agens, Head, Technical Operations, Pubrica Group: www.pubrica.com Email:sales@pubrica.com

  2. Today'sDiscussion Outline Inbrief Basics ofAI AI in Medical Research AI inPathology. What’s on the Horizon Conclusion

  3. InBrief 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 vieworpurchase. To get to an answer, wecan assess how AI has impacted medical research, what strides have been made in converting research & development into commercialized AI- basedtechnology.

  4. Basics ofAI AI is essentially a branch of computer science whereby data are collectedand algorithms created based on patterns found across the data using deep machinelearning. 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 tocomplete. For AI to be reliable, it is absolutely critical that the data numbers be high and ofsufficient breath and quality to avoid skewed, biased results that are notgeneralizable. 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 preciseoutput.

  5. AI in MedicalResearch Several areas of medicine have been particularly amenable to AI based on the sheer volume of datareadilyavailable: radiology, ophthalmology andpathology. The data are derived from the vast numbers of patient-derived images and recordings that these medical segments collect tomakediagnoses: from X-rays to CT scans, MRI imaging, retinal imaging and tissue histologyimages. Another compelling example is AI applications in radiology, specifically breast mammographydiagnostics. Based on 100,000 breast mammogram images, Google’s health researcharm very recently announced that their AI-trained software resulted in 5.7% fewer false positive and 9.4% fewer false negative rates than trainedradiologists

  6. AI inPathology AI in pathology has also made strides on the research and development front, particularly in cancer diagnostics given its extensive dependence on tissuemorphology. The push for some form of automatized assistance comes partially frominterobserver variability in the analysis of H&E stained tissue and the sheer volume ofimages. 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 bemarketed. While pathologists are still required to review and interpret the images, they can do so from digitized images rather than tissuesamples.

  7. What’s on theHorizon The market has been bullish on AI medical R&D being translated into commercialproducts. The appetite for AI-based medicinecontinues to increase at a rate of 40% and is expected to top $6.6 billion by2021. With funding supporting AI R&D and a marketplace appearing ready to adopt, discussions abound over the implications of AI for physicians in theworkforce. 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 isused.

  8. Conclusion 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 forphysicians. 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 overworkedproviders. This could ultimately translate into more face time with patients -- “yes, the doctor isin.”

  9. ContactUs UNITEDKINGDOM +44-1143520021 INDIA +91-9884350006 EMAIL sales@pubrica.com

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