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Edward G. Miner Library

Graduate Student Guide: Applications

Information and tools for URMC Ph.D. and Master's students

Generative AI Product Tracker

Ithaka S+R helps academic and cultural communities serve the public good and navigate economic, technological, and demographic change. Their Generative AI Product Tracker lists generative AI products that are either marketed specifically towards postsecondary faculty or students or appear to be actively in use by postsecondary faculty or students for teaching, learning, or research activities:

Available at: https://docs.google.com/document/d/1yg7KJmMl7d_xZAGgHiXc-9iSNT5vmmp1iyK5zYcS2IE/edit

To learn more visit: https://sr.ithaka.org/publications/generative-ai-in-higher-education/

Keenious

 

Keenious is an AI powered academic search engine that recommends relevant research papers.

You can easily add Keenious as a sidebar in Microsoft Word or Google Docs so that you can get suggested relevant research without breaking your workflow.

Amazon Comprehend Medical

Amazon Comprehend Medical is a HIPAA-eligible natural language processing (NLP) service that uses machine learning that has been pre-trained to understand and extract health data from medical text, such as prescriptions, procedures, or diagnoses.

Iris.ai

Iris.ai is an AI-powered tool which allows researchers to analyze, summarize, and connect a vast array of scientific papers and patents, making it easier to find relevant studies, identify trends, and gain insights across various disciplines. Notable features include: Smart search and a wide range of smart filters, reading list analysis, auto-generated summaries, autonomous extraction and systematizing of data.

Covidence

Covidence uses machine learning to identify trends in your team’s past screening behavior on the review to determine and display the studies that are most likely to be included first. The more studies you screen, the stronger the system’s prediction will be.

Scite.ai

Scite.ai is AI powered tool that evaluates the reliability of scientific claims and uses proprietary deep learning algorithms and natural language processing techniques to analyze the text of scientific papers and extra citation contexts. Scite's dataset is derived from the PubMed Central Open Access (PMC-OA) corpus.