Scientific research indicates that for every hour spent in direct patient care, physicians spend nearly two additional hours on administrative tasks, particularly on electronic health records (EHRs) and desk work. This excessive administrative burden not only reduces the time available for patient care but also contributes to physician burnout and inefficiencies in healthcare delivery. To address these challenges, this study introduces MediGen, a fine-tuned large language model (LLM) designed to automate the generation of medical reports from medical dialogues. By leveraging state-of-the-art methodologies for fine-tuning open-source pretrained models, including LLaMA3-8B, MediGen achieves high accuracy in transcribing and summarizing clinical interactions. The fine-tuned LLaMA3-8B model demonstrated promising results, achieving a ROUGE score of 58% and a BERTScore-F1 of 72%, indicating its effectiveness in generating accurate and clinically relevant medical reports. These findings suggest that MediGen has the potential to significantly reduce the administrative workload on physicians, improving both healthcare efficiency and physician well-being.

Data will control the twenty-first century. Every company, big or small, is attempting to use data to their advantage. Data-driven insights could aid businesses in transforming and targeting new markets, addressing customer pain points, increasing revenue, and more. As a result, a growing number of companies are concentrating on data collecting, interpretation, and application. of India sees significant digitisation of its industries and services, making it the second-largest data science hub. Analysts estimate that by 2026, the country will have around 11 million job openings.

SAN FRANCISCO--July 28, 2021-- The American College of Radiology Data Science Institute (ACR DSI) and the American Academy of Ophthalmology today announced a collaboration that will expand ACR DSI's groundbreaking AI-LAB platform to include eye care. Leveraging use cases and data from the Academy, this collaboration will accelerate the use of machine learning in the ophthalmic industry to the benefit of patients across the globe. "We've now made it easier for the ophthalmology community to access real world examples for our own use cases. By working together with ACR, we are leveraging a platform developed for the radiology community to educate our own community about AI development and encouraging new AI to be developed that will benefit our specialty," said Tamara R. Fountain, MD, president of the American Academy of Ophthalmology. The Academy will provide the ophthalmology content and the ACR will provide the IT infrastructure to integrate the use cases and datasets into the landmark AI-LAB.

The conference will host keynote presentations from leading voices in data-driven innovation, lightning talks from Columbia University researchers, & interactive poster & technology demonstrations. Data Science Day provides a forum for innovators in academia, industry, & government to connect. Keynote Speakers Pat Bajari, Chief Economist, Vice President of Artificial Intelligence, Amazon Eric Schmidt, Technical Advisor to the Board, Alphabet Columbia University & Columbia University Data ScienceInstitute Affiliated Faculty Talks Lightning Talk:Cause, Learn, Optimize & Reason Melanie Wall, Professor, Department of Biostatistics, Mailman School of Public Health; & Director of Mental Health Data Science in the Department of Psychiatry, Columbia University Irving Medical Center & the New York State Psychiatric Institute Samory Kpotufe, Associate Professor, Department of Statistics, Faculty of Arts & Sciences Elias Bareinboim, Associate Professor, Department of Computer Science, Columbia Engineering; & Director of the Causal Artificial Intelligence (CausalAI) Laboratory, Columbia University Clifford Stein, Professor of Industrial Engineering & Operations Research, Department of Computer Science, Columbia Engineering; & Associate Director for Research, Data Science Institute, Columbia University Lightning Talk: Human Machine: A New Hybrid World Oded Netzer, Professor of Business, Marketing Division, Columbia Business School Lydia Chilton, Assistant Professor, Department of Computer Science, Columbia Engineering Sarah Rossetti, Assistant Professor, Biomedical Informatics, Department of Biomedical Informatics; Assistant Professor, School of Nursing, Columbia University Irving Medical Center Lightning Talk: Ethics & Privacy: Terms of Usage Roxana Geambasu, Associate Professor, Department of Computer Science, Columbia Engineering Rafael Yuste, Professor, Department of Biological Sciences, Faculty of Arts & Sciences Jeff Goldsmith, Associate Professor, Department of Biostatistics, Columbia University Mailman School of Public Health What are my transportation/parking options for getting to & from the event? Please visit the following link for directions & parking information: http://transportation.columbia.edu/For How can I contact the organizer with any questions?

How do clinicians set rules that allow professionals "to make good use of technology to find patterns in complex data" but also "stop companies from extracting unethical value from those data?" Geis, from the American College of Radiology (ACR) Data Science Institute, is one of the authors of a joint statement that addresses the potential for the unethical use of data, the bias inherent in datasets, and the limits of algorithmic learning, and was the moderator of a session on the topic at the Radiological Society of North America (RSNA) 2019 Annual Meeting in Chicago. There's a very big grey area between an absolute ethical approach to data use and decisions that are profit-driven, he told Medscape Medical News. "Sitting on the sainthood side, I can stick to doing only what I see as good for my patients, maybe even taking vows of poverty," he said. "On the extreme other side, I'm doing things that put me in prison."

This project aims to develop opportunities for teachers to practice dialogue techniques in realistic but safe, virtually simulated environments. Rather than forcing young teachers to first encounter these conflicts in real situations, Adewole and Bywater will build a simulator to enable teachers to practice having difficult conversations using immersive 3D virtual reality. The system will create realistic settings that involve conversations between the teacher and a diverse group of artificially intelligent virtual students. In this project, we will learn and evaluate adaptive emotion regulation (ER) strategies for socially anxious individuals by developing methods that combine network analysis with reinforcement learning in an off-policy setting. This interdisciplinary collaboration between psychology and engineering permits a deeper understanding of the dynamics of ER in real life.

Earlier this week, a consensus draft document dealing with the ethics of AI in medical imaging was posted on the ACR website. I would like to congratulate the authors, listed with their affiliations below, on a collaborative effort to address this important topic. This was a multi-society effort including the American College of Radiology (ACR), American Association of Physicists in Medicine (AAPM), Canadian Association of Radiologists (CAR), European Society of Radiology (ESR), Radiological Society of North American (RSNA), Society for Imaging Informatics in Medicine (SIIM) and European Society of Medical Imaging Informatics (EuSoMII). Importantly, the group included trainees, patients and other stakeholders such as an ethicist from MIT. But despite the wide ranging backgrounds and expert input that created this draft, the writing group and our Societies' leaders are very clear that this is just that: a draft.

A recent article in Bloomberg magazine called data science "America's hottest job." In response to increasing demand by industry, government and academia for highly trained data scientists, the University of Virginia's Data Science Institute is launching an online version of its Master of Science in Data Science program next summer. Through a collaboration with Noodle Partners, a company that provides online education management support, the degree can be earned entirely online, and will mirror the curriculum of the Data Science Institute's residential M.S.D.S. program. Currently, 49 students are enrolled in UVA's residential program and 20 more are working toward joint MBA/M.S. in Data Science degrees. The online M.S.D.S. program initially will enroll about 30 students, and that number is likely to grow each semester as the program modestly expands.

New York, NY--June 19, 2018--A major challenge in current climate prediction models is how to accurately represent clouds and their atmospheric heating and moistening. This challenge is behind the wide spread in climate prediction. Yet accurate predictions of global warming in response to increased greenhouse gas concentrations are essential for policy-makers (e.g. the Paris climate agreement). In a paper recently published online in Geophysical Research Letters (May 23), researchers led by Pierre Gentine, associate professor of earth and environmental engineering at Columbia Engineering, demonstrate that machine learning techniques can be used to tackle this issue and better represent clouds in coarse resolution ( 100km) climate models, with the potential to narrow the range of prediction. "This could be a real game-changer for climate prediction," says Gentine, lead author of the paper, and a member of the Earth Institute and the Data Science Institute.

A link has been posted to your Facebook feed. What would it take to inhabit Mars? Or bring self-driving cars to Middle Tennessee? The one thing connecting both ideas is that they require big data to address them. Researchers at Middle Tennessee State University's new Data Science Institute are planning to conduct the research to find out.