NEJM AI @NEJM_AI
NEJM AI is a new journal on medical artificial intelligence and machine learning from NEJM Group, the publisher of @NEJM. ai.nejm.org Boston Joined November 2022-
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A multiyear effort indicates that AI-generated feedback and guidance to providers on five key elements of their clinical notes is having a positive impact at @nyulangone.
Dr. @DaphneKoller outlines the challenges of using AI to identify new therapeutic hypotheses. She discusses “biology risk” and strategies @insitro is deploying to increase the probability of success of drug discovery. Listen to the full episode: nejm.ai/ep17
New study reveals limitations of large language models (LLMs) in medical coding. Key findings: Tokenization algorithms lead to mismatches between billing codes & descriptions GPT-4 had highest exact match rate, but only 45.9% for ICD-9-CM LLMs often generate codes with…
Tokenization algorithms may be to blame when generative large language models inconsistently match medical billing codes to their preferred code descriptions. Learn more in a new Datasets, Benchmarks, and Protocols article by @AliSoroushMD et al.: nejm.ai/3w7JKGn
Perspective: Who’s Training Whom? by Jonathan H. Chen, M.D., Ph.D. (@jonc101x) nejm.ai/3Un9Oq4
Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying - @NEJM_AI 🔬 They evaluated GPT-3.5, GPT-4, Gemini Pro, and Llama2-70b Chat. ‼️ Conclusion: #LLMs are not appropriate for use in medical coding tasks buff.ly/3UBBFDp
How to diagnose rare diseases more accurately? Use #AI As shown with a new tool ai.marrvel.org ai.nejm.org/doi/full/10.10… @DongxueMao @ZhandongLiu @ChaozhongL @linhuaw_w @sasipasu @ramioh and colleagues @bcmhouston @NEJM_AI
Important case study by💫@renato_umeton et al. in @NEJM_AI reporting on the challenges and lessons learned in the deployment of large language models (LLMs) at @DanaFarber for use in basic research, clinical research, operations, & more! @OncoAlert ai.nejm.org/doi/full/10.10…
Great @NEJM_AI article illustrating how reimbursement impacts AI commercialization strategies. Article compares pros/cons for 3 reimbursement models: fee-for-service, value-based, & revenue sharing. Hugely important for driving AI adoption. ai.nejm.org/doi/full/10.10…
Original Article: Comparative Evaluation of LLMs in Clinical Oncology nejm.ai/4aJWOAY
In a new Perspective, Jonathan H. Chen, M.D., Ph.D. (@jonc101x), discusses how human–computer interactions with artificial intelligence may stimulate our most human activities needed in medicine. Read "Who’s Training Whom?" here: nejm.ai/3Un9Oq4
Datasets, Benchmarks, and Protocols: GPT versus Resident Physicians — A Benchmark Based on Official Board Scores by Dr. @uriel_katz_ and team: nejm.ai/3Q7hB9j #ArtificialIntelligence
Sustainable reimbursement is key for medical AI to scale and benefit patients. A new Policy Corner article by @MichaelAbramoff, @TinglongDai, and @james_y_zou explores fee-for-service and value-based care payment models for the use of medical AI. nejm.ai/445c5JT
This case study explores how @nyulangone leveraged AI to address the challenge to improve the content and quality of medical documentation.
Comparative Evaluation of #LLMs in Clinical #Oncology @NEJM_AI Although this performance is impressive, all LLMs continue to have clinically significant error rates, including examples of overconfidence and consistent inaccuracies @_SEOM ai.nejm.org/doi/full/10.10…
Pranav Rajpurkar @pranavrajpurkar
21K Followers 715 Following Professor at Harvard | Medical Artificial Intelligence | https://t.co/Z6tBGoluEGNEJM @NEJM
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3K Followers 373 Following Assistant Professor of Biomedical Informatics, Harvard Medical School @HarvardDBMI | Founding Deputy Editor, @NEJM_AI | Co-host, NEJM AI Grand RoundsKarandeep Singh @kdpsinghlab
10K Followers 3K Following Jacobs Chancellor’s Endowed Chair @UCSanDiego. Chief Health AI Officer @UCSDHealth. Creator of Tidier.jl #JuliaLang. #GoBlue. Views own.Andrew Beam @AndrewLBeam
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How is AI applied in healthcare? And what issues do clinicians see with these technologies? Hear from Patrick McGinty, General Manager, @NEJM Group & Publisher, @NEJM_AI, about the impact: ow.ly/w91q50RsrHv #AI #HealthcareAI
How to make #AI-enabled #drugdiscovery and #drugdevelopment more effective, and sustainable from @DaphneKoller, CEO @insitro. 📢Spoiler alert: Computational Biology is hard!!! #NewMedsMatter #ResponsibleAI #HealthcareInnovation #HealthcareRevolution #MedTwitter @NEJM_AI
Dr. @DaphneKoller outlines the challenges of using AI to identify new therapeutic hypotheses. She discusses “biology risk” and strategies @insitro is deploying to increase the probability of success of drug discovery. Listen to the full episode: nejm.ai/ep17
A comprehensive and thoughtful editorial.
Volume 1, No. 5 is now available! Here are the latest articles in NEJM AI: Editorial: Are Stronger Feature Representations All You Need for Histology Image Search? nejm.ai/4b7zhtl 🔖 Save for later and share with a colleague
@realmargaretta @DanaFarber @NEJM_AI Thank you for the highlight @realmargaretta 💫! It took an extensive list of stakeholders from inside & outside the institute to make this a reality (🙏 list at ai.nejm.org/stoken/default… )
In just 6 months @DanaFarber Cancer Institute deployed GPT-4 Institute-Wide linkedin.com/pulse/dana-far… #ArtificialIntelligence #AI @renato_umeton @NEJM_AI @Microsoft @Azure
New study reveals limitations of large language models (LLMs) in medical coding. Key findings: Tokenization algorithms lead to mismatches between billing codes & descriptions GPT-4 had highest exact match rate, but only 45.9% for ICD-9-CM LLMs often generate codes with…
AI-MARRVEL @NEJM_AI: Knowledge-Driven AI System for Diagnosing Mendelian Disorders. Trained on 3.5M SNPs from thousands of Mendelian disorders - precision 98% (current precision~30% and it takes >6 months), correctly identified 57% of diagnosable cases. ai.nejm.org/doi/full/10.10…
"Of the models tested on a standardized set of oncology questions, GPT-4 was observed to have the highest performance. ... all #LLMs continue to have clinically significant error rates, including examples of overconfidence and consistent inaccuracies."
Original Article: Comparative Evaluation of LLMs in Clinical Oncology nejm.ai/4aJWOAY
Great study benchmarking LLMs on clinical oncology questions! They employ some similar techniques as we do, in particular the consistency approach on repeated prompts. The self-assessed confidence is a very interesting approach I'd like to see more in the future.
Original Article: Comparative Evaluation of LLMs in Clinical Oncology nejm.ai/4aJWOAY
Is AI-driven clinical-grade search of pathology images/slides ready for prime time? @NEJM_AI a case study with four leading programs suggests not yet. ai.nejm.org/doi/full/10.10… Accompanying editorial provides context and roadmap ai.nejm.org/doi/full/10.10…
A recent study in @NEJM_AI highlights our collaboration w/@bcmhouston to leverage #AI and machine learning to more efficiently diagnose rare Mendelian disorders. Learn more: baylorgenetics.com/news/using-ai-…
GPT4、もはやアメリカ医師国家試験でレジデントに並ぶのか… #AI医療 #AI創薬
Datasets, Benchmarks, and Protocols: GPT versus Resident Physicians — A Benchmark Based on Official Board Scores by Dr. @uriel_katz_ and team: nejm.ai/3Q7hB9j #ArtificialIntelligence
Exciting developments in #AI and #healthcare! While the results are promising, there's potential for even greater accuracy with advanced prompting techniques like #MedPrompt. This is just the beginning of AI's transformative impact in medicine. 😌 #AIinhealthcare #healthcareAI…
Datasets, Benchmarks, and Protocols: GPT versus Resident Physicians — A Benchmark Based on Official Board Scores by Dr. @uriel_katz_ and team: nejm.ai/3Q7hB9j #ArtificialIntelligence
Research & Development AI Benchmark: GPT-4 Vs Physicians in Core Medical Specialties Artificial intelligence, specifically large language models, was compared with resident physicians based on official board scores in core medical disciplines. Generative pretrained…
Datasets, Benchmarks, and Protocols: GPT versus Resident Physicians — A Benchmark Based on Official Board Scores by Dr. @uriel_katz_ and team: nejm.ai/3Q7hB9j #ArtificialIntelligence
This study shows that GPT-4 compares well against a cohort of 849 humans who took medical board exams in 5 specialties in 2022. The analysis is solid but uses zero-shot AI; smarter (and fully automatic) prompting techniques such as MedPrompt would improve GPT-4 even further.
Datasets, Benchmarks, and Protocols: GPT versus Resident Physicians — A Benchmark Based on Official Board Scores by Dr. @uriel_katz_ and team: nejm.ai/3Q7hB9j #ArtificialIntelligence
Important read. "High-Impact Medical Journals Reflect Negative Sentiment Toward #Psychiatry" @NEJM_AI ai.nejm.org/doi/full/10.10…
Sustainable reimbursement is key for scaling medical AI adoption and benefiting patients, as explored in a new Policy Corner article by @MichaelAbramoff, @TinglongDai, and @james_y_zou. T The authors discuss the roles of stakeholders and the alignment of fee-for-service and…
GPT versus Resident Physicians - A Benchmark Based on Official Board Scores @NEJM_AI #AI #IA ai.nejm.org/doi/full/10.10…
@rafaelsidi @NEJM_AI Enjoy the full-text 🚀 ai.nejm.org/stoken/default…