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Large Language Models (LLMs) have emerged as powerful candidates to inform clinical decision-making processes.
Defining racial and ethnic disparities in pain management
Jana M. Mossey · 2011
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The mythos of model interpretability, 2017
Zachary C. Lipton · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
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Pubmedqa: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu · 2019
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The global landscape of AI ethics guidelines
Anna Jobin, Marcello Ienca, and Effy Vayena · 2019
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Language models are few-shot learners, 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Treating health disparities with artificial intelligence
Irene Y Chen, Shalmali Joshi, and Marzyeh Ghassemi · 2020
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On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Q-pain: a question answering dataset to measure social bias in pain management
Cécile Logé, Emily Ross, David Yaw Amoah Dadey, Saahil Jain, Adriel Saporta, Andrew Y Ng, and Pranav Rajpurkar · 2021
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A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
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Constitutional ai: Harmlessness from ai feedback, 2022
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan · 2022
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Sources of bias in artificial intelligence that perpetuate healthcare disparities—a global review
Leo Anthony Celi, Jacqueline Cellini, Marie-Laure Charpignon, Edward Christopher Dee, Franck Dernoncourt, Rene Eber, William Greig Mitchell, Lama Moukheiber, Julian Schirmer, Julia Situ, Joseph Paguio, Joel Park, Judy Gichoya Wawira, Seth Yao, and for MIT Critical Data · 2022
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Artificial intelligence and health inequities in primary care: a systematic scoping review and framework
Alexander d’Elia, Mark Gabbay, Sarah Rodgers, Ciara Kierans, Elisa Jones, Irum Durrani, Adele Thomas, and Lucy Frith · 2022
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Few-shot learning with semi-supervised transformers for electronic health records
Raphael Poulain, Mehak Gupta, and Rahmatollah Beheshti · 2022
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Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model
Eliane Röösli, Selen Bozkurt, and Tina Hernandez-Boussard · 2022
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Galactica: A large language model for science, 2022
Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic · 2022
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A brief review on algorithmic fairness
Xiaomeng Wang, Yishi Zhang, and Ruilin Zhu · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou · 2022
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Considerations for addressing bias in artificial intelligence for health equity
Michael D Abràmoff, Michelle E Tarver, Nilsa Loyo-Berrios, Sylvia Trujillo, Danton Char, Ziad Obermeyer, Malvina B Eydelman, Foundational Principles of Ophthalmic Imaging, DC Algorithmic Interpretation Working Group of the Collaborative Community for Ophthalmic Imaging Foundation, Washington, and William H Maisel · 2023
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Cited alongside, same era.
Conversational ai models for ophthalmic diagnosis: Comparison of chatgpt and the isabel pro differential diagnosis generator
Michael Balas and Edsel B Ing · 2023
Cited alongside, same era.
Leveraging Large Language Models for Decision Support in Personalized Oncology
Manuela Benary, Xing David Wang, Max Schmidt, Dominik Soll, Georg Hilfenhaus, Mani Nassir, Christian Sigler, Maren Knödler, Ulrich Keller, Dieter Beule, Ulrich Keilholz, Ulf Leser, and Damian T. Rieke · 2023
Cited alongside, same era.
Survey of explainable ai techniques in healthcare
Ahmad Chaddad, Jihao Peng, Jian Xu, and Ahmed Bouridane · 2023
Cited alongside, same era.
Meditron-70b: Scaling medical pretraining for large language models
Using chain-of-thought prompting for interpretable recognition of social bias
Jacob-Junqi Tian, Omkar Dige, D Emerson, and Faiza Khattak · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Towards generalist biomedical ai, 2023
Tao Tu, Shekoofeh Azizi, Danny Driess, Mike Schaekermann, Mohamed Amin, Pi-Chuan Chang, Andrew Carroll, Chuck Lau, Ryutaro Tanno, Ira Ktena, Basil Mustafa, Aakanksha Chowdhery, Yun Liu, Simon Kornblith, David Fleet, Philip Mansfield, Sushant Prakash, Renee Wong, Sunny Virmani, Christopher Semturs, S Sara Mahdavi, Bradley Green, Ewa Dominowska, Blaise Aguera y Arcas, Joelle Barral, Dale Webster, Greg S. Corrado, Yossi Matias, Karan Singhal, Pete Florence, Alan Karthikesalingam, and Vivek Natarajan · 2023
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Clinical text summarization: Adapting large language models can outperform human experts
Dave Van Veen, Cara Van Uden, Louis Blankemeier, Jean-Benoit Delbrouck, Asad Aali, Christian Bluethgen, Anuj Pareek, Malgorzata Polacin, Eduardo Pontes Reis, Anna Seehofnerova, et al · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Zeming Chen, Alejandro Hernández Cano, Angelika Romanou, Antoine Bonnet, Kyle Matoba, Francesco Salvi, Matteo Pagliardini, Simin Fan, Andreas Köpf, Amirkeivan Mohtashami, et al · 2023
Cited alongside, same era.
Reducing the carbon impact of generative ai inference (today and in 2035)
Andrew A Chien, Liuzixuan Lin, Hai Nguyen, Varsha Rao, Tristan Sharma, and Rajini Wijayawardana · 2023
Cited alongside, same era.
The future landscape of large language models in medicine
Jan Clusmann, Fiona R Kolbinger, Hannah Sophie Muti, Zunamys I Carrero, Jan-Niklas Eckardt, Narmin Ghaffari Laleh, Chiara Maria Lavinia Löffler, Sophie-Caroline Schwarzkopf, Michaela Unger, Gregory P Veldhuizen, et al · 2023
Cited alongside, same era.
Bias and fairness in large language models: A survey, 2023
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K. Ahmed · 2023
Cited alongside, same era.
Medalpaca–an open-source collection of medical conversational ai models and training data
Tianyu Han, Lisa C Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, and Keno K Bressem · 2023
Cited alongside, same era.
The accuracy and potential racial and ethnic biases of gpt-4 in the diagnosis and triage of health conditions: Evaluation study
Naoki Ito, Sakina Kadomatsu, Mineto Fujisawa, Kiyomitsu Fukaguchi, Ryo Ishizawa, Naoki Kanda, Daisuke Kasugai, Mikio Nakajima, Tadahiro Goto, and Yusuke Tsugawa · 2023
Cited alongside, same era.
Evaluating the performance of large language models: Chatgpt and google bard in generating differential diagnoses in clinicopathological conferences of neurodegenerative disorders
Shunsuke Koga, Nicholas B Martin, and Dennis W Dickson · 2023
Cited alongside, same era.
Chatdoctor: A medical chat model fine-tuned on a large language model meta-ai (llama) using medical domain knowledge
Yunxiang Li, Zihan Li, Kai Zhang, Ruilong Dan, Steve Jiang, and You Zhang · 2023
Cited alongside, same era.
Are large language models ready for healthcare? a comparative study on clinical language understanding, 2023
Yuqing Wang, Yun Zhao, and Linda Petzold · 2023
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Pmc-llama: Towards building open-source language models for medicine
Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie · 2023
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A survey of large language models, 2023
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen · 2023
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Beyond efficiency: A systematic survey of resource-efficient large language models
Guangji Bai, Zheng Chai, Chen Ling, Shiyu Wang, Jiaying Lu, Nan Zhang, Tingwei Shi, Ziyang Yu, Mengdan Zhu, Yifei Zhang, et al · 2024
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Efficient prompting methods for large language models: A survey, 2024
Kaiyan Chang, Songcheng Xu, Chenglong Wang, Yingfeng Luo, Tong Xiao, and Jingbo Zhu · 2024
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Gemini: A family of highly capable multimodal models, 2024
Gemini Team · 2024
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Gemma: Open models based on gemini research and technology, 2024
Gemma Team · 2024
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Sociodemographic bias in language models: A survey and forward path, 2024
Vipul Gupta, Pranav Narayanan Venkit, Shomir Wilson, and Rebecca J. Passonneau · 2024
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Mixtral of experts, 2024
Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2024
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A survey on fairness in large language models, 2024
Yingji Li, Mengnan Du, Rui Song, Xin Wang, and Ying Wang · 2024
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A toolbox for surfacing health equity harms and biases in large language models, 2024
Stephen R. Pfohl, Heather Cole-Lewis, Rory Sayres, Darlene Neal, Mercy Asiedu, Awa Dieng, Nenad Tomasev, Qazi Mamunur Rashid, Shekoofeh Azizi, Negar Rostamzadeh, Liam G. McCoy, Leo Anthony Celi, Yun Liu, Mike Schaekermann, Alanna Walton, Alicia Parrish, Chirag Nagpal, Preeti Singh, Akeiylah Dewitt, Philip Mansfield, Sushant Prakash, Katherine Heller, Alan Karthikesalingam, Christopher Semturs, Joelle Barral, Greg Corrado, Yossi Matias, Jamila Smith-Loud, Ivor Horn, and Karan Singhal · 2024
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Graph transformers on EHRs: Better representation improves downstream performance
Raphael Poulain and Rahmatollah Beheshti · 2024
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Gpt-4 — Wikipedia, the free encyclopedia, 2024
Wikipedia contributors · 2024
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Assessing the potential of gpt-4 to perpetuate racial and gender biases in health care: a model evaluation study
Travis Zack, Eric Lehman, Mirac Suzgun, Jorge A Rodriguez, Leo Anthony Celi, Judy Gichoya, Dan Jurafsky, Peter Szolovits, David W Bates, Raja-Elie E Abdulnour, et al · 2024
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A clarification of the nuances in the fairness metrics landscape
Alessandro Castelnovo, Riccardo Crupi, Greta Greco, Daniele Regoli, Ilaria Giuseppina Penco, and Andrea Claudio Cosentini · 2045
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The promise of explainable ai in digital health for precision medicine: A systematic review
Ben Allen · 2075
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