LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models

M. Khayyat and A. Awasthi, “An intelligent multi-agent based model for collaborative logistics systems,” Transportation research procedia , vol. 12, pp. 325–338, 2016

2016

Earlier work this paper cites.

D. Isern and A. Moreno, “A systematic literature review of agents applied in healthcare,” Journal of medical systems , vol. 40, pp. 1–14, 2016

2016

Earlier work this paper cites.

C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of machine learning research , vol. 21, no. 140, pp. 1–67, 2020

2020

Earlier work this paper cites.

T. B. Brown, “Language models are few-shot learners,” arXiv preprint arXiv:2005.14165 , 2020

Original

2020

Earlier work this paper cites.

D. Jin, E. Pan, N. Oufattole, W.-H. Weng, H. Fang, and P. Szolovits, “What disease does this patient have? a large-scale open domain question answering dataset from medical exams,” Applied Sciences , vol. 11, no. 14, 2021

2021

Earlier work this paper cites.

X. Han, Z. Zhang, N. Ding, Y. Gu, X. Liu, Y. Huo, J. Qiu, Y. Yao, A. Zhang, L. Zhang et al. , “Pre-trained models: Past, present and future,” AI Open , vol. 2, pp. 225–250, 2021

2021

Earlier work this paper cites.

X. Yu, J. Wang, Q.-Q. Hong, R. Teku, S.-H. Wang, and Y.-D. Zhang, “Transfer learning for medical images analyses: A survey,” Neurocomputing , vol. 489, pp. 230–254, 2022

2022

Earlier work this paper cites.

G. Erion, J. D. Janizek, C. Hudelson, R. B. Utarnachitt, A. M. McCoy, M. R. Sayre, N. J. White, and S.-I. Lee, “A cost-aware framework for the development of ai models for healthcare applications,” Nature Biomedical Engineering , vol. 6, no. 12, pp. 1384–1398, 2022

2022

Earlier work this paper cites.

M. Bhanu Sridhar, “Applications of multi-agent systems in intelligent health care,” in Multi Agent Systems: Technologies and Applications towards Human-Centered . Springer, 2022, pp. 173–195

2022

Earlier work this paper cites.

J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022

2022

Earlier work this paper cites.

X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, S. Narang, A. Chowdhery, and D. Zhou, “Self-consistency improves chain of thought reasoning in language models,” arXiv preprint arXiv:2203.11171 , 2022

Original

2022

Earlier work this paper cites.

J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al. , “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023

Original

2023

Earlier work this paper cites.

H. Zhou, F. Liu, B. Gu, X. Zou, J. Huang, J. Wu, Y. Li, S. S. Chen, P. Zhou, J. Liu et al. , “A survey of large language models in medicine: Progress, application, and challenge,” arXiv preprint arXiv:2311.05112 , 2023

Original

2023

Earlier work this paper cites.

M. Karabacak and K. Margetis, “Embracing large language models for medical applications: opportunities and challenges,” Cureus , vol. 15, no. 5, 2023

2023

Earlier work this paper cites.

A. Haque and M. N.-U.-R. Chowdhury, “The future of medicine: large language models redefining healthcare dynamics,” Authorea Preprints , 2023

2023

Cited alongside, same era.

J. Clusmann, F. R. Kolbinger, H. S. Muti, Z. I. Carrero, J.-N. Eckardt, N. G. Laleh, C. M. L. Löffler, S.-C. Schwarzkopf, M. Unger, G. P. Veldhuizen et al. , “The future landscape of large language models in medicine,” Communications medicine , vol. 3, no. 1, p. 141, 2023

2023

Cited alongside, same era.

K. J. Prabhod, “Integrating large language models for enhanced clinical decision support systems in modern healthcare,” Journal of Machine Learning for Healthcare Decision Support , vol. 3, no. 1, pp. 18–62, 2023

2023

Cited alongside, same era.

S. Nerella, S. Bandyopadhyay, J. Zhang, M. Contreras, S. Siegel, A. Bumin, B. Silva, J. Sena, B. Shickel, A. Bihorac et al. , “Transformers in healthcare: A survey,” arXiv preprint arXiv:2307.00067 , 2023

Original

2023

Cited alongside, same era.

X. Wang and H. Zhu, “Artificial intelligence in image-based cardiovascular disease analysis: A comprehensive survey and future outlook,” arXiv preprint arXiv:2402.03394 , 2024

Original

2024

Closest in time.

Z. A. Nazi and W. Peng, “Large language models in healthcare and medical domain: A review,” in Informatics , vol. 11, no. 3. MDPI, 2024, p. 57

2024

Closest in time.

M. Geantă, D. Bădescu, N. Chirca, O. C. Nechita, C. G. Radu, S. Rascu, D. Rădăvoi, C. Sima, C. Toma, and V. Jinga, “The potential impact of large language models on doctor–patient communication: A case study in prostate cancer,” in Healthcare , vol. 12, no. 15. MDPI, 2024, p. 1548

2024

Closest in time.

D. Menzies, S. Kirwan, and A. Albarqawi, “Ai managed emergency documentation with a pretrained model,” arXiv preprint arXiv:2408.09193 , 2024

Original

2024

Closest in time.