Fetching the paper…
Reading the bibliography…
The rapid advancements in large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI.
H. Goodglass, E. Kaplan, and B. Barresi, Boston Diagnostic Aphasia Examination: Short Form , 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins, 2001
2001
Earlier work this paper cites.
K. A. Van Orden, T. K. Witte, K. H. Gordon, T. W. Bender, and T. E. J. Joiner, “Suicidal desire and the capability for suicide: Tests of the interpersonal-psychological theory of suicidal behavior among adults,” Journal of Consulting and Clinical Psychology , vol. 76, no. 1, pp. 72–83, 2008. [Online]. Available: https://doi.org/10.1037/0022-006X.76.1.72
2008
Earlier work this paper cites.
K. A. Van Orden, T. K. Witte, K. C. Cukrowicz, S. R. Braithwaite, E. A. Selby, and T. E. Joiner Jr, “The interpersonal theory of suicide.” Psychological review , vol. 117, no. 2, p. 575, 2010. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3130348
2010
Earlier work this paper cites.
E. D. Klonsky and A. M. May, “The three-step theory (3st): A new theory of suicide rooted in the “ideation-to-action” framework,” International Journal of Cognitive Therapy , vol. 8, no. 2, pp. 114–129, 2015
2015
Earlier work this paper cites.
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei, “Deep reinforcement learning from human preferences,” in Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
R. Kotov, R. F. Krueger, D. Watson, T. M. Achenbach, R. R. Althoff, R. M. Bagby, T. A. Brown, W. T. Carpenter, A. Caspi, L. A. Clark et al. , “The hierarchical taxonomy of psychopathology (hitop): A dimensional alternative to traditional nosologies.” Journal of abnormal psychology , vol. 126, no. 4, p. 454, 2017
2017
Earlier work this paper cites.
R. C. O’Connor and O. J. Kirtley, “The integrated motivational–volitional model of suicidal behaviour,” Philosophical Transactions of the Royal Society B: Biological Sciences , vol. 373, no. 1754, p. 20170268, 2018
2018
Earlier work this paper cites.
I. Mehmood, S. Anwar, AneezaDilawar, IsmaZulfiqar, and R. M. Abbas, “Managing data diversity on the internet of medical things (iomt),” International Journal of Information Technology and Computer Science , 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:231661158
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” International Conference on Machine Learning , pp. 8821–8831, 2021
2021
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in Neural Information Processing Systems , vol. 35, pp. 27 730–27 744, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 10 684–10 695, 2022
2022
Earlier work this paper cites.
A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen, “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” International Conference on Machine Learning , pp. 16 784–16 804, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
A. Kiseleva and D. Kotzinos, “Transparency of ai in healthcare as a multilayered system of accountabilities: Between legal requirements and technical limitations,” Frontiers in Artificial Intelligence , vol. 5, 2022. [Online]. Available: https://www.frontiersin.org/articles/10.3389/frai.2022.879603/full
2022
Earlier work this paper cites.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
OpenAI et al., “GPT-4 Technical Report,” arXiv preprint arXiv:2303.08774 , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
“Exploring the interplay between ai and blockchain,” 2023. [Online]. Available: https://magnimindacademy.com/blog/exploring-the-interplay-between-ai-and-blockchain/
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. A. E. Houda, A. S. Hafid, L. Khoukhi, and B. Brik, “When collaborative federated learning meets blockchain to preserve privacy in healthcare,” IEEE Transactions on Network Science and Engineering , vol. 10, no. 5, pp. 2455–2465, 2023
2023
Later among the works it cites.
H. Mumtaz, M. H. Riaz, H. Wajid, M. Saqib, M. H. Zeeshan, S. E. Khan, Y. R. Chauhan, H. Sohail, and L. I. Vohra, “Current challenges and potential solutions to the use of digital health technologies in evidence generation: a narrative review,” Frontiers in Digital Health , vol. 5, p. 1203945, 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
R. Girdhar, A. El-Nouby, Z. Liu, M. Singh, K. V. Alwala, A. Joulin, and I. Misra, “Imagebind one embedding space to bind them all,” 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 15 180–15 190, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258564264
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
T. Brooks, B. Peebles, C. Holmes, W. DePue, Y. Guo, L. Jing, D. Schnurr, J. Taylor, T. Luhman, E. Luhman, C. Ng, R. Wang, and A. Ramesh, “Video generation models as world simulators,” 2024. [Online]. Available: https://openai.com/research/video-generation-models-as-world-simulators
2024
Closest in time.
B. Bauer, R. Norel, A. Leow, Z. Rached, B. Wen, and G. Cecchi, “Using large language models to understand suicidality in a social media-based taxonomy of mental health disorders,” JMIR Mental Health , 2024, accepted on 29 March 2024, preprint available at https://preprints.jmir.org/preprint/57234
2024
Closest in time.
H. Zhou, E. Chen, S. Wen, Y. Wang, and R. Norel, “Large language models as a tool for cognitive stimulation: Chatbot book clubs for seniors,” in IEEE International Conference on Digital Health (ICDH) , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
K. Kalinin, “Large language models in healthcare: Bridging technology and medicine,” Topflight Apps Blog , Mar 2024. [Online]. Available: https://topflightapps.com/ideas/large-language-models-in-healthcare/
2024
Closest in time.
T. L. D. Health, “Large language models: a new chapter in digital health,” The Lancet. Digital health , vol. 6, no. 1, p. e1, 2024
2024
Closest in time.
D. Ueda, T. Kakinuma, S. Fujita, K. Kamagata, Y. Fushimi, R. Ito, Y. Matsui, T. Nozaki, T. Nakaura, N. Fujima et al. , “Fairness of artificial intelligence in healthcare: review and recommendations,” Japanese Journal of Radiology , vol. 42, no. 1, pp. 3–15, 2024
2024
Closest in time.
Nixon Gwilt Law, “Your 2024 legal vision board: Trends and opportunities for digital health innovators,” Jan 2024. [Online]. Available: https://nixongwiltlaw.com/nlg-blog/2024/1/11/your-2024-legal-vision-board-trends-and-opportunities-for-digital-health-innovators
2024
Closest in time.
Y.-J. Park, A. Pillai, J. Deng, E. Guo, M. Gupta, M. Paget, and C. Naugler, “Assessing the research landscape and clinical utility of large language models: a scoping review,” BMC Medical Informatics and Decision Making , vol. 24, no. 1, p. 72, 2024
2024
Closest in time.
E. Ullah, A. Parwani, M. M. Baig, and R. Singh, “Challenges and barriers of using large language models (llm) such as chatgpt for diagnostic medicine with a focus on digital pathology – a recent scoping review,” Diagnostic Pathology , vol. 19, no. 43, 2024. [Online]. Available: https://diagnosticpathology.biomedcentral.com/articles/10.1186/s13000-024-01464-7
2024
Closest in time.