Fetching the paper…
Reading the bibliography…
The global mental health crisis is looming with a rapid increase in mental disorders, limited resources, and the social stigma of seeking treatment.
doi:10.1126/science.217.4566.1237
G. Kolata, How can computers get common sense?, Science 217 (4566) (1982) 1237–1238 · 1982
Earlier work this paper cites.
D. Crevier, AI, 2nd Edition, Basic Books, New York, NY, 1995
1995
Earlier work this paper cites.
doi:10.1162/neco.1997.9.8.1735
S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation 9 (8) (1997) 1735–1780 · 1997
Earlier work this paper cites.
doi:10.1136/amiajnl-2011-000089
K. Goddard, A. Roudsari, J. C. Wyatt, Automation bias: a systematic review of frequency, effect mediators, and mitigators, Journal of the American Medical Informatics Association 19 (1) (Goddard2012) 121–127 · 2011
Earlier work this paper cites.
Department of Health and Human Services, Modifications to the hipaa privacy, security, enforcement, and breach notification rules under the health information technology for economic and clinical health act and the genetic information nondiscrimination act; other modifications to the hipaa rules; final rule, 78, fed. reg. 5566 (jan. 25, 2013) (to be codified at 45 c.f.r. pts. 160 and 164). , Federal RegisterHttps://www.govinfo.gov/content/pkg/FR-2013-01-25/pdf/2013-01073.pdf (2013). URL https://www.govinfo.gov/content/pkg/FR-2013-01-25/pdf/2013-01073.pdf
2013
Earlier work this paper cites.
doi:10.1176/appi.ajp.2014.14030423
V. M. Castro, J. Minnier, S. N. Murphy, I. Kohane, S. E. Churchill, V. Gainer, T. Cai, A. G. Hoffnagle, Y. Dai, S. Block, S. R. Weill, M. Nadal-Vicens, A. R. Pollastri, J. N. Rosenquist, S. Goryachev, D. Ongur, P. Sklar, R. H. Perlis, J. W. Smoller, J. W. Smoller, R. H. Perlis, P. H. Lee, V. M. Castro, A. G. Hoffnagle, P. Sklar, E. A. Stahl, S. M. Purcell, D. M. Ruderfer, A. W. Charney, P. Roussos, C. Pato, M. Pato, H. Medeiros, J. Sobel, N. Craddock, I. Jones, L. Forty, A. DiFlorio, E. Green, L. Jones, K. Dunjewski, M. Landén, C. Hultman, A. Juréus, S. Bergen, O. Svantesson, S. McCarroll, J. Moran, J. W. Smoller, K. Chambert, R. A. B. and, Validation of electronic health record phenotyping of bipolar disorder cases and controls, American Journal of Psychiatry 172 (4) (Castro2015) 363–372 · 2014
Earlier work this paper cites.
European Parliament, Council of the European Union, Consolidated text: Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec (general data protection regulation) (text with eea relevance), Official Journal of the European Union (2016)
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need, Advances in neural information processing systems 30 (2017)
2017
Earlier work this paper cites.
doi:10.1126/science.aal4230
A. Caliskan, J. J. Bryson, A. Narayanan, Semantics derived automatically from language corpora contain human-like biases, Science 356 (6334) (2017) 183–186 · 2017
Earlier work this paper cites.
N. Kilbertus, M. Rojas-Carulla, G. Parascandolo, M. Hardt, D. Janzing, B. Schölkopf, Avoiding discrimination through causal reasoning, Advances in Neural Information Processing Systems 30, 2017, p. 656–666 (Jun. Kilbertus2017) · 2017
Earlier work this paper cites.
doi:10.18653/v1/d17-1323
J. Zhao, T. Wang, M. Yatskar, V. Ordonez, K.-W. Chang, Men also like shopping: Reducing gender bias amplification using corpus-level constraints, in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2017 · 2017
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding (Oct. 2018) · 2018
Earlier work this paper cites.
S. Hanneke, A. T. Kalai, G. Kamath, C. Tzamos, Actively avoiding nonsense in generative models , in: S. Bubeck, V. Perchet, P. Rigollet (Eds.), Proceedings of the 31st Conference On Learning Theory, Vol. 75 of Proceedings of Machine Learning Research, PMLR, 2018, pp. 209–227. URL https://proceedings.mlr.press/v75/hanneke18a.html
2018
Earlier work this paper cites.
doi:10.1109/ichi.2018.00039
T. Roy, J. McClendon, L. Hodges, Analyzing abusive text messages to detect digital dating abuse, in: 2018 IEEE International Conference on Healthcare Informatics (ICHI), IEEE, 2018 · 2018
Earlier work this paper cites.
doi:10.18653/v1/n19-1423
J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, Bert: Pre-training of deep bidirectionaltransformers for language understanding, in: Proceedings of the 2019 Conference of the North American Chapterof the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Association for Computational Linguistics, 2019 · 2019
Earlier work this paper cites.
M. Whittaker, M. Alper, C. Bennett, S. Hendren, O. College, L. Kaziunas, M. Mills, M. R. Morris, J. Rankin, E. Rogers, M. Salas, S. M. West, Disability, bias, and ai., Tech. rep., AI Now Institute (2019)
2019
Earlier work this paper cites.
doi:10.1145/3287560.3287596
M. Mitchell, S. Wu, A. Zaldivar, P. Barnes, L. Vasserman, B. Hutchinson, E. Spitzer, I. D. Raji, T. Gebru, Model cards for model reporting, in: Proceedings of the Conference on Fairness, Accountability, and Transparency, ACM, 2019 · 2019
Earlier work this paper cites.
doi:10.18653/v1/P19-3007
J. Vig, A multiscale visualization of attention in the transformer model , in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Association for Computational Linguistics, Florence, Italy, 2019, pp. 37–42 · 2019
Earlier work this paper cites.
doi:10.15252/embr.201949177
H. Shevlin, K. Vold, M. Crosby, M. Halina, The limits of machine intelligence, EMBO reports 20 (10) (sep 2019) · 2019
Earlier work this paper cites.
doi:10.1016/s0140-6736(21)02143-7
D. F. Santomauro, A. M. M. Herrera, J. Shadid, P. Zheng, C. Ashbaugh, D. M. Pigott, C. Abbafati, C. Adolph, J. O. Amlag, A. Y. Aravkin, B. L. Bang-Jensen, G. J. Bertolacci, S. S. Bloom, R. Castellano, E. Castro, S. Chakrabarti, J. Chattopadhyay, R. M. Cogen, J. K. Collins, X. Dai, W. J. Dangel, C. Dapper, A. Deen, M. Erickson, S. B. Ewald, A. D. Flaxman, J. J. Frostad, N. Fullman, J. R. Giles, A. Z. Giref, G. Guo, J. He, M. Helak, E. N. Hulland, B. Idrisov, A. Lindstrom, E. Linebarger, P. A. Lotufo, R. Lozano, B. Magistro, D. C. Malta, J. C. Månsson, F. Marinho, A. H. Mokdad, L. Monasta, P. Naik, S. Nomura, J. K. O'Halloran, S. M. Ostroff, M. Pasovic, L. Penberthy, R. C. R. Jr, G. Reinke, A. L. P. Ribeiro, A. Sholokhov, R. J. D. Sorensen, E. Varavikova, A. T. Vo, R. Walcott, S. Watson, C. S. Wiysonge, B. Zigler, S. I. Hay, T. Vos, C. J. L. Murray, H. A. Whiteford, A. J. Ferrari, Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic, The Lancet 398 (10312) (Santomauro2021) 1700–1712 · 2020
Earlier work this paper cites.
doi:10.18653/v1/2020.acl-main.703
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, L. Zettlemoyer, BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension , in: D. Jurafsky, J. Chai, N. Schluter, J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Online, 2020, pp. 7871–7880 · 2020
Earlier work this paper cites.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, D. Amodei, Language models are few-shot learners, in: Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS’20, Curran Associates Inc., Red Hook, NY, USA, 2020
2020
Earlier work this paper cites.
doi:10.18653/v1/2020.acl-main.463
E. M. Bender, A. Koller, Climbing towards NLU: On meaning, form, and understanding in the age of data, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2020 · 2020
Cited alongside, same era.
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, et al., Retrieval-augmented generation for knowledge-intensive nlp tasks, Advances in Neural Information Processing Systems 33 (2020) 9459–9474
2020
Cited alongside, same era.
doi:10.18653/v1/2020.acl-main.442
M. T. Ribeiro, T. Wu, C. Guestrin, S. Singh, Beyond accuracy: Behavioral testing of NLP models with CheckList, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2020 · 2020
Cited alongside, same era.
doi:10.1055/s-0040-1701980
S.-T. Liaw, H. Liyanage, C. Kuziemsky, A. L. Terry, R. Schreiber, J. Jonnagaddala, S. de Lusignan, Ethical use of electronic health record data and artificial intelligence: Recommendations of the primary care informatics working group of the international medical informatics association, Yearbook of Medical Informatics 29 (01) (2020) 051–057 · 2020
Cited alongside, same era.
doi:10.3389/fmed.2022.990604
M. Chen, B. Zhang, Z. Cai, S. Seery, M. J. Gonzalez, N. M. Ali, R. Ren, Y. Qiao, P. Xue, Y. Jiang, Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey, Frontiers in Medicine 9 (aug Chen2022) · 2022
Later among the works it cites.
OpenAI, Gpt-4 technical report (Mar. 2023) · 2023
Closest in time.
doi:10.1109/bigcomp57234.2023.00097
L. Brocki, G. C. Dyer, A. Gładka, N. C. Chung, Deep learning mental health dialogue system, in: 2023 IEEE International Conference on Big Data and Smart Computing (BigComp), IEEE, 2023 · 2023
Closest in time.
J. M. Liu, D. Li, H. Cao, T. Ren, Z. Liao, J. Wu, Chatcounselor: A large language models for mental health support (Sep. 2023) · 2023
Closest in time.
T. Lai, Y. Shi, Z. Du, J. Wu, K. Fu, Y. Dou, Z. Wang, Psy-llm: Scaling up global mental health psychological services with ai-based large language models (Jul. 2023) · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
doi:10.18653/v1/2020.findings-emnlp.301
S. Gehman, S. Gururangan, M. Sap, Y. Choi, N. A. Smith, RealToxicityPrompts: Evaluating neural toxic degeneration in language models, in: Findings of the Association for Computational Linguistics: EMNLP 2020, Association for Computational Linguistics, 2020 · 2020
Cited alongside, same era.
doi:10.2471/blt.20.273383
S. Chen, R. Cardinal, Accessibility and efficiency of mental health services, united kingdom of great britain and northern ireland, Bulletin of the World Health Organization 99 (09) (2021) 674–679 · 2021
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, I. Sutskever, Learning transferable visual models from natural language supervision (Feb. 2021) · 2021
Cited alongside, same era.
doi:10.2196/15708
A. L. Glaz, Y. Haralambous, D.-H. Kim-Dufor, P. Lenca, R. Billot, T. C. Ryan, J. Marsh, J. DeVylder, M. Walter, S. Berrouiguet, C. Lemey, Machine learning and natural language processing in mental health: Systematic review, Journal of Medical Internet Research 23 (5) (2021) e15708 · 2021
Cited alongside, same era.
L. Gao, S. Biderman, S. Black, L. Golding, T. Hoppe, C. Foster, J. Phang, H. He, A. Thite, N. Nabeshima, S. Presser, C. Leahy, The pile: An 800gb dataset of diverse text for language modeling (Dec. 2021) · 2021
Cited alongside, same era.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, A. Joulin, Emerging properties in self-supervised vision transformers, in: Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 9650–9660
2021
Cited alongside, same era.
doi:10.1016/j.beth.2021.09.007
S. M. Lim, C. W. C. Shiau, L. J. Cheng, Y. Lau, Chatbot-delivered psychotherapy for adults with depressive and anxiety symptoms: A systematic review and meta-regression, Behavior Therapy 53 (2) (2022) 334–347 · 2021
Cited alongside, same era.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen, Lora: Low-rank adaptation of large language models (Jun. 2021) · 2021
Cited alongside, same era.
Closest in time.
Common Crawl, https://commoncrawl.org/ (Nov. 2023)
2023
Closest in time.
doi:10.1073/pnas.2313790120
A. Acerbi, J. M. Stubbersfield, Large language models show human-like content biases in transmission chain experiments, Proceedings of the National Academy of Sciences 120 (44) (Oct. 2023) · 2023
Closest in time.
R. BRODERICK, People are using ai for therapy, whether the tech is ready for it or not , Tech. rep., Fast Company (2023). URL https://www.fastcompany.com/90836906/ai-therapy-koko-chatgpt
2023
Closest in time.
J. Zhou, M. Hu, J. Li, X. Zhang, X. Wu, I. King, H. Meng, Rethinking machine ethics – can llms perform moral reasoning through the lens of moral theories? (Aug. 2023) · 2023
Closest in time.
doi:10.1145/3571730
Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, P. Fung, Survey of hallucination in natural language generation, ACM Computing Surveys 55 (12) (2023) 1–38 · 2023
Closest in time.
doi:10.1101/2023.03.16.23287316
S. Chen, B. H. Kann, M. B. Foote, H. J. Aerts, G. K. Savova, R. H. Mak, D. S. Bitterman, The utility of ChatGPT for cancer treatment information (mar 2023) · 2023
Closest in time.
doi:10.3350/cmh.2023.0089
Y. H. Yeo, J. S. Samaan, W. H. Ng, P.-S. Ting, H. Trivedi, A. Vipani, W. Ayoub, J. D. Yang, O. Liran, B. Spiegel, A. Kuo, Assessing the performance of ChatGPT in answering questions regarding cirrhosis and hepatocellular carcinoma, Clinical and Molecular Hepatology 29 (3) (2023) 721–732 · 2023
Closest in time.
doi:10.3389/frai.2023.1229805
S. Sarkar, M. Gaur, L. K. Chen, M. Garg, B. Srivastava, A review of the explainability and safety of conversational agents for mental health to identify avenues for improvement, Frontiers in Artificial Intelligence 6 (oct Sarkar2023) · 2023
Closest in time.
L. Brocki, N. C. Chung, Class-discriminative attention maps for vision transformers, Arxiv (2023)
2023
Closest in time.
Y. Sheng, S. Cao, D. Li, C. Hooper, N. Lee, S. Yang, C. Chou, B. Zhu, L. Zheng, K. Keutzer, J. E. Gonzalez, I. Stoica, S-lora: Serving thousands of concurrent lora adapters (Nov. 2023) · 2023
Closest in time.
L. Chen, Z. Ye, Y. Wu, D. Zhuo, L. Ceze, A. Krishnamurthy, Punica: Multi-tenant lora serving (Oct. 2023) · 2023
Closest in time.
S. Pichai, An important next step on our ai journey , Tech. rep., Google (Feb. 2023). URL https://blog.google/technology/ai/bard-google-ai-search-updates/
2023
Closest in time.
doi:10.1007/s43681-023-00289-2
J. Mökander, J. Schuett, H. R. Kirk, L. Floridi, Auditing large language models: a three-layered approach, AI and Ethics (may 2023) · 2023
Closest in time.
doi:10.1038/s41586-023-06291-2
K. Singhal, S. Azizi, T. Tu, S. S. Mahdavi, J. Wei, H. W. Chung, N. Scales, A. Tanwani, H. Cole-Lewis, S. Pfohl, P. Payne, M. Seneviratne, P. Gamble, C. Kelly, A. Babiker, N. Schärli, A. Chowdhery, P. Mansfield, D. Demner-Fushman, B. A. y Arcas, D. Webster, G. S. Corrado, Y. Matias, K. Chou, J. Gottweis, N. Tomasev, Y. Liu, A. Rajkomar, J. Barral, C. Semturs, A. Karthikesalingam, V. Natarajan, Large language models encode clinical knowledge, Nature 620 (7972) (2023) 172–180 · 2023
Closest in time.
doi:10.2217/fmai-2023-0011
M. Soni, P. Anjaria, P. Koringa, The revolutionary impact of chatGPT: advances in biomedicine and redefining healthcare with large language models, Future Medicine AI (aug Soni2023) · 2023
Closest in time.
doi:10.1001/jamainternmed.2023.1838
J. W. Ayers, A. Poliak, M. Dredze, E. C. Leas, Z. Zhu, J. B. Kelley, D. J. Faix, A. M. Goodman, C. A. Longhurst, M. Hogarth, D. M. Smith, Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum, JAMA Internal Medicine 183 (6) (Ayers2023) 589 · 2023
Closest in time.