Join the “Artificial Intelligence (AI) in Health” Journal Club to stay up to date on the latest in artificial intelligence research and its applications for biomedicine and health. The monthly meetings encourage active discussions about the application and impact of AI in healthcare with the goal of inspiring collaborative research.
The group meets online at noon on the third Tuesday of each month. All interested in AI are welcome, including those from other institutions and the community.
Click to register for upcoming AI in Health Journal Club meetings.
For more information please contact Assistant Professor of Biomedical Informatics Jonathan Bona, Ph.D., at JPBona@uams.edu.
Schedule
| Date | Topic |
|---|---|
| December 17, 2024 | Integrating Foundation Models with Domain Knowledge and Clinical Context Discussion lead by Fred Prior, Ph.D., Distinguished Professor and Chair, Department of Biomedical Informatics Paper: Wilson, P.F., To, M.N.N., Jamzad, A., Gilany, M., Harmanani, M., Elghareb, T., Fooladgar, F., Wodlinger, B., Abolmaesumi, P. and Mousavi, P., 2024, October. ProstNFound: Integrating Foundation Models with Ultrasound Domain Knowledge and Clinical Context for Robust Prostate Cancer Detection. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 499-509). Cham: Springer Nature Switzerland. https://link.springer.com/chapter/10.1007/978-3-031-72089-5_47 |
| November 19, 2024 | Panel and Debate: Recurrent Neural Networks vs Transformers Presentations and discussion by: – Fred Prior, Ph.D., Distinguished Professor and Chair, Department of Biomedical Informatics – Aaron Kemp, M.B.A, Instructor and Ph.D. Candidate, Department of Biomedical Informatics – Md. Enamul Hoq, M.S., Ph.D. Candidate, Department of Biomedical Informatics Readings: Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17). Curran Associates Inc., Red Hook, NY, USA, 6000–6010. https://arxiv.org/abs/1706.03762 Feng, L., Tung, F., Ahmed, M.O., Bengio, Y. and Hajimirsadegh, H., 2024. Were RNNs All We Needed?. arXiv preprint arXiv:2410.01201. https://arxiv.org/abs/2410.01201 |
| October 15, 2024 | AI Equity Framework Discussion lead by Daniel Liu, M.D., M.A. is a Clinical Informaticist/Pediatric Hospitalist at ACH and Assistant Professor of Pediatrics and Biomedical Informatics at UAMS. Paper: Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study Schaekermann, Mike et al. eClinicalMedicine, Volume 70, 102479 https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(24)00058-0/fulltext |
| January 16, 2024 | Deep learning segmentation of CT images Discussion lead by Christopher Wardell, Ph.D., Assistant Professor UAMS Biomedical Informatics. Paper: Wasserthal J, Breit HC, Meyer MT, Pradella M, Hinck D, Sauter AW, Heye T, Boll DT, Cyriac J, Yang S, Bach M. Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images. Radiology: Artificial Intelligence. 2023 Sep;5(5). https://pubs.rsna.org/doi/10.1148/ryai.230024 Preprint: https://arxiv.org/abs/2208.05868 |
| November 21, 2023 | Humans inherit artificial intelligence biases. Discussion lead by Daniel Liu, M.D., M.A., Clinical Informaticist/Pediatric Hospitalist at ACH and Assistant Professor of Pediatrics at UAMS. Paper: Vicente, L., Matute, H. Humans inherit artificial intelligence biases. Sci Rep 13, 15737 (2023). https://doi.org/10.1038/s41598-023-42384-8 |
| October 17, 2023 | Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Discussion lead by Fred Prior, Ph.D., Distinguished Professor and Chair, Department of Biomedical Informatics Paper: Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, Brogi E, Reuter VE, Klimstra DS, Fuchs TJ. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature medicine. 2019 Aug;25(8):1301-9. |
| August 15, 2023 | Machine Learning–Assisted Recurrence Prediction for Patients With Early-Stage Non–Small-Cell Lung Cancer Discussion lead by Jim Chen, MD, Hematology/Oncology Fellow PGY6, UAMS. Paper: Janik A, Torrente M, Costabello L, Calvo V, Walsh B, Camps C, Mohamed SK, Ortega AL, Nováček V, Massutí B, Minervini P. Machine Learning–Assisted Recurrence Prediction for Patients With Early-Stage Non–Small-Cell Lung Cancer. JCO Clinical Cancer Informatics. 2023 Jul;7:e2200062. https://ascopubs.org/doi/full/10.1200/CCI.22.00062 |
| July 18, 2023 | Natural language processing of radiology requests and reports of chest imaging Discussion lead by Michael Rutherford, M.S., Ph.D. Candidate, Instructor UAMS Biomedical Informatics. Paper: Olthof AW, van Ooijen PM, Cornelissen LJ. The natural language processing of radiology requests and reports of chest imaging: Comparing five transformer models’ multilabel classification and a proof-of-concept study. Health Informatics Journal. 2022 Oct 10;28(4):14604582221131198. https://journals.sagepub.com/doi/pdf/10.1177/14604582221131198 |
| May | GPT and Large Generative Language Models for AI in Health Discussion lead by Daniel Liu, M.D., M.A., Clinical Informaticist/Pediatric Hospitalist, ACH Assistant Professor of Pediatrics, UAMS Paper: Lee P, Bubeck S, Petro J. Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine. New England Journal of Medicine. 2023 Mar 30;388(13):1233-9. https://www.nejm.org/doi/10.1056/NEJMsr2214184 Supplementary reading: Gozalo-Brizuela R, Garrido-Merchan EC. ChatGPT is not all you need. A State of the Art Review of large Generative AI models. arXiv preprint arXiv:2301.04655. 2023 Jan 11. https://arxiv.org/abs/2301.04655 |
| April 18, 2023 | Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence Discussion lead by Azriel Stinson, DO, Clinical Informatics Fellow, UAMS & ACH Paper: Liang, H., Tsui, B.Y., Ni, H. et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med 25, 433–438 (2019). https://doi.org/10.1038/s41591-018-0335-9 |
| March 21, 2023 | medigan: a Python library of pretrained generative models for medical image synthesis Discussion lead by Fred Prior, Ph.D., Distinguished Professor and Chair, Department of Biomedical Informatics Paper: Richard Osuala, Grzegorz Skorupko, Noussair Lazrak, Lidia Garrucho, Eloy García, Smriti Joshi, Socayna Jouide, Michael Rutherford, Fred Prior, Kaisar Kushibar, Oliver Díaz, Karim Lekadir, “medigan: a Python library of pretrained generative models for medical image synthesis,” J. Med. Imag. 10(6) 061403 (20 February 2023) https://doi.org/10.1117/1.JMI.10.6.061403 |
| February 21, 2023 | Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis Discussion lead by Salem AlGhamdi, M.D., Clinical Informatics Fellow. Paper: Adler-Milstein J, Aggarwal N, Ahmed M, Castner J, Evans BJ, Gonzalez AA, James CA, Lin S, Mandl KD, Matheny ME, Sendak MP, Shachar C, Williams A. Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis. NAM Perspect. 2022 Sep 29;2022:10.31478/202209c. doi: 10.31478/202209c. PMID: 36713769; PMCID: PMC9875857. |
| January 17, 2023 | Explainable Artificial Intelligence Models Using Real-world Electronic Health Record Data. Discussion lead by Obeid Shafi, M.D., Clinical Informatics Fellow. Paper: Seyedeh Neelufar Payrovnaziri, Zhaoyi Chen, Pablo Rengifo-Moreno, Tim Miller, Jiang Bian, Jonathan H Chen, Xiuwen Liu, Zhe He, Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review, Journal of the American Medical Informatics Association, Volume 27, Issue 7, July 2020, Pages 1173–1185, https://doi.org/10.1093/jamia/ocaa053 |
| November 15, 2022 | Biomedical Ontologies to Guide AI Development in Radiology. Discussion lead by Mathias Brochhausen, Ph.D., Professor of Biomedical Informatics,. Paper: Filice RW, Kahn CE Jr. Biomedical Ontologies to Guide AI Development in Radiology. J Digit Imaging. 2021 Dec;34(6):1331-1341. doi: 10.1007/s10278-021-00527-1. Epub 2021 Nov 1. Erratum in: J Digit Imaging. 2022 Oct;35(5):1419. PMID: 34724143; PMCID: PMC8669056. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8669056/ |
| October 20, 2022 | Plasma proteomic signature predicts who will get persistent symptoms following SARS-CoV-2 infection. Discussion lead by Brian Delavan, M.P.H. Paper: Captur G, Moon JC, Topriceanu CC, Joy G, Swadling L, Hallqvist J, Doykov I, Patel N, Spiewak J, Baldwin T, Hamblin M. Plasma proteomic signature predicts who will get persistent symptoms following SARS-CoV-2 infection. eBioMedicine. 2022 Sep 28:104293. https://doi.org/10.1016/j.ebiom.2022.104293 |
| September 20, 2022 | Artificial Intelligence in Health and Medicine. Discussion lead by Jonathan Bona, Ph.D., Assistant Professor of Biomedical Informatics. Paper: Rajpurkar, P., Chen, E., Banerjee, O. et al. AI in health and medicine. Nat Med 28, 31–38 (2022). https://doi.org/10.1038/s41591-021-01614-0 |