Recent Presentations w/ References

Johns Hopkins DOM Grand Rounds – 2/25/25

  • Barocas S, Hardt M, Narayanan A. Fairness and Machine Learning.; 2019.
  • Bhagwat AM, Ferryman KS, Gibbons JB. Mitigating algorithmic bias in opioid risk-score modeling to ensure equitable access to pain relief. Nat Med. 2023;29(4):769-770. doi:10.1038/s41591-023-02256-0
  • Chen F, Wang L, Hong J, Jiang J, Zhou L. Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models. J Am Med Inform Assoc. 2024;31(5):1172-1183. doi:10.1093/jamia/ocae060
  • Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378. doi:10.1136/bmj-2023-078378
  • Daneshjou R, Vodrahalli K, Novoa RA, et al. Disparities in dermatology AI performance on a diverse, curated clinical image set. Sci Adv. 2022;8(32):eabq6147. doi:10.1126/sciadv.abq6147
  • Ferryman K, Goldenberg AJ, Sabatello M. Moving to equity in the all of us research program. Am J Bioeth. 2024;24(3):115-117. doi:10.1080/15265161.2024.2307255
  • Ferryman K, Mackintosh M, Ghassemi M. Considering Biased Data as Informative Artifacts in AI-Assisted Health Care. N Engl J Med. 2023;389(9):833-838. doi:10.1056/NEJMra2214964
  • Gianfrancesco M, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 2018;178(11):1544-1547. doi:10.1001/jamainternmed.2018.3763
  • Hardt M, Price E, Srebro N. Equality of opportunity in supervised learning. In: ; 2016. https://papers.neurips.cc/paper/6374-equality-of-opportunity-in-supervised-learning.pdf
  • Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys. 2021;54(6):1-35. doi:10.1145/3457607
  • Nadarzynski T, Knights N, Husbands D, et al. Achieving health equity through conversational AI: A roadmap for design and implementation of inclusive chatbots in healthcare. PLOS Digit Health. 2024;3(5):e0000492. doi:10.1371/journal.pdig.0000492
  • Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453. doi:10.1126/science.aax2342
  • Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring Fairness in Machine Learning to Advance Health Equity. Ann Intern Med. 2018;169(12):866-872. doi:10.7326/M18-1990
  • Saxena NA, Huang K, DeFilippis E, Radanovic G, Parkes DC, Liu Y. How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. AIES ’19. Association for Computing Machinery; 2019:99-106. doi:10.1145/3306618.3314248
  • Strauss AT, Sidoti CN, Sung HC, et al. Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness: A qualitative study. Hepatol Commun. 2023;7(10). doi:10.1097/HC9.0000000000000239
  • AIM-AHEAD. AIM-AHEAD. Accessed February 24, 2025. https://www.aim-ahead.net/
  • Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations. Draft Guidance for Industry and Food and Drug Administration Staff. Food and Drug Administration; 2025.

Tulane DREAM Symposium – 12/6/24

  • Liver Transplant Evaluation Process Video: https://www.youtube.com/watch?v=ezBg1vX0Ozg&ab_channel=JohnsHopkinsMedicine
  • Patzer RE, Retzloff S, Buford J, et al. Community engagement to improve equity in kidney transplantation from the ground up: The southeastern kidney transplant coalition. Curr Transplant Rep. 2021;8(4):324-332. doi:10.1007/s40472-021-00346-x
  • Ku L, Vichare A. The association of racial and ethnic concordance in primary care with patient satisfaction and experience of care. J Gen Intern Med. 2023;38(3):727-732. doi:10.1007/s11606-022-07695-y
  • Jetty A, Jabbarpour Y, Pollack J, Huerto R, Woo S, Petterson S. Patient-physician racial concordance associated with improved healthcare use and lower healthcare expenditures in minority populations. J Racial Ethn Health Disparities. 2022;9(1):68-81. doi:10.1007/s40615-020-00930-4
  • Simpson DC, Obayemi JE, Kershaw KN, Franklin JE, Ladner DP. The African American transplant access program: Mitigating disparities in solid organ transplantation. NEJM Catal Innov Care Deliv. 2024;5(9). doi:10.1056/cat.24.0140
  • McElroy LM, Reed RD, Gordon E, et al. CHART: Harmonizing data for research, transparency and equity: Harmonizing data for research, transparency and equity. Ann Surg. Published online June 20, 2024. doi:10.1097/SLA.0000000000006410
  • Strauss AT, Brundage J, Sidoti CN, et al. Patient perspectives on liver transplant evaluation: A qualitative study. Patient Educ Couns. 2024;127(108346):108346. doi:10.1016/j.pec.2024.108346
  • Brahmania M, Kuo A, Tapper EB, et al. Quality measures in pre-liver transplant care by the Practice Metrics Committee of the American Association for the Study of Liver Diseases. Hepatology. Published online March 27, 2024:10.1097/HEP.0000000000000870. doi:10.1097/HEP.0000000000000870
  • Warren, Carpenter C, Neal AM, et al. Racial Disparity in Liver Transplantation Listing. J Am Coll Surg. 2021;232(4):526-534. doi:10.1016/j.jamcollsurg.2020.12.021
  • Gordon EJ, Reddy E, Gil S, et al. Culturally Competent Transplant Program Improves Hispanics’ Knowledge and Attitudes about Live Kidney Donation and Transplant. Progress in Transplantation. 2014;24(1):56-68. doi:10.7182/pit2014378
  • Mohamed KA, Ghabril M, Desai A, et al. Neighborhood poverty is associated with failure to be waitlisted and death during liver transplantation evaluation. Liver Transpl. 2022;28(9):1441-1453. doi:10.1002/lt.26473
  • Strauss AT, Sidoti CN, Purnell TS, et al. Multicenter study of racial and ethnic inequities in liver transplantation evaluation: Understanding mechanisms and identifying solutions. Liver Transpl. 2022;28(12):1841-1856. doi:10.1002/lt.26532
  • Rosenblatt R, Lee H, Liapakis A, et al. Equitable Access to Liver Transplant: Bridging the Gaps in the Social Determinants of Health. Hepatology. 2021;74(5):2808-2812. doi:10.1002/hep.31986
  • Jesse MT, Abouljoud M, Goldstein ED, et al. Racial disparities in patient selection for liver transplantation: An ongoing challenge. Clin Transplant. 2019;33(11):e13714. doi:10.1111/ctr.13714
  • Holden RJ, Carayon P, Gurses AP, et al. SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients. Ergonomics. 2013;56(11):1669-1686. doi:10.1080/00140139.2013.838643
  • Kemmer N, Alsina A, Neff GW. Social determinants of orthotopic liver transplantation candidacy: role of patient-related factors. Transplant Proc. 2011;43(10):3769-3772. doi:10.1016/j.transproceed.2011.08.076

AASLD TLM – 11/2024

  • Schwabe D, Becker K, Seyferth M, Klaß A, Schaeffter T. The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review. NPJ Digit Med. 2024;7(1):203. doi:10.1038/s41746-024-01196-4
  • Price WN 2nd, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019;25(1):37-43. doi:10.1038/s41591-018-0272-7
  • Kwon OY, Lee MK, Lee HW, Kim H, Lee JS, Jang Y. Mobile app-based lifestyle coaching intervention for patients with nonalcoholic fatty liver disease: Randomized controlled trial. J Med Internet Res. 2024;26(1):e49839. doi:10.2196/49839
  • Cacciaglia A. Gen AI Saves Nurses Time by Drafting Responses to Patient Messages. EpicShare. March 4, 2024. Accessed November 13, 2024. https://www.epicshare.org/share-and-learn/mayo-ai-message-responses
  • Au J, Falloon C, Ravi A, Ha P, Le S. A beta-prototype chatbot for increasing health literacy of patients with decompensated cirrhosis: Usability study. JMIR Hum Factors. 2023;10(1):e42506. doi:10.2196/42506
  • Ponzo V, Goitre I, Favaro E, et al. Is ChatGPT an effective tool for providing dietary advice? Nutrients. 2024;16(4):469. doi:10.3390/nu16040469
  • Pugliese N, Wai-Sun Wong V, Schattenberg JM, et al. Accuracy, reliability, and comprehensibility of ChatGPT-generated medical responses for patients with nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol. 2024;22(4):886-889.e5. doi:10.1016/j.cgh.2023.08.033
  • Gerke S, Minssen T, Cohen G. Chapter 12 – Ethical and legal challenges of artificial intelligence-driven healthcare. In: Bohr A, Memarzadeh K, eds. Artificial Intelligence in Healthcare. Academic Press; 2020:295-336. doi:10.1016/B978-0-12-818438-7.00012-5
  • Matheny ME, Thadaney IS, Ahmed M, Whicher D. Artificial Intelligence in Healthcare: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine; 2019. Accessed October 19, 2020. https://nam.edu/artificial-intelligence-special-publication/
  • Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. doi:10.1038/s41591-018-0300-7
  • Strauss AT, Brundage J, Sidoti CN, et al. Patient perspectives on liver transplant evaluation: A qualitative study. Patient Educ Couns. 2024;127(108346):108346. doi:10.1016/j.pec.2024.108346
  • Simpson DC, Obayemi JE, Kershaw KN, Franklin JE, Ladner DP. The African American transplant access program: Mitigating disparities in solid organ transplantation. NEJM Catal Innov Care Deliv. 2024;5(9). doi:10.1056/cat.24.0140
  • McElroy LM, Reed RD, Gordon E, et al. CHART: Harmonizing data for research, transparency and equity: Harmonizing data for research, transparency and equity. Ann Surg. Published online June 20, 2024. doi:10.1097/SLA.0000000000006410
  • Brahmania M, Kuo A, Tapper EB, et al. Quality measures in pre-liver transplant care by the Practice Metrics Committee of the American Association for the Study of Liver Diseases. Hepatology. Published online March 27, 2024:10.1097/HEP.0000000000000870. doi:10.1097/HEP.0000000000000870
  • Ku L, Vichare A. The association of racial and ethnic concordance in primary care with patient satisfaction and experience of care. J Gen Intern Med. 2023;38(3):727-732. doi:10.1007/s11606-022-07695-y
  • Strauss AT, Sidoti CN, Purnell TS, et al. Multicenter study of racial and ethnic inequities in liver transplantation evaluation: Understanding mechanisms and identifying solutions. Liver Transpl. 2022;28(12):1841-1856. doi:10.1002/lt.26532
  • Jetty A, Jabbarpour Y, Pollack J, Huerto R, Woo S, Petterson S. Patient-physician racial concordance associated with improved healthcare use and lower healthcare expenditures in minority populations. J Racial Ethn Health Disparities. 2022;9(1):68-81. doi:10.1007/s40615-020-00930-4
  • Patzer RE, Retzloff S, Buford J, et al. Community engagement to improve equity in kidney transplantation from the ground up: The southeastern kidney transplant coalition. Curr Transplant Rep. 2021;8(4):324-332. doi:10.1007/s40472-021-00346-x
  • Rosenblatt R, Lee H, Liapakis A, et al. Equitable Access to Liver Transplant: Bridging the Gaps in the Social Determinants of Health. Hepatology. 2021;74(5):2808-2812. doi:10.1002/hep.31986
  • Gordon EJ, Reddy E, Gil S, et al. Culturally Competent Transplant Program Improves Hispanics’ Knowledge and Attitudes about Live Kidney Donation and Transplant. Progress in Transplantation. 2014;24(1):56-68. doi:10.7182/pit2014378

AASLD LGBTQ+ Task Force Webinar – 6/2024

  • Lee TH, Paul S, Kahn J. Model for End-Stage Liver Disease 3.0: One step forward to mitigate sex and gender disparities in liver transplant. Am J Transplant. 2024;24(1):145-146. doi:10.1016/j.ajt.2023.09.013
  • Glossary of Terms. Human Rights Campaign. https://www.hrc.org/resources/glossary-of-terms
  • GLAAD Media Reference Guide – 11th Edition. GLAAD | GLAAD rewrites the script for LGBTQ acceptance. Published February 21, 2022. https://glaad.org/reference/
  • New York City Department of Social Services Human Resources Administration. Gender Pronouns. Published 2017. https://www.nyc.gov/assets/hra/downloads/pdf/services/lgbtqi/Gender%20Pronouns%20final%20draft%2010.23.17.pdf
  • Stryker S. Transgender History, Second Edition. 2nd ed. Seal Press; 2017.
  • Pickett B. Homosexuality. Published August 6, 2002. https://plato.stanford.edu/entries/homosexuality/
  • Al-Mamun M, Hossain MJ, Alam M, Parvez MS, Dhar BK, Islam MR. Discrimination and social exclusion of third-gender population (Hijra) in Bangladesh: A brief review. Heliyon. 2022;8(10):e10840. doi:10.1016/j.heliyon.2022.e10840
  • Thurston I. The History of Two-Spirit Folks. The Indigenous Foundation. Published June 29, 2022. Accessed June 25, 2024. https://www.theindigenousfoundation.org/articles/the-history-of-two-spirit-folks
  • Gender Unicorn. Published August 13, 2017. Accessed June 25, 2024. https://transstudent.org/gender/
  • Henningham M. Nowhere to bi: Barriers to belonging in the broader LGBTQ+ community for Aboriginal bi+ people in Australia. J Lesbian Stud. 2024;28(1):63-83. doi:10.1080/10894160.2023.2233339

DDW – 5/2024

  • Alvidrez, J., Castille, D., Laude-Sharp, M., Rosario, A., & Tabor, D. (2019). The National Institute on Minority Health and Health Disparities Research Framework. American Journal of Public Health, 109(S1), S16–S20. https://doi.org/10.2105/AJPH.2018.304883
  • Bertot, L. C., Jeffrey, G. P., Wallace, M., MacQuillan, G., Garas, G., Ching, H. L., & Adams, L. A. (2017). Nonalcoholic fatty liver disease-related cirrhosis is commonly unrecognized and associated with hepatocellular carcinoma. Hepatology Communications, 1(1), 53–60. https://doi.org/10.1002/hep4.1018
  • Marcozzi, D., Carr, B., Liferidge, A., Baehr, N., & Browne, B. (2018). Trends in the Contribution of Emergency Departments to the Provision of Hospital-Associated Health Care in the USA. International Journal of Health Services: Planning, Administration, Evaluation, 48(2), 267–288. https://doi.org/10.1177/0020731417734498
  • Ochoa-Allemant, P., Marrero, J. A., & Serper, M. (2023). Racial and ethnic differences and the role of unfavorable social determinants of health across steatotic liver disease subtypes in the United States. Hepatology Communications, 7(12). https://doi.org/10.1097/HC9.0000000000000324
  • Rich, N. E., Oji, S., Mufti, A. R., Browning, J. D., Parikh, N. D., Odewole, M., Mayo, H., & Singal, A. G. (2018). Racial and Ethnic Disparities in Nonalcoholic Fatty Liver Disease Prevalence, Severity, and Outcomes in the United States: A Systematic Review and Meta-analysis. Clinical Gastroenterology and Hepatology: The Official Clinical Practice Journal of the American Gastroenterological Association, 16(2), 198–210.e2. https://doi.org/10.1016/j.cgh.2017.09.041
  • Younossi, Z. M., Koenig, A. B., Abdelatif, D., Fazel, Y., Henry, L., & Wymer, M. (2016). Global epidemiology of nonalcoholic fatty liver disease—Meta‐analytic assessment of prevalence, incidence, and outcomes. Hepatology , 64(1), 73–84. https://doi.org/10.1002/hep.28431

  • Department of Veterans Affairs. (2024, February 14). VHA Aligns with Leading Health Care Organizations to Ensure Trustworthy Use of AI – VA Artificial Intelligence. https://department.va.gov/ai/vha-aligns-with-leading-health-care-organizations-to-ensure-trustworthy-use-of-ai/
  • Dite, G. S., Spaeth, E., Wong, C. K., Murphy, N. M., & Allman, R. (2023). Predicting 10-Year Risk of Pancreatic Cancer Using a Combined Genetic and Clinical Model. Gastro Hep Advances, 2(7), 979–989. https://doi.org/10.1016/j.gastha.2023.05.008
  • Ebigbo, A., Mendel, R., Probst, A., Manzeneder, J., Prinz, F., de Souza, L. A., Jr, Papa, J., Palm, C., & Messmann, H. (2020). Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus. Gut, 69(4), 615–616. https://doi.org/10.1136/gutjnl-2019-319460
  • Faghani, S., Codipilly, D. C., David Vogelsang, Moassefi, M., Rouzrokh, P., Khosravi, B., Agarwal, S., Dhaliwal, L., Katzka, D. A., Hagen, C., Lewis, J., Leggett, C. L., Erickson, B. J., & Iyer, P. G. (2022). Development of a deep learning model for the histologic diagnosis of dysplasia in Barrett’s esophagus. Gastrointestinal Endoscopy, 96(6), 918–925.e3. https://doi.org/10.1016/j.gie.2022.06.013
  • Fattovich, G., Stroffolini, T., Zagni, I., & Donato, F. (2004). Hepatocellular carcinoma in cirrhosis: incidence and risk factors. Gastroenterology, 127(5 Suppl 1), S35–S50. https://doi.org/10.1053/j.gastro.2004.09.014
  • Fockens, K. N., Jong, M. R., Jukema, J. B., Boers, T. G. W., Kusters, C. H. J., van der Putten, J. A., Pouw, R. E., Duits, L. C., Montazeri, N. S. M., van Munster, S. N., Weusten, B. L. A. M., Alvarez Herrero, L., Houben, M. H. M. G., Nagengast, W. B., Westerhof, J., Alkhalaf, A., Mallant-Hent, R. C., Scholten, P., Ragunath, K., … Barrett’s Oesophagus Imaging for Artificial Intelligence (BONS-AI) consortium. (2023). A deep learning system for detection of early Barrett’s neoplasia: a model development and validation study. The Lancet. Digital Health, 5(12), e905–e916. https://doi.org/10.1016/S2589-7500(23)00199-1
  • Gimeno-García, A. Z., Hernández Negrin, D., Hernández, A., Nicolás-Pérez, D., Rodríguez, E., Montesdeoca, C., Alarcon, O., Romero, R., Baute Dorta, J. L., Cedrés, Y., Castillo, R. D., Jiménez, A., Felipe, V., Morales, D., Ortega, J., Reygosa, C., Quintero, E., & Hernández-Guerra, M. (2023). Usefulness of a novel computer-aided detection system for colorectal neoplasia: a randomized controlled trial. Gastrointestinal Endoscopy, 97(3), 528–536.e1. https://doi.org/10.1016/j.gie.2022.09.029
  • Lui, T. K. L., Ko, M. K. L., Liu, J. J., Xiao, X., & Leung, W. K. (2024). Artificial intelligence-assisted real-time monitoring of effective withdrawal time during colonoscopy: a novel quality marker of colonoscopy. Gastrointestinal Endoscopy, 99(3), 419–427.e6. https://doi.org/10.1016/j.gie.2023.10.035
  • McDonald, O. G., & Montgomery, E. A. (2022). Artificial intelligence for dysplasia grading in Barrett’s esophagus: hematoxylin and eosin is here to stay [Review of Artificial intelligence for dysplasia grading in Barrett’s esophagus: hematoxylin and eosin is here to stay]. Gastrointestinal Endoscopy, 96(6), 926–928. https://doi.org/10.1016/j.gie.2022.08.013
  • Meinikheim, M., Messmann, H., & Ebigbo, A. (2023). Role of artificial intelligence in diagnosing Barrett’s esophagus-related neoplasia. Clinical Endoscopy, 56(1), 14–22. https://doi.org/10.5946/ce.2022.247
  • Mukherjee, S., Patra, A., Khasawneh, H., Korfiatis, P., Rajamohan, N., Suman, G., Majumder, S., Panda, A., Johnson, M. P., Larson, N. B., Wright, D. E., Kline, T. L., Fletcher, J. G., Chari, S. T., & Goenka, A. H. (2022). Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis. Gastroenterology, 163(5), 1435–1446.e3. https://doi.org/10.1053/j.gastro.2022.06.066
  • Nehme, F., Coronel, E., Barringer, D. A., Romero, L. G., Shafi, M. A., Ross, W. A., & Ge, P. S. (2023). Performance and attitudes toward real-time computer-aided polyp detection during colonoscopy in a large tertiary referral center in the United States. Gastrointestinal Endoscopy, 98(1), 100–109.e6. https://doi.org/10.1016/j.gie.2023.02.016
  • Placido, D., Yuan, B., Hjaltelin, J. X., Zheng, C., Haue, A. D., Chmura, P. J., Yuan, C., Kim, J., Umeton, R., Antell, G., Chowdhury, A., Franz, A., Brais, L., Andrews, E., Marks, D. S., Regev, A., Ayandeh, S., Brophy, M. T., Do, N. V., … Sander, C. (2023). A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nature Medicine, 29(5), 1113–1122. https://doi.org/10.1038/s41591-023-02332-5
  • Samarasena, J., Yang, D., & Berzin, T. M. (2023). AGA Clinical Practice Update on the Role of Artificial Intelligence in Colon Polyp Diagnosis and Management: Commentary. Gastroenterology, 165(6), 1568–1573. https://doi.org/10.1053/j.gastro.2023.07.010
  • Schöler, J., Alavanja, M., de Lange, T., Yamamoto, S., Hedenström, P., & Varkey, J. (2024). Impact of AI-aided colonoscopy in clinical practice: a prospective randomised controlled trial. BMJ Open Gastroenterology, 11(1). https://doi.org/10.1136/bmjgast-2023-001247
  • The White House. (2023). Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/
  • Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, I. https://cir.nii.ac.jp/crid/1370849946232757637
  • Wei, M. T., Shankar, U., Parvin, R., Abbas, S. H., Chaudhary, S., Friedlander, Y., & Friedland, S. (2023). Evaluation of Computer-Aided Detection During Colonoscopy in the Community (AI-SEE): A Multicenter Randomized Clinical Trial. The American Journal of Gastroenterology, 118(10), 1841–1847. https://doi.org/10.14309/ajg.0000000000002239
  • Yao, L., Li, X., Wu, Z., Wang, J., Luo, C., Chen, B., Luo, R., Zhang, L., Zhang, C., Tan, X., Lu, Z., Zhu, C., Huang, Y., Tan, T., Liu, Z., Li, Y., Li, S., & Yu, H. (2024). Effect of artificial intelligence on novice-performed colonoscopy: a multicenter randomized controlled tandem study. Gastrointestinal Endoscopy, 99(1), 91–99.e9. https://doi.org/10.1016/j.gie.2023.07.044
  • Yeo, Y. H., Samaan, J. S., Ng, W. H., Ting, P.-S., Trivedi, H., Vipani, A., Ayoub, W., Yang, J. D., Liran, O., Spiegel, B., & Kuo, A. (2023). Assessing the performance of ChatGPT in answering questions regarding cirrhosis and hepatocellular carcinoma. Clinical and Molecular Hepatology, 29(3), 721–732. https://doi.org/10.3350/cmh.2023.0089
  • Zhang, X., Tang, D., Zhou, J.-D., Ni, M., Yan, P., Zhang, Z., Yu, T., Zhan, Q., Shen, Y., Zhou, L., Zheng, R., Zou, X., Zhang, B., Li, W.-J., & Wang, L. (2023). A real-time interpretable artificial intelligence model for the cholangioscopic diagnosis of malignant biliary stricture (with videos). Gastrointestinal Endoscopy, 98(2), 199–210.e10. https://doi.org/10.1016/j.gie.2023.02.026