Subscribe to RSS
DOI: 10.1055/a-2335-6122
Current Developments from Silicon Valley – How Artificial Intelligence is Changing Gynecology and Obstetrics
Article in several languages: English | deutsch
Abstract
Artificial intelligence (AI) has become an omnipresent topic in the media. Lively discussions are being held on how AI could revolutionize the global healthcare landscape. The development of innovative AI models, including in the medical sector, is increasingly dominated by large high-tech companies. As a global technology epicenter, Silicon Valley hosts many of these technological giants which are muscling their way into healthcare provision with their advanced technologies. The annual conference of the American College of Obstetrics and Gynecology (ACOG) was held in San Francisco from 17 – 19 May 2024. ACOG celebrated its AI premier, hosting two sessions on current AI topics in gynecology at their annual conference. This paper provides an overview of the topics discussed and permits an insight into the thinking in Silicon Valley, showing how technology companies grow and fail there and examining how our American colleagues perceive increased integration of AI in gynecological and obstetric care. In addition to the classification of various, currently popular AI terms, the article also presents three areas where artificial intelligence is being used in gynecology and looks at the current developmental status in the context of existing obstacles to implementation and the current digitalization status of the German healthcare system.
Publication History
Received: 31 May 2024
Accepted after revision: 01 September 2024
Article published online:
06 December 2024
© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commecial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
-
References/Literatur
- 1 OʼMara MP. The Code: Silicon Valley and the remaking of America. 1st ed.. ed. New York City: Penguin Press; 2019
- 2 Wall Street Journal. Tracing the Scandal of Theranos Founder Elizabeth Holmes. 2024. Accessed May 23, 2024 at: https://www.wsj.com/news/collection/theranos-coverage-ea13b200
- 3 Bundesministerium für Gesundheit. Gemeinsam Digital – Digitalisierungsstrategie für Gesundheitswesen und die Pflege. 2023. Accessed May 23, 2024 at: https://www.bundesgesundheitsministerium.de/fileadmin/Dateien/3_Downloads/D/Digitalisierungsstrategie/BMG_Broschuere_Digitalisierungsstrategie_bf.pdf
- 4 Bundesministerium für Gesundheit. Gesetzentwurf der Bundesregierung – Entwurf eines Gesetzes zur Beschleunigung der Digitalisierung des Gesundheitswesens (Digital-Gesetz – DigiG). 2023. Accessed May 23, 2024 at: https://www.bundesgesundheitsministerium.de/fileadmin/Dateien/3_Downloads/Gesetze_und_Verordnungen/GuV/D/Kabinettvorlage_Digital-Gesetz-DigiG.pdf
- 5 Bundesministerium für Gesundheit. Referentenentwurf des Bundesministeriums für Gesundheit Entwurf eines Gesetzes zur verbesserten Nutzung von Gesundheitsdaten (Gesundheitsdatennutzungsgesetz – GDNG). 2023 Accessed May 23, 2024 at: https://www.bundesgesundheitsministerium.de/service/gesetze-und-verordnungen/detail/gesundheitsdatennutzungsgesetz.html
- 6 Maslej N, Fattorini L, Perrault R. et al. The AI Index 2024 Annual Report. AI Index Steering Committee, Institute for Human-Centered AI, Stanford University. 2024. Accessed May 09, 2024 at: https://aiindex.stanford.edu/wp-content/uploads/2024/04/HAI_2024_AI-Index-Report.pdf
- 7 U.S. Food & Drug Administration. Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices. 2024. Accessed May 23, 2024 at: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices
- 8 Zhou J, Li R, Zhou J. et al. The KIDScore™ D3 scoring system contributes to the prediction of embryonic development potential: A promising tool for screening high-quality embryos. Zygote 2022; 30: 528-535
- 9 Chiou N, Young-Lin N, Kelly C. et al. Development and Evaluation of Deep Learning Models for Cardiotocography Interpretation. medRxiv 2024;
- 10 Brocklehurst P, Field D, Greene K. et al. Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. Lancet 2017; 389: 1719-1729
- 11 OʼSullivan ME, Considine EC, OʼRiordan M. et al. Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring. Front Artif Intell 2021; 4: 765210
- 12 Vargas Calixto CA, Johann CA, Wu Y. et al. Prediction of Hypoxic-Ischemic Encephalopathy Using Events in Fetal Heart Rate and Uterine Pressure. Computing in Cardiology Conference (CinC) 2023. doi:10.22489/CinC.2023.380
- 13 Kearney RE, Wu YW, Vargas-Calixto J. et al. Construction of a comprehensive fetal monitoring database for the study of perinatal hypoxic ischemic encephalopathy. MethodsX 2024; 12: 102664
- 14 Chen Z, Liu Z, Du M. et al. Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications. Front Med (Lausanne) 2021; 8: 733468
- 15 Jost E, Kosian P, Jimenez Cruz J. et al. Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology. J Clin Med 2023; 12: 6833
- 16 Horgan R, Nehme L, Abuhamad A. Artificial intelligence in obstetric ultrasound: A scoping review. Prenat Diagn 2023; 43: 1176-1219
- 17 Maier-Hein L, Vedula SS, Speidel S. et al. Surgical data science for next-generation interventions. Nat Biomed Eng 2017; 1: 691-696
- 18 Ward TM, Mascagni P, Madani A. et al. Surgical data science and artificial intelligence for surgical education. J Surg Oncol 2021; 124: 221-230
- 19 Kiela D, Bartolo M, Nie Y. et al. Dynabench: Rethinking Benchmarking in NLP. ArXiv 2021;
- 20 Nori H, Lee YT, Zhang S. et al. Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine. ArXiv 2023;
- 21 Alkaissi H, McFarlane SI. Artificial Hallucinations in ChatGPT: Implications in Scientific Writing. Cureus 2023; 15: e35179
- 22 Zack T, Lehman E, Suzgun M. et al. Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study. Lancet Digit Health 2024; 6: e12-e22
- 23 Microsoft. New consortium of healthcare leaders announces formation of Trustworthy & Responsible AI Network (TRAIN), making safe and fair AI accessible to every healthcare organization. 2024. Accessed May 23, 2024 at: https://news.microsoft.com/2024/03/11/new-consortium-of-healthcare-leaders-announces-formation-of-trustworthy-responsible-ai-network-train-making-safe-and-fair-ai-accessible-to-every-healthcare-organization/
- 24 Coalition for Health AI. Blueprint for trustworthy AI implementation guidance and assurance for healthcare. 2023. Accessed May 23, 2024 at: https://www.coalitionforhealthai.org/papers/blueprint-for-trustworthy-ai_V1.0.pdf
- 25 European Commission. AI Act. 2023. Accessed May 23, 2024 at: https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
- 26 The Lancet Regional Health-Western Pacific. Precision therapeutic landscape for breast cancer: where are we headed?. Lancet Reg Health West Pac 2024; 42: 101024
- 27 Johnson KB, Wei WQ, Weeraratne D. et al. Precision Medicine, AI, and the Future of Personalized Health Care. Clin Transl Sci 2021; 14: 86-93
- 28 Meskó B, Görög M. A short guide for medical professionals in the era of artificial intelligence. NPJ Digit Med 2020; 3: 126
- 29 Statistisches Bundesamt. Durchschnittliches Alter der Mütter und Väter bei der Geburt eines Kindes in Deutschland von 1991 bis 2022. 2023. Accessed May 23, 2024 at: https://www-genesis.destatis.de/genesis/online