Artificial Intelligence: The Next Blockbuster Drug in Critical Care?

1. Lewis JF, Brackenbury A. Role of exogenous surfactant in acute lung injury. Crit Care Med. 2003;31(4 Suppl):S324–8. doi: 10.1097/01.CCM.0000057911.19145.9F. Lewis JF Brackenbury A. Role of exogenous surfactant in acute lung injury . Crit Care Med . 2003 ; 31 ( 4 Suppl ): S324 – 8 . doi: 10.1097/01.CCM.0000057911.19145.9F . Open DOISearch in Google Scholar

2. Zarychanski R, Abou-Setta AM, Turgeon AF, et al. Association of hydroxyethyl starch administration with mortality and acute kidney injury in critically ill patients requiring volume resuscitation: a systematic review and meta-analysis. JAMA. 2013;309(7):678–88. doi: 10.1001/jama.2013.430. Zarychanski R Abou-Setta AM Turgeon AF Association of hydroxyethyl starch administration with mortality and acute kidney injury in critically ill patients requiring volume resuscitation: a systematic review and meta-analysis . JAMA . 2013 ; 309 ( 7 ): 678 – 88 . doi: 10.1001/jama.2013.430 . Open DOISearch in Google Scholar

3. Savel RH, Munro CL. Evidence-based backlash: the tale of drotrecogin alfa. Am J Crit Care. 2012;21(2):81–3. doi: 10.4037/ajcc2012903. Savel RH Munro CL. Evidence-based backlash: the tale of drotrecogin alfa . Am J Crit Care . 2012 ; 21 ( 2 ): 81 – 3 . doi: 10.4037/ajcc2012903 . Open DOISearch in Google Scholar

4. Acute Respiratory Distress Syndrome Network, Brower RG, Matthay MA, et al. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. N Engl J Med. 2000;342(18):1301–8. doi: 10.1056/NEJM200005043421801. Acute Respiratory Distress Syndrome Network Brower RG Matthay MA Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome . N Engl J Med . 2000 ; 342 ( 18 ): 1301 – 8 . doi: 10.1056/NEJM200005043421801 . Open DOISearch in Google Scholar

5. Feretzakis G, Karlis G, Loupelis E, et al. Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department. J Crit Care Med (Targu Mures). 2022;8(2):107–16. doi: 10.2478/jccm-2022-0003. Feretzakis G Karlis G Loupelis E Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department . J Crit Care Med (Targu Mures) . 2022 ; 8 ( 2 ): 107 – 16 . doi: 10.2478/jccm-2022-0003 . Open DOISearch in Google Scholar

6. Lundberg SM, Nair B, Vavilala MS, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng. 2018;2(10):749–60. doi: 10.1038/s41551-018-0304-0. Lundberg SM Nair B Vavilala MS Explainable machine-learning predictions for the prevention of hypoxaemia during surgery . Nat Biomed Eng . 2018 ; 2 ( 10 ): 749 – 60 . doi: 10.1038/s41551-018-0304-0 . Open DOISearch in Google Scholar

7. Lyons PG, Hofford MR, Yu SC, et al. Factors Associated With Variability in the Performance of a Proprietary Sepsis Prediction Model Across 9 Networked Hospitals in the US. JAMA Intern Med. 2023. doi: 10.1001/jamainternmed.2022.7182. Lyons PG Hofford MR Yu SC Factors Associated With Variability in the Performance of a Proprietary Sepsis Prediction Model Across 9 Networked Hospitals in the US . JAMA Intern Med . 2023 . doi: 10.1001/jamainternmed.2022.7182 . Open DOISearch in Google Scholar

8. Vela D, Sharp A, Zhang R, et al. Temporal quality degradation in AI models. Sci Rep. 2022;12(1):11654. doi: 10.1038/s41598-022-15245-z. Vela D Sharp A Zhang R Temporal quality degradation in AI models . Sci Rep . 2022 ; 12 ( 1 ): 11654 . doi: 10.1038/s41598-022-15245-z . Open DOISearch in Google Scholar

9. Varoquaux G, Cheplygina V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ Digit Med. 2022;5(1):48. doi: 10.1038/s41746-022-00592-y. Varoquaux G Cheplygina V. Machine learning for medical imaging: methodological failures and recommendations for the future . NPJ Digit Med . 2022 ; 5 ( 1 ): 48 . doi: 10.1038/s41746-022-00592-y . Open DOISearch in Google Scholar

10. Mazurowski MA. Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce. J Am Coll Radiol. 2019;16(8):1077–82. doi: 10.1016/j.jacr.2019.01.026. Mazurowski MA. Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce . J Am Coll Radiol . 2019 ; 16 ( 8 ): 1077 – 82 . doi: 10.1016/j.jacr.2019.01.026 . Open DOISearch in Google Scholar

11. Krittanawong C. The rise of artificial intelligence and the uncertain future for physicians. Eur J Intern Med. 2018;48:e13–e4. doi: 10.1016/j.ejim.2017.06.017. Krittanawong C. The rise of artificial intelligence and the uncertain future for physicians . Eur J Intern Med . 2018 ; 48 : e13 – e4 . doi: 10.1016/j.ejim.2017.06.017 . Open DOISearch in Google Scholar

12. Lee P, Bubeck S, Petro J. Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine. N Engl J Med. 2023;388(13):1233–9. doi: 10.1056/NEJMsr2214184. Lee P Bubeck S Petro J. Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine . N Engl J Med . 2023 ; 388 ( 13 ): 1233 – 9 . doi: 10.1056/NEJMsr2214184 . Open DOISearch in Google Scholar

13. Kung TH, Cheatham M, Medenilla A, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2(2):e0000198. doi: 10.1371/journal.pdig.0000198. Kung TH Cheatham M Medenilla A Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models . PLOS Digit Health . 2023 ; 2 ( 2 ): e0000198 . doi: 10.1371/journal.pdig.0000198 . Open DOISearch in Google Scholar

14. Salvagno M, Taccone FS, Gerli AG. Can artificial intelligence help for scientific writing? Crit Care. 2023;27(1):75. doi: 10.1186/s13054-023-04380-2. Salvagno M Taccone FS Gerli AG. Can artificial intelligence help for scientific writing? Crit Care . 2023 ; 27 ( 1 ): 75 . doi: 10.1186/s13054-023-04380-2 . Open DOISearch in Google Scholar

15. Ayers JW, Poliak A, Dredze M, et al. Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Intern Med. 2023. doi: 10.1001/jamainternmed.2023.1838. Ayers JW Poliak A Dredze M Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum . JAMA Intern Med . 2023 . doi: 10.1001/jamainternmed.2023.1838 . Open DOISearch in Google Scholar

16. Vaswani A, Shazeer N, Parmar N, et al. Attention is All you Need. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, et al., editors.2017. Vaswani A Shazeer N Parmar N Attention is All you Need . In: Guyon I Luxburg UV Bengio S Wallach H Fergus R Vishwanathan S , editors. 2017 . Search in Google Scholar

17. Liu H, Ning R, Teng Z, et al. Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4. arXiv preprint arXiv:230403439. 2023. Liu H Ning R Teng Z Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4 . arXiv preprint arXiv:230403439 . 2023 . Search in Google Scholar

18. Wong A, Otles E, Donnelly JP, et al. External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients. JAMA Intern Med. 2021;181(8):1065–70. doi: 10.1001/jamainternmed.2021.2626. Wong A Otles E Donnelly JP External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients . JAMA Intern Med . 2021 ; 181 ( 8 ): 1065 – 70 . doi: 10.1001/jamainternmed.2021.2626 . Open DOISearch in Google Scholar

19. Smith H. Clinical AI: opacity, accountability, responsibility and liability. Ai & Society. 2020;36(2):535–45. doi: 10.1007/s00146-020-01019-6. Smith H. Clinical AI: opacity, accountability, responsibility and liability . Ai & Society . 2020 ; 36 ( 2 ): 535 – 45 . doi: 10.1007/s00146-020-01019-6 . Open DOISearch in Google Scholar

20. Daneshjou R, Smith MP, Sun MD, Rotemberg V, Zou J. Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review. JAMA Dermatol. 2021;157(11):1362–9. doi: 10.1001/jamadermatol.2021.3129. Daneshjou R Smith MP Sun MD Rotemberg V Zou J. Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review . JAMA Dermatol . 2021 ; 157 ( 11 ): 1362 – 9 . doi: 10.1001/jamadermatol.2021.3129 . Open DOISearch in Google Scholar

21. Noseworthy PA, Attia ZI, Brewer LC, et al. Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis. Circ Arrhythm Electrophysiol. 2020;13(3):e007988. doi: 10.1161/CIRCEP.119.007988. Noseworthy PA Attia ZI Brewer LC Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis . Circ Arrhythm Electrophysiol . 2020 ; 13 ( 3 ): e007988 . doi: 10.1161/CIRCEP.119.007988 . Open DOISearch in Google Scholar