Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801–10. https://doi.org/10.1001/jama.2016.0287.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200–11. https://doi.org/10.1016/S0140-6736(19)32989-7.

Article  PubMed  PubMed Central  Google Scholar 

Angus DC, Bindman AB. Achieving diagnostic excellence for sepsis. JAMA. 2022;327(2):117–8. https://doi.org/10.1001/jama.2021.23916.

Article  PubMed  Google Scholar 

Wolf RM, Channa R, Abramoff MD, Lehmann HP. Cost-effectiveness of autonomous point-of-care diabetic retinopathy screening for pediatric patients with diabetes. JAMA Ophthalmol. 2020;138(10):1063–9. https://doi.org/10.1001/jamaophthalmol.2020.3190.

Article  PubMed  Google Scholar 

Zhou D, Tian F, Tian X, Sun L, Huang X, Zhao F, et al. Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer. Nat Commun. 2020;11(1):2961. https://doi.org/10.1038/s41467-020-16777-6.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Huang P, Lin CT, Li Y, Tammemagi MC, Brock MV, Atkar-Khattra S, et al. Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method. Lancet Digit Health. 2019;1(7):e353–62. https://doi.org/10.1016/S2589-7500(19)30159-1.

Article  PubMed  PubMed Central  Google Scholar 

Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, et al. A deep learning approach to antibiotic discovery. Cell. 2020;181(2):475–83. https://doi.org/10.1016/j.cell.2020.04.001.

Article  CAS  PubMed  Google Scholar 

Van den Bruel A, Haj-Hassan T, Thompson M, Buntinx F, Mant D. European Research Network on Recognising Serious Infection i. Diagnostic value of clinical features at presentation to identify serious infection in children in developed countries: a systematic review. Lancet. 2010;375(9717):834–45. https://doi.org/10.1016/S0140-6736(09)62000-6.

Van den Bruel A, Thompson MJ, Haj-Hassan T, Stevens R, Moll H, Lakhanpaul M, et al. Diagnostic value of laboratory tests in identifying serious infections in febrile children: systematic review. BMJ. 2011;342: d3082. https://doi.org/10.1136/bmj.d3082.

Article  PubMed  Google Scholar 

Nijman RG, Vergouwe Y, Thompson M, van Veen M, van Meurs AH, van der Lei J, et al. Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: diagnostic study. BMJ. 2013;346: f1706. https://doi.org/10.1136/bmj.f1706.

Article  PubMed  PubMed Central  Google Scholar 

Wong HR, Cvijanovich N, Allen GL, Lin R, Anas N, Meyer K, et al. Genomic expression profiling across the pediatric systemic inflammatory response syndrome, sepsis, and septic shock spectrum. Crit Care Med. 2009;37(5):1558–66. https://doi.org/10.1097/CCM.0b013e31819fcc08.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Herberg JA, Kaforou M, Wright VJ, Shailes H, Eleftherohorinou H, Hoggart CJ, et al. Diagnostic test accuracy of a 2-transcript host RNA signature for discriminating bacterial vs viral infection in febrile children. JAMA. 2016;316(8):835–45. https://doi.org/10.1001/jama.2016.11236.

Article  PubMed  PubMed Central  Google Scholar 

Abbas M, El-Manzalawy Y. Machine learning based refined differential gene expression analysis of pediatric sepsis. BMC Med Genomics. 2020;13(1):122. https://doi.org/10.1186/s12920-020-00771-4.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Banerjee S, Mohammed A, Wong HR, Palaniyar N, Kamaleswaran R. Machine learning identifies complicated sepsis course and subsequent mortality based on 20 genes in peripheral blood immune cells at 24 H post-ICU admission. Front Immunol. 2021;12: 592303. https://doi.org/10.3389/fimmu.2021.592303.

Article  CAS  PubMed  PubMed Central  Google Scholar 

But S, Celar B, Fister P. Tackling neonatal sepsis-can it be predicted? Int J Environ Res Public Health. 2023;20(4):3644. https://doi.org/10.3390/ijerph20043644.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lamping F, Jack T, Rubsamen N, Sasse M, Beerbaum P, Mikolajczyk RT, et al. Development and validation of a diagnostic model for early differentiation of sepsis and non-infectious SIRS in critically ill children - a data-driven approach using machine-learning algorithms. BMC Pediatr. 2018;18(1):112. https://doi.org/10.1186/s12887-018-1082-2.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Stocker M, Daunhawer I, van Herk W, El Helou S, Dutta S, Schuerman F, et al. Machine learning used to compare the diagnostic accuracy of risk factors, clinical signs and biomarkers and to develop a new prediction model for neonatal early-onset sepsis. Pediatr Infect Dis J. 2022;41(3):248–54. https://doi.org/10.1097/INF.0000000000003344.

Article  PubMed  Google Scholar 

Wong HR, Cvijanovich N, Lin R, Allen GL, Thomas NJ, Willson DF, et al. Identification of pediatric septic shock subclasses based on genome-wide expression profiling. BMC Med. 2009;7:34. https://doi.org/10.1186/1741-7015-7-34.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Khalilzad Z, Hasasneh A, Tadj C. Newborn cry-based diagnostic system to distinguish between sepsis and respiratory distress syndrome using combined acoustic features. Diagnostics (Basel). 2022;12(11):2802. https://doi.org/10.3390/diagnostics12112802.

Article  PubMed  Google Scholar 

Tarricone F, Brunetti A, Buongiorno D, Altini N, Bevilacqua V, Del Vecchio A, et al. Intelligent neonatal sepsis early diagnosis system for very low birth weight infants. Appl Sci. 2021;11(1):404. https://doi.org/10.3390/app11010404.

Article  CAS  Google Scholar 

Aczon MD, Ledbetter DR, Laksana E, Ho LV, Wetzel RC. Continuous prediction of mortality in the PICU: a recurrent neural network model in a single-center dataset. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies. 2021;22(6):519–29. https://doi.org/10.1097/PCC.0000000000002682.

Article  PubMed  Google Scholar 

Chowdhery A, Narang S, Devlin J, Bosma M, Mishra G, Roberts A, et al. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:220402311. 2022.

Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, et al. Language models are few-shot learners. Adv Neural Inf Process Syst. 2020;33:1877–901.

Google Scholar 

Devlin J, Chang M-W, Lee K, Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:181004805. 2018.

OpenAI. Introducing ChatGPT. 2022. https://openai.com/blog/chatgpt. Accessed 12 July 2023.

Manyika J. An overview of Bard: an early experiment with generative AI. 2023. https://ai.google/static/documents/google-about-bard.pdf. Accessed 12 July 2023.

Anthropic. Introducing Claude. 2023. https://www.anthropic.com/index/introducing-claude. Accessed 12 July 2023.

Chen PC, Gadepalli K, MacDonald R, Liu Y, Kadowaki S, Nagpal K, et al. An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. Nat Med. 2019;25(9):1453–7. https://doi.org/10.1038/s41591-019-0539-7.

Article  CAS  PubMed  Google Scholar 

Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019;1(6):e271–97. https://doi.org/10.1016/S2589-7500(19)30123-2.

Article  PubMed  Google Scholar 

Park A, Chute C, Rajpurkar P, Lou J, Ball RL, Shpanskaya K, et al. Deep learning-assisted diagnosis of cerebral aneurysms using the HeadXNet Model. JAMA Netw Open. 2019;2(6): e195600. https://doi.org/10.1001/jamanetworkopen.2019.5600.

Article  PubMed  PubMed Central  Google Scholar 

Kim HE, Kim HH, Han BK, Kim KH, Han K, Nam H, et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit Health. 2020;2(3):e138–48. https://doi.org/10.1016/S2589-7500(20)30003-0.

Article  PubMed  Google Scholar 

Tschandl P, Rinner C, Apalla Z, Argenziano G, Codella N, Halpern A, et al. Human-computer collaboration for skin cancer recognition. Nat Med. 2020;26(8):1229–34. https://doi.org/10.1038/s41591-020-0942-0.

Article  CAS  PubMed  Google Scholar 

• Henry KE, Adams R, Parent C, Soleimani H, Sridharan A, Johnson L, et al. Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing. Nat Med. 2022;28(7):1447–54. https://doi.org/10.1038/s41591-022-01895-z. Accompanied a large multi-centre evaluation of a machine-learning early warning system and reported the factors which supported its adoption.

Article  CAS  PubMed  Google Scholar 

Goldstein B, Giroir B, Randolph A. International Consensus Conference on Pediatric S. International pediatric sepsis consensus conference: definitions for sepsis and organ dysfunction in pediatrics. Pediatric critical care medicine : A Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies. 2005;6(1):2–8. https://doi.org/10.1097/01.PCC.0000149131.72248.E6.

Menon K, Schlapbach LJ, Akech S, Argent A, Biban P, Carrol ED, et al. Criteria for pediatric sepsis-a systematic review and meta-analysis by the pediatric sepsis definition taskforce. Crit Care Med. 2022;50(1):21–36. https://doi.org/10.1097/CCM.0000000000005294.

Article  PubMed  Google Scholar 

Seymour CW, Kennedy JN, Wang S, Chang CH, Elliott CF, Xu Z, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA. 2019;321(20):2003–17. https://doi.org/10.1001/jama.2019.5791.

Article  CAS  PubMed  PubMed Central  Google Scholar 

• Ferreira I, Beisken S, Lueftinger L, Weinmaier T, Klein M, Bacher J, et al. Species identification and antibiotic resistance prediction by analysis of whole-genome sequence data by use of ARESdb: an Analysis of isolates from the unyvero lower respiratory tract infection trial. J Clin Microbiol. 2020;58(7):e00273-e320. https://doi.org/10.1128/JCM.00273-20. Ilustrated the potential to use apply machine learning to whole genome sequencing data to infer antibiotic suscpetibility.

Article  CAS  PubMed  PubMed Central