Artificial Intelligence in the Neonatal Intensive Care Unit: the Time is Now

Overview

Journal J Perinatol

Specialty Gynecology & Obstetrics

Date 2023 Jul 13

PMID 37443271

Authors

Kristyn Beam

Puneet Sharma

Phil Levy

Andrew L Beam

Affiliations

Soon will be listed here.

Abstract

Artificial intelligence (AI) has the potential to revolutionize the neonatal intensive care unit (NICU) care by leveraging the large-scale, high-dimensional data that are generated by NICU patients. There is an emerging recognition that the confluence of technological progress, commercialization pathways, and rich data sets provides a unique opportunity for AI to make a lasting impact on the NICU. In this perspective article, we discuss four broad categories of AI applications in the NICU: imaging interpretation, prediction modeling of electronic health record data, integration of real-time monitoring data, and documentation and billing. By enhancing decision-making, streamlining processes, and improving patient outcomes, AI holds the potential to transform the quality of care for vulnerable newborns, making the excitement surrounding AI advancements well-founded and the potential for significant positive change stronger than ever before.

Citing Articles

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References

Rosenblatt F . The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958; 65(6):386-408. DOI: 10.1037/h0042519. View

Beam A, Kohane I . Translating Artificial Intelligence Into Clinical Care. JAMA. 2016; 316(22):2368-2369. DOI: 10.1001/jama.2016.17217. View

Yu K, Beam A, Kohane I . Artificial intelligence in healthcare. Nat Biomed Eng. 2019; 2(10):719-731. DOI: 10.1038/s41551-018-0305-z. View

Schmaltz A, Beam A . Sharpening the resolution on data matters: a brief roadmap for understanding deep learning for medical data. Spine J. 2020; 21(10):1606-1609. PMC: 7904953. DOI: 10.1016/j.spinee.2020.08.012. View

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

Schmidhuber J . Deep learning in neural networks: an overview. Neural Netw. 2014; 61:85-117. DOI: 10.1016/j.neunet.2014.09.003. View

Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O . Highly accurate protein structure prediction with AlphaFold. Nature. 2021; 596(7873):583-589. PMC: 8371605. DOI: 10.1038/s41586-021-03819-2. View

Silver D, Huang A, Maddison C, Guez A, Sifre L, Van Den Driessche G . Mastering the game of Go with deep neural networks and tree search. Nature. 2016; 529(7587):484-9. DOI: 10.1038/nature16961. View

Beam A, Drazen J, Kohane I, Leong T, Manrai A, Rubin E . Artificial Intelligence in Medicine. N Engl J Med. 2023; 388(13):1220-1221. DOI: 10.1056/NEJMe2206291. View

10.

Kates-Harbeck J, Svyatkovskiy A, Tang W . Predicting disruptive instabilities in controlled fusion plasmas through deep learning. Nature. 2019; 568(7753):526-531. DOI: 10.1038/s41586-019-1116-4. View

11.

Baldi P, Sadowski P, Whiteson D . Searching for exotic particles in high-energy physics with deep learning. Nat Commun. 2014; 5:4308. DOI: 10.1038/ncomms5308. View

12.

Moult J . A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. Curr Opin Struct Biol. 2005; 15(3):285-9. DOI: 10.1016/j.sbi.2005.05.011. View

13.

Ghorbani A, Ouyang D, Abid A, He B, Chen J, Harrington R . Deep learning interpretation of echocardiograms. NPJ Digit Med. 2020; 3:10. PMC: 6981156. DOI: 10.1038/s41746-019-0216-8. View

14.

Liu Y, Jain A, Eng C, Way D, Lee K, Bui P . A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020; 26(6):900-908. DOI: 10.1038/s41591-020-0842-3. View

15.

Esteva A, Kuprel B, Novoa R, Ko J, Swetter S, Blau H . Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 546(7660):686. DOI: 10.1038/nature22985. View

16.

Abramoff M, Lavin P, Birch M, Shah N, Folk J . Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2019; 1:39. PMC: 6550188. DOI: 10.1038/s41746-018-0040-6. View

17.

Abramoff M, Lou Y, Erginay A, Clarida W, Amelon R, Folk J . Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. Invest Ophthalmol Vis Sci. 2016; 57(13):5200-5206. DOI: 10.1167/iovs.16-19964. View

18.

Campanella G, Hanna M, Geneslaw L, Miraflor A, Silva V, Busam K . Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019; 25(8):1301-1309. PMC: 7418463. DOI: 10.1038/s41591-019-0508-1. View

19.

Wu E, Wu K, Daneshjou R, Ouyang D, Ho D, Zou J . How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med. 2021; 27(4):582-584. DOI: 10.1038/s41591-021-01312-x. View

20.

Cruz Rivera S, Liu X, Chan A, Denniston A, Calvert M . Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Health. 2020; 2(10):e549-e560. PMC: 8212701. DOI: 10.1016/S2589-7500(20)30219-3. View