Algor-ethics: charting the ethical path for AI in critical care.
Algorethics
Artificial intelligence
Data engineering
Ethics
Machine learning
Journal
Journal of clinical monitoring and computing
ISSN: 1573-2614
Titre abrégé: J Clin Monit Comput
Pays: Netherlands
ID NLM: 9806357
Informations de publication
Date de publication:
04 Apr 2024
04 Apr 2024
Historique:
received:
18
03
2023
accepted:
22
03
2024
medline:
4
4
2024
pubmed:
4
4
2024
entrez:
4
4
2024
Statut:
aheadofprint
Résumé
The integration of Clinical Decision Support Systems (CDSS) based on artificial intelligence (AI) in healthcare is groundbreaking evolution with enormous potential, but its development and ethical implementation, presents unique challenges, particularly in critical care, where physicians often deal with life-threating conditions requiring rapid actions and patients unable to participate in the decisional process. Moreover, development of AI-based CDSS is complex and should address different sources of bias, including data acquisition, health disparities, domain shifts during clinical use, and cognitive biases in decision-making. In this scenario algor-ethics is mandatory and emphasizes the integration of 'Human-in-the-Loop' and 'Algorithmic Stewardship' principles, and the benefits of advanced data engineering. The establishment of Clinical AI Departments (CAID) is necessary to lead AI innovation in healthcare, ensuring ethical integrity and human-centered development in this rapidly evolving field.
Identifiants
pubmed: 38573370
doi: 10.1007/s10877-024-01157-y
pii: 10.1007/s10877-024-01157-y
doi:
Types de publication
Letter
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
Références
Moazemi S, Vahdati S, Li J, Kalkhoff S, Castano LJV, Dewitz B, et al. Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: a systematic review. Front Med. 2023;10:1109411.
doi: 10.3389/fmed.2023.1109411
Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. Npj Digit Med. 2020;3:118.
doi: 10.1038/s41746-020-00324-0
pubmed: 32984550
pmcid: 7486909
Muehlematter UJ, Bluethgen C, Vokinger KN. FDA-cleared artificial intelligence and machine learning-based medical devices and their 510(k) predicate networks. Lancet Digit Health. 2023;5:e618–26.
doi: 10.1016/S2589-7500(23)00126-7
pubmed: 37625896
van de Sande D, van Genderen ME, Huiskens J, Gommers D, van Bommel J. Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit. Intensive Care Med. 2021;47:750–60.
doi: 10.1007/s00134-021-06446-7
pubmed: 34089064
pmcid: 8178026
van de Sande D, van Bommel J, Fung Fen Chung E, Gommers D, van Genderen ME. Algorithmic fairness audits in intensive care medicine: artificial intelligence for all? Crit Care. 2022;26:315.
doi: 10.1186/s13054-022-04197-5
pubmed: 36258241
pmcid: 9578232
McCradden MD, Joshi S, Mazwi M, Anderson JA. Ethical limitations of algorithmic fairness solutions in health care machine learning. Lancet Digit Health. 2020;2:e221–3.
doi: 10.1016/S2589-7500(20)30065-0
pubmed: 33328054
Grote T, Keeling G. Enabling Fairness in Healthcare through Machine Learning. Ethics Inf Technol. 2022;24:39.
doi: 10.1007/s10676-022-09658-7
pubmed: 36060496
pmcid: 9428374
Eaneff S, Obermeyer Z, Butte AJ. The case for Algorithmic Stewardship for Artificial Intelligence and Machine Learning Technologies. JAMA. 2020;324:1397.
doi: 10.1001/jama.2020.9371
pubmed: 32926087
Mosqueira-Rey E, Hernández-Pereira E, Alonso-Ríos D, Bobes-Bascarán J, Fernández-Leal Á. Human-in-the-loop machine learning: a state of the art. Artif Intell Rev. 2023;56:3005–54.
doi: 10.1007/s10462-022-10246-w
Friedman TL. Thank you for being late: an Optimist’s guide to thriving in the age of accelerations. London, UK: Allen Lane; 2016.
Nakanishi R, Okubo R, Sobue Y, Kaneko U, Sato H, Fujimoto S, Nozaki Y, Kajiya T, Miyoshi T, Ichikawa K, Abe M, Kitagawa T, Ikenaga H, Osawa K, Saji M, Iguchi N, Nakazawa G, Takahashi K, Ijich T, Mikamo H, Kurata A, Moroi M, Iijima R, Malkasian S, Crabtree T, Chamie D, Alexandra LJ, Min JK, Earls JP, Matsuo H. Rationale and design of the INVICTUS Registry: (Multicenter Registry of Invasive and Non-Invasive imaging modalities to compare Coronary Computed Tomography Angiography, Intravascular Ultrasound and Optical Coherence Tomography for the determination of Severity, Volume and Type of coronary atherosclerosiS). J Cardiovasc Comput Tomogr. 2023 Sep 5:S1934-5925(23)00427-6.
Gerard SE, Herrmann J, Xin Y, Martin KT, Rezoagli E, Ippolito D, Bellani G, Cereda M, Guo J, Hoffman EA, Kaczka DW, Reinhardt JM. CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network. Sci Rep. 2021;11(1):1455.
doi: 10.1038/s41598-020-80936-4
pubmed: 33446781
pmcid: 7809065
Connell M, Xin Y, Gerard SE, Herrmann J, Shah PK, Martin KT, Rezoagli E, Ippolito D, Rajaei J, Baron R, Delvecchio P, Humayun S, Rizi RR, Bellani G, Cereda M. Unsupervised segmentation and quantification of COVID-19 lesions on computed tomography scans using CycleGAN. Methods. 2022;205:200–9.
doi: 10.1016/j.ymeth.2022.07.007
pubmed: 35817338
pmcid: 9288584
Maddali MV, Churpek M, Pham T, Rezoagli E, Zhuo H, Zhao W, He J, Delucchi KL, Wang C, Wickersham N, McNeil JB, Jauregui A, Ke S, Vessel K, Gomez A, Hendrickson CM, Kangelaris KN, Sarma A, Leligdowicz A, Liu KD, Matthay MA, Ware LB, Laffey JG, Bellani G, Calfee CS, Sinha P. LUNG SAFE investigators and the ESICM Trials Group. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis. Lancet Respir Med. 2022;10(4):367–77.
doi: 10.1016/S2213-2600(21)00461-6
pubmed: 35026177
pmcid: 8976729
Stephens AF, Šeman M, Diehl A, Pilcher D, Barbaro RP, Brodie D, Pellegrino V, Kaye DM, Gregory SD, Hodgson C. Extracorporeal Life Support Organization Member centres. ECMO PAL: using deep neural networks for survival prediction in venoarterial extracorporeal membrane oxygenation. Intensive Care Med. 2023;49(9):1090–9.
doi: 10.1007/s00134-023-07157-x
pubmed: 37548758
pmcid: 10499722
Chen YY, Liu CF, Shen YT, Kuo YT, Ko CC, Chen TY, Wu TC, Shih YJ. Development of real-time individualized risk prediction models for contrast associated acute kidney injury and 30-day dialysis after contrast enhanced computed tomography. Eur J Radiol. 2023;167:111034.
doi: 10.1016/j.ejrad.2023.111034
pubmed: 37591134
Avidan A, Sprung CL, Schefold JC, Ricou B, Hartog CS, Nates JL, et al. Variations in end-of-life practices in intensive care units worldwide (Ethicus-2): a prospective observational study. Lancet Respiratory Med. 2021;9:1101–10.
doi: 10.1016/S2213-2600(21)00261-7
Denney MJ, Long DM, Armistead MG, Anderson JL, Conway BN. Validating the extract, transform, load process used to populate a large clinical research database. Int J Med Inf. 2016;94:271–4.
doi: 10.1016/j.ijmedinf.2016.07.009
Quiroz JC, Chard T, Sa Z, Ritchie A, Jorm L, Gallego B. Extract, transform, load framework for the conversion of health databases to OMOP. Deserno TM, editor. PLoS ONE. 2022;17:e0266911.
Henke E, Peng Y, Reinecke I, Zoch M, Sedlmayr M, Bathelt F. An extract-transform-load process design for the Incremental Loading of German Real-World Data based on FHIR and OMOP CDM: Algorithm Development and Validation. JMIR Med Inf. 2023;11:e47310.
Unberath P, Prokosch HU, Gründner J, Erpenbeck M, Maier C, Christoph J. EHR-Independent Predictive decision Support Architecture based on OMOP. Appl Clin Inf. 2020;11:399–404.
doi: 10.1055/s-0040-1710393
Fleuren LM, Dam TA, Tonutti M, De Bruin DP, Lalisang RCA, Gommers D, et al. The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients. Crit Care. 2021;25:304.
doi: 10.1186/s13054-021-03733-z
pubmed: 34425864
pmcid: 8381710
Nazer LH, Zatarah R, Waldrip S, Ke JXC, Moukheiber M, Khanna AK et al. Bias in artificial intelligence algorithms and recommendations for mitigation. Kalla M, editor. PLOS Digit Health. 2023;2:e0000278.
McNicholas BA, Madotto F, Pham T, Rezoagli E, Masterson CH, Horie S, Bellani G, Brochard L, Laffey JG, LUNG SAFE Investigators and the ESICM Trials Group. Demographics, management and outcome of females and males with acute respiratory distress syndrome in the LUNG SAFE prospective cohort study. Eur Respir J. 2019;54(4):1900609.
doi: 10.1183/13993003.00609-2019
pubmed: 31346004
Rezoagli E, McNicholas BA, Madotto F, Pham T, Bellani G, Laffey JG, LUNG SAFE Investigators, the ESICM Trials Group. Presence of comorbidities alters management and worsens outcome of patients with acute respiratory distress syndrome: insights from the LUNG SAFE study. Ann Intensive Care. 2022;12(1):42.
doi: 10.1186/s13613-022-01015-7
pubmed: 35596885
pmcid: 9123875
Majid Z, Welch C, Davies J, Jackson T. Global frailty: the role of ethnicity, migration and socioeconomic factors. Maturitas. 2020;139:33–41.
doi: 10.1016/j.maturitas.2020.05.010
pubmed: 32747038
pmcid: 8054560
Bellini V, Montomoli J, Bignami E. Poor quality data, privacy, lack of certifications: the lethal triad of new technologies in intensive care. Intensive Care Med 202147:1052–3.
Agarwal R, Bjarnadottir M, Rhue L, Dugas M, Crowley K, Clark J, et al. Addressing algorithmic bias and the perpetuation of health inequities: an AI bias aware framework. Health Policy Technol. 2023;12:100702.
doi: 10.1016/j.hlpt.2022.100702
Floridi L. On human dignity as a Foundation for the right to privacy. Philos Technol. 2016;29:307–12.
doi: 10.1007/s13347-016-0220-8
Pooch EHP, Ballester P, Barros RC et al. Can We Trust Deep Learning Based Diagnosis? The Impact of Domain Shift in Chest Radiograph Classification. In: Petersen J, San José Estépar R, Schmidt-Richberg A, Gerard S, Lassen-Schmidt B, Jacobs C, editors. Thoracic Image Analysis [Internet]. Cham: Springer International Publishing; 2020 [cited 2023 Jun 16]. pp. 74–83. https://link.springer.com/ https://doi.org/10.1007/978-3-030-62469-9_7 .
Zhang A, Xing L, Zou J, Wu JC. Shifting machine learning for healthcare from development to deployment and from models to data. Nat Biomed Eng. 2022;6:1330–45.
doi: 10.1038/s41551-022-00898-y
pubmed: 35788685
Farahani A, Voghoei S, Rasheed K, Arabnia HR. A Brief Review of Domain Adaptation. In: Stahlbock R, Weiss GM, Abou-Nasr M, Yang C-Y, Arabnia HR, Deligiannidis L, editors. Advances in Data Science and Information Engineering [Internet]. Cham: Springer International Publishing; 2021 [cited 2023 Jun 16]. pp. 877–94. https://link.springer.com/ https://doi.org/10.1007/978-3-030-71704-9_65 .
Nelson GS. Bias in Artificial Intelligence. N C Med J. 2019;80:220–2.
pubmed: 31278182
Croskerry P. A Universal Model of Diagnostic reasoning. Acad Med. 2009;84:1022–8.
doi: 10.1097/ACM.0b013e3181ace703
pubmed: 19638766
Kahneman D. Thinking, fast and slow. London: Penguin Books; 2012.
Gaube S, Suresh H, Raue M, Merritt A, Berkowitz SJ, Lermer E, et al. Do as AI say: susceptibility in deployment of clinical decision-aids. Npj Digit Med. 2021;4:31.
doi: 10.1038/s41746-021-00385-9
pubmed: 33608629
pmcid: 7896064
Vicente L, Matute H. Humans inherit artificial intelligence biases. Sci Rep. 2023;13:15737.
doi: 10.1038/s41598-023-42384-8
pubmed: 37789032
pmcid: 10547752
Sujan M, Furniss D, Grundy K, Grundy H, Nelson D, Elliott M, et al. Human factors challenges for the safe use of artificial intelligence in patient care. BMJ Health Care Inf. 2019;26:e100081.
doi: 10.1136/bmjhci-2019-100081
Benanti P. Homo Faber: The Techno-Human condition [Internet]. EDB - Edizioni Dehoniane Bologna; 2018. https://books.google.it/books?id=7-wCEAAAQBAJ .
Adler-Milstein J, Chen JH, Dhaliwal G. Next-generation Artificial Intelligence for diagnosis: from Predicting Diagnostic labels to Wayfinding. JAMA. 2021;326:2467.
doi: 10.1001/jama.2021.22396
pubmed: 34882190
Montomoli J, Rezoagli E, Bellini V, Finazzi S, Bignami EG. A generalized wayfinding paradigm for improving AKI understanding and classification: insights from the Dutch registries. Minerva Anestesiol. 2023.
Celi LA, Cellini J, Charpignon M-L, Dee EC, Dernoncourt F, Eber R et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review. Fraser HS, editor. PLOS Digit Health. 2022;1:e0000022.
Montomoli J, Hilty MP, Ince C. Artificial intelligence in intensive care: moving towards clinical decision support systems. Minerva Anestesiol. 2022.
Blanchard MD, Kleitman S, Aidman E. Are two naïve and distributed heads better than one? Factors influencing the performance of teams in a challenging real-time task. Front Psychol. 2023;14:1042710.
doi: 10.3389/fpsyg.2023.1042710
pubmed: 37251042
pmcid: 10213526
Koriat A. When two heads are better than one and when they can be worse: the amplification hypothesis. J Exp Psychol Gen. 2015;144:934–50.
doi: 10.1037/xge0000092
pubmed: 26168039
Aporta C, Higgs E. Satellite Culture: Global Positioning Systems, Inuit Wayfinding, and the need for a New Account of Technology. Curr Anthropol. 2005;46:729–53.
doi: 10.1086/432651
Cosgriff CV, Stone DJ, Weissman G, Pirracchio R, Celi LA. The clinical artificial intelligence department: a prerequisite for success. BMJ Health Care Inf. 2020;27:e100183.
doi: 10.1136/bmjhci-2020-100183