{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T05:50:53Z","timestamp":1768974653927,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T00:00:00Z","timestamp":1667001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Agency","award":["ANR-17-RHUS-0010"],"award-info":[{"award-number":["ANR-17-RHUS-0010"]}]},{"name":"National Research Agency","award":["2019_11235"],"award-info":[{"award-number":["2019_11235"]}]},{"name":"National Research Agency","award":["754995"],"award-info":[{"award-number":["754995"]}]},{"name":"National Research Agency","award":["ANR-21-RHUS-0014"],"award-info":[{"award-number":["ANR-21-RHUS-0014"]}]},{"name":"National Research Agency","award":["ProjetIA-16-IDEX-0007"],"award-info":[{"award-number":["ProjetIA-16-IDEX-0007"]}]},{"name":"Bourse R\u00e9gion Pays de la Loire","award":["ANR-17-RHUS-0010"],"award-info":[{"award-number":["ANR-17-RHUS-0010"]}]},{"name":"Bourse R\u00e9gion Pays de la Loire","award":["2019_11235"],"award-info":[{"award-number":["2019_11235"]}]},{"name":"Bourse R\u00e9gion Pays de la Loire","award":["754995"],"award-info":[{"award-number":["754995"]}]},{"name":"Bourse R\u00e9gion Pays de la Loire","award":["ANR-21-RHUS-0014"],"award-info":[{"award-number":["ANR-21-RHUS-0014"]}]},{"name":"Bourse R\u00e9gion Pays de la Loire","award":["ProjetIA-16-IDEX-0007"],"award-info":[{"award-number":["ProjetIA-16-IDEX-0007"]}]},{"name":"Research and Innovation Programme","award":["ANR-17-RHUS-0010"],"award-info":[{"award-number":["ANR-17-RHUS-0010"]}]},{"name":"Research and Innovation Programme","award":["2019_11235"],"award-info":[{"award-number":["2019_11235"]}]},{"name":"Research and Innovation Programme","award":["754995"],"award-info":[{"award-number":["754995"]}]},{"name":"Research and Innovation Programme","award":["ANR-21-RHUS-0014"],"award-info":[{"award-number":["ANR-21-RHUS-0014"]}]},{"name":"Research and Innovation Programme","award":["ProjetIA-16-IDEX-0007"],"award-info":[{"award-number":["ProjetIA-16-IDEX-0007"]}]},{"name":"French National Research Agency (Agence Nationale de la Recherche, ANR)","award":["ANR-17-RHUS-0010"],"award-info":[{"award-number":["ANR-17-RHUS-0010"]}]},{"name":"French National Research Agency (Agence Nationale de la Recherche, ANR)","award":["2019_11235"],"award-info":[{"award-number":["2019_11235"]}]},{"name":"French National Research Agency (Agence Nationale de la Recherche, ANR)","award":["754995"],"award-info":[{"award-number":["754995"]}]},{"name":"French National Research Agency (Agence Nationale de la Recherche, ANR)","award":["ANR-21-RHUS-0014"],"award-info":[{"award-number":["ANR-21-RHUS-0014"]}]},{"name":"French National Research Agency (Agence Nationale de la Recherche, ANR)","award":["ProjetIA-16-IDEX-0007"],"award-info":[{"award-number":["ProjetIA-16-IDEX-0007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Machine learning (ML) models have proven their potential in acquiring and analyzing large amounts of data to help solve real-world, complex problems. Their use in healthcare is expected to help physicians make diagnoses, prognoses, treatment decisions, and disease outcome predictions. However, ML solutions are not currently deployed in most healthcare systems. One of the main reasons for this is the provenance, transparency, and clinical utility of the training data. Physicians reject ML solutions if they are not at least based on accurate data and do not clearly include the decision-making process used in clinical practice. In this paper, we present a hybrid human\u2013machine intelligence method to create predictive models driven by clinical practice. We promote the use of quality-approved data and the inclusion of physician reasoning in the ML process. Instead of training the ML algorithms on the given data to create predictive models (conventional method), we propose to pre-categorize the data according to the expert physicians\u2019 knowledge and experience. Comparing the results of the conventional method of ML learning versus the hybrid physician\u2013algorithm method showed that the models based on the latter can perform better. Physicians\u2019 engagement is the most promising condition for the safe and innovative use of ML in healthcare.<\/jats:p>","DOI":"10.3390\/s22218313","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T10:47:57Z","timestamp":1667126877000},"page":"8313","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human\u2013Machine Intelligence"],"prefix":"10.3390","volume":"22","author":[{"given":"Chadia","family":"Ed-Driouch","sequence":"first","affiliation":[{"name":"\u00c9cole Centrale Nantes, IMT Atlantique, Nantes Universit\u00e9, CNRS, LS2N, UMR 6004, F-44000 Nantes, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4140-0049","authenticated-orcid":false,"given":"Franck","family":"Mars","sequence":"additional","affiliation":[{"name":"Centrale Nantes, Nantes Universit\u00e9, CNRS, LS2N, UMR 6004, F-44000 Nantes, France"}]},{"given":"Pierre-Antoine","family":"Gourraud","sequence":"additional","affiliation":[{"name":"Clinique des Donn\u00e9es, P\u00f4le Hospitalo-Universitaire 11: Sant\u00e9 Publique, CHU Nantes, Nantes Universit\u00e9, INSERM, CIC 1413, F-44000 Nantes, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3779-0988","authenticated-orcid":false,"given":"C\u00e9dric","family":"Dumas","sequence":"additional","affiliation":[{"name":"D\u00e9partement Automatique, Productique et Informatique, IMT Atlantique, CNRS, LS2N, UMR CNRS 6004, F-44000 Nantes, France"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ed-Driouch, C., Mars, F., Gourraud, P.-A., and Dumas, C. 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