{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T20:12:18Z","timestamp":1772827938251,"version":"3.50.1"},"reference-count":44,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,1]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>Overuse of radiography studies and underuse of conservative therapies for cranial deformities in neonates is a known inefficiency in pediatric craniofacial healthcare. This study sought to establish whether the introduction of artificial intelligence (AI)\u2013generated craniometrics and craniometric interpretations into craniofacial clinical workflow improved resource utilization patterns in the initial evaluation and management of neonatal cranial deformities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>A retrospective chart review of pediatric patients referred for head shape concerns between January 2019 and June 2023 was conducted. Patient demographics, final encounter diagnosis, review of an AI analysis, and provider orders were documented. Patients were divided based on whether an AI cranial deformity analysis was documented as reviewed during the index evaluation, then both groups were propensity matched. Rates of index-encounter radiology studies, physical therapy (PT), orthotic therapy, and craniofacial specialist follow-up evaluations were compared using logistic regression and ANOVA analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>One thousand patient charts were reviewed (663 conventional encounters, 337 AI-assisted encounters). One-to-one propensity matching was performed between these groups. AI models were significantly more likely to be reviewed during telemedicine encounters and advanced practice provider (APP) visits (54.8% telemedicine vs 11.4% in-person, p &lt; 0.0001; 12.3% physician vs 44.4% APP, p &lt; 0.0001). All AI diagnoses of craniosynostosis versus benign deformities were congruent with final diagnoses. AI model review was associated with a significant increase in the use of orthotic therapies for neonatal cranial deformities (31.5% vs 38.6%, p = 0.0132) but not PT or specialist follow-up evaluations. Radiology ordering rates did not correlate with AI-interpreted data review.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>As neurosurgeons and pediatricians continue to work to limit neonatal radiation exposure and contain healthcare costs, AI-assisted clinical care could be a cheap and easily scalable diagnostic adjunct for reducing reliance on radiography and encouraging adherence to established clinical guidelines. In practice, however, providers appear to default to preexisting diagnostic biases and underweight AI-generated data and interpretations, ultimately negating any potential advantages offered by AI. AI engineers and specialty leadership should prioritize provider education and user interface optimization to improve future adoption of validated AI diagnostic tools.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2024.12.peds24429","type":"journal-article","created":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T20:37:11Z","timestamp":1743280631000},"page":"517-525","source":"Crossref","is-referenced-by-count":1,"title":["Healthcare resource utilization for the management of neonatal head shape deformities: a propensity-matched analysis of AI-assisted and conventional approaches"],"prefix":"10.3171","volume":"35","author":[{"given":"Jimin","family":"Shin","sequence":"first","affiliation":[{"name":"School of Medicine, University of Connecticut, Farmington;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabrielle","family":"Caron","sequence":"additional","affiliation":[{"name":"School of Medicine, University of Connecticut, Farmington;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Petronella","family":"Stoltz","sequence":"additional","affiliation":[{"name":"Division of Neurosurgery, Connecticut Children\u2019s, Hartford; and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan E.","family":"Martin","sequence":"additional","affiliation":[{"name":"Division of Neurosurgery, Connecticut Children\u2019s, Hartford; and"},{"name":"Department of Surgery, UConn School of Medicine, Hartford, Connecticut"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David S.","family":"Hersh","sequence":"additional","affiliation":[{"name":"Division of Neurosurgery, Connecticut Children\u2019s, Hartford; and"},{"name":"Department of Surgery, UConn School of Medicine, Hartford, Connecticut"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus J.","family":"Bookland","sequence":"additional","affiliation":[{"name":"Division of Neurosurgery, Connecticut Children\u2019s, Hartford; and"},{"name":"Department of Surgery, UConn School of Medicine, Hartford, Connecticut"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1754","reference":[{"issue":"2","key":"b1","series-title":"Pediatrics","first-page":"298","article-title":"The incidence of positional plagiocephaly: a cohort study","volume":"132","author":"Mawji A","year":"2013","unstructured":"Mawji A, Vollman AR, Hatfield J, McNeil DA, Sauv\u00e9 R. The incidence of positional plagiocephaly: a cohort study. Pediatrics. 2013;132(2):298\u2013304."},{"issue":"5","key":"b2","series-title":"J Neurosurg Pediatr","first-page":"581","article-title":"Image processing and machine learning for telehealth craniosynostosis screening in newborns","volume":"27","author":"Bookland MJ","year":"2021","unstructured":"Bookland MJ, Ahn ES, Stoltz P, Martin JE. Image processing and machine learning for telehealth craniosynostosis screening in newborns. J Neurosurg Pediatr. 2021;27(5):581\u2013588."},{"issue":"5","key":"b3","series-title":"Plast Reconstr Surg","first-page":"1261","article-title":"Evidence-based medicine: craniosynostosis","volume":"133","author":"Fearon JA","year":"2014","unstructured":"Fearon JA. Evidence-based medicine: craniosynostosis. Plast Reconstr Surg. 2014;133(5):1261\u20131275."},{"issue":"3","key":"b4","series-title":"Transl Pediatr","first-page":"247","article-title":"Endoscopic craniosynostosis repair","volume":"3","author":"Proctor MR","year":"2014","unstructured":"Proctor MR. Endoscopic craniosynostosis repair. Transl Pediatr. 2014;3(3):247\u2013258."},{"issue":"22","key":"b5","series-title":"N Engl J Med","first-page":"2277","article-title":"Computed tomography\u2014an increasing source of radiation exposure","volume":"357","author":"Brenner DJ","year":"2007","unstructured":"Brenner DJ, Hall EJ. Computed tomography\u2014an increasing source of radiation exposure. N Engl J Med. 2007;357(22):2277\u20132284."},{"issue":"8","key":"b6","series-title":"JAMA Pediatr","first-page":"700","article-title":"The use of computed tomography in pediatrics and the associated radiation exposure and estimated cancer risk","volume":"167","author":"Miglioretti DL","year":"2013","unstructured":"Miglioretti DL, Johnson E, Williams A,  The use of computed tomography in pediatrics and the associated radiation exposure and estimated cancer risk. JAMA Pediatr. 2013;167(8):700\u2013707."},{"issue":"4","key":"b7","series-title":"Pediatrics","first-page":"971","article-title":"Children, computed tomography radiation dose, and the As Low As Reasonably Achievable (ALARA) concept","volume":"112","author":"Slovis TL","year":"2003","unstructured":"Slovis TL. Children, computed tomography radiation dose, and the As Low As Reasonably Achievable (ALARA) concept. Pediatrics. 2003;112(4):971\u2013972."},{"issue":"2","key":"b8","series-title":"AJR Am J Roentgenol","first-page":"655","article-title":"Reducing radiation dose associated with pediatric CT by decreasing unnecessary examinations","volume":"184","author":"Donnelly LF","year":"2005","unstructured":"Donnelly LF. Reducing radiation dose associated with pediatric CT by decreasing unnecessary examinations. AJR Am J Roentgenol. 2005;184(2):655\u2013657."},{"issue":"5","key":"b9","series-title":"Neurosurgery","first-page":"613","article-title":"Machine learning-driven clinical image analysis to identify craniosynostosis: a pilot study of telemedicine and clinic patients","volume":"90","author":"Paro M","year":"2022","unstructured":"Paro M, Lambert WA, Leclair NK,  Machine learning-driven clinical image analysis to identify craniosynostosis: a pilot study of telemedicine and clinic patients. 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J Neurosurg Pediatr. 2021;27(5):581\u2013588."},{"issue":"5","key":"b3","series-title":"Plast Reconstr Surg","first-page":"1261","article-title":"Evidence-based medicine: craniosynostosis","volume":"133","author":"Fearon JA","year":"2014","unstructured":"Fearon JA. Evidence-based medicine: craniosynostosis. Plast Reconstr Surg. 2014;133(5):1261\u20131275."},{"issue":"3","key":"b4","series-title":"Transl Pediatr","first-page":"247","article-title":"Endoscopic craniosynostosis repair","volume":"3","author":"Proctor MR","year":"2014","unstructured":"Proctor MR. Endoscopic craniosynostosis repair. Transl Pediatr. 2014;3(3):247\u2013258."},{"issue":"22","key":"b5","series-title":"N Engl J Med","first-page":"2277","article-title":"Computed tomography\u2014an increasing source of radiation exposure","volume":"357","author":"Brenner DJ","year":"2007","unstructured":"Brenner DJ, Hall EJ. Computed tomography\u2014an increasing source of radiation exposure. 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Pediatrics. 2003;112(4):971\u2013972."},{"issue":"2","key":"b8","series-title":"AJR Am J Roentgenol","first-page":"655","article-title":"Reducing radiation dose associated with pediatric CT by decreasing unnecessary examinations","volume":"184","author":"Donnelly LF","year":"2005","unstructured":"Donnelly LF. Reducing radiation dose associated with pediatric CT by decreasing unnecessary examinations. AJR Am J Roentgenol. 2005;184(2):655\u2013657."},{"issue":"5","key":"b9","series-title":"Neurosurgery","first-page":"613","article-title":"Machine learning-driven clinical image analysis to identify craniosynostosis: a pilot study of telemedicine and clinic patients","volume":"90","author":"Paro M","year":"2022","unstructured":"Paro M, Lambert WA, Leclair NK,  Machine learning-driven clinical image analysis to identify craniosynostosis: a pilot study of telemedicine and clinic patients. 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Plast Reconstr Surg. 2007;119(6):1866\u20131873."},{"key":"b15","article-title":"State of Connecticut Department of Social Services. CT Access Monitoring Review Plan, Appendix 2: Access Analysis for Payment Reduction Medicaid State Plan Amendments","unstructured":"State of Connecticut Department of Social Services. CT Access Monitoring Review Plan, Appendix 2: Access Analysis for Payment Reduction Medicaid State Plan Amendments. State of Connecticut Department of Social Services.Accessed January 3, 2025. https:\/\/portal.ct.gov\/-\/media\/departments-and-agencies\/dss\/medicaid-access-monitoring-review-plan\/ct_amrp_appendix_2.pdf"},{"key":"b16","article-title":"Artificial Intelligence (AI) in Pathology. College of American Pathologists","unstructured":"Artificial Intelligence (AI) in Pathology. College of American Pathologists. 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Promises and perils of artificial intelligence in neurosurgery. Neurosurgery. 2020;87(1):33\u201344."},{"issue":"5","key":"b19","series-title":"Editorial. J Neurosurg","first-page":"1211","article-title":"AtlasGPT: dawn of a new era in neurosurgery for intelligent care augmentation, operative planning, and performance","volume":"140","author":"Hopkins BS","year":"2024","unstructured":"Hopkins BS, Carter B, Lord J, Rutka JT, Cohen-Gadol AA. AtlasGPT: dawn of a new era in neurosurgery for intelligent care augmentation, operative planning, and performance. Editorial. J Neurosurg. 2024;140(5):1211\u20131214."},{"issue":"2","key":"b20","series-title":"Pediatrics","first-page":"e20152230","article-title":"Cranial ultrasound as a first-line imaging examination for craniosynostosis","volume":"137","author":"Rozovsky K","year":"2016","unstructured":"Rozovsky K, Udjus K, Wilson N, Barrowman NJ, Simanovsky N, Miller E. Cranial ultrasound as a first-line imaging examination for craniosynostosis. Pediatrics. 2016;137(2):e20152230."},{"issue":"5","key":"b21","series-title":"Pediatr Radiol","first-page":"285","article-title":"Imaging modalities of craniosynostosis with surgical and pathological correlation","volume":"15","author":"Gellad FE","year":"1985","unstructured":"Gellad FE, Haney PJ, Sun JC, Robinson WL, Rao KC, Johnston GS. Imaging modalities of craniosynostosis with surgical and pathological correlation. Pediatr Radiol. 1985;15(5):285\u2013290."},{"issue":"10","key":"b22","series-title":"JAMA Netw Open","first-page":"e2440969","article-title":"Large language model influence on diagnostic reasoning: a randomized clinical trial","volume":"7","author":"Goh E","year":"2024","unstructured":"Goh E, Gallo R, Hom J,  Large language model influence on diagnostic reasoning: a randomized clinical trial. 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