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This observational study evaluates whether audio recordings of patient–physician dialogue during routine visits can identify cognitive impairment using acoustic speech features and machine learning. Audio was recorded with a microphone and portable device, and an external validation cohort was recruited in a separate city. 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The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.  \nIt is made available under a CC-BY 4.0 International license .  \nAcoustic Analysis of Primary Care Patient-provider Conversations to Screen for Cognitive Impairment  \n1 Title:  \n2 Acoustic Analysis of Primary Care Patient-provider Conversations to Screen for Cognitive  \n3 Impairment  \n4 Authors: 5  \n6 Joseph T. Colonel, PhD, 1 Jacqueline Becker, PhD, 2 Lili Chan, MD, 2 Cara Faherty, MS, 2  \n7 Tielman van Vleck, PhD,3 Laura Curtis, PhD, 4 Juan Wisnivesky, MD, DrPH, 2 Alex Federman, 8 MD, MPH,2* and Baihan Lin, PhD 1*  \n9  \n10 *co-senior authors  \n11 1Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA 12 2Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York,  \n13 NY, USA  \n14 3Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY,  \n15 USA  \n16 4Division of General Internal Medicine, Feinberg School of Medicine at Northwestern  \n17 University, Chicago, IL, USA 18  \n19 Word Count:  \n20 2937 words  \n21  \n22 Corresponding Author:  \n23 Joseph T Colonel, PhD  \n24 [joseph.colonel@mssm.edu](joseph.colonel@mssm.edu)  \n25  \n26  \n27 Icahn School of Medicine at Mount Sinai 28 3 East 101st Street,  \n29 New York, NY 10029  \n30  \n31  \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.  \nmedRxiv preprint doi: [https://doi.org/10.64898/2025.12.27.25343088](https://doi.org/10.64898/2025.12.27.25343088); this version posted December 29, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.  \nIt is made available under a CC-BY 4.0 International license .  \nAcoustic Analysis of Primary Care Patient-provider Conversations to Screen for Cognitive Impairment  \n32  \n33 Key Points  \n34 Question: Can acoustic features extracted from audio recordings of patient–physician  \n35 conversations during routine primary care visits be used to screen for cognitive impairment?  \n36 Findings: In this study including 787 older adults without diagnosis of cognitive problems,  \n37 machine learning models trained on acoustic features from audio segments of recordings of  \n38 primary care visits achieved area under the receiver operating characteristic curve values of 0.72  \n39 for predicting cognitive impairment. The algorithm achieved a sensitivity of 83%, specificity of  \n40 44%, and positive predictive value of 28%, identifying a subset of primary care patients at higher  \n41 risk for cognitive impairment. Models performed similarly on an external validation dataset of  \n42 179 participants. Interpretability analyses highlighted patient pause duration and energy-related  \n43 features as salient indicators of cognition status.  \n44 Meaning: These findings suggest that short segments of naturalistic clinical dialogue may  \n45 contain useful acoustic signals for passively screening patients for cognitive impairment. 46  \n47  \n48  \nmedRxiv preprint doi: [https://doi.org/10.64898/2025.12.27.25343088](https://doi.org/10.64898/2025.12.27.25343088); this version posted December 29, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.  \nIt is made available under a CC-BY 4.0 International license .  \nAcoustic Analysis of Primary Care Patient-provider Conversations to Screen for Cognitive Impairment  \n49 Abstract  \n50 Importance  \n51 Cognitive impairment (CI) is often under detected in primary care due to time and","cbCaioxyQZZY3whD","https://ap.wps.com/l/cbCaioxyQZZY3whD","pdf",469060,31,"English","# Key Points\n## Question\n## Findings\n## Meaning\n# Abstract\n## Importance\n## Objective\n## Design\n## Setting\n## Participants\n## Exposures\n## Main Outcomes and Measures","[{\"question\":\"What question does the study address?\",\"answer\":\"It tests whether acoustic features from audio recordings of patient–physician conversations during routine primary care can be used to screen for cognitive impairment.\"},{\"question\":\"How was cognitive impairment identified in the study?\",\"answer\":\"Cognitive impairment was assessed using acoustic speech features extracted from multiple short speech segments and classified with machine learning models.\"},{\"question\":\"What did the models perform like and what signals were most informative?\",\"answer\":\"In 787 older adults, the models achieved an AUC of 0.72, with sensitivity of 83% and specificity of 44%. Interpretability analyses indicated patient pause duration and energy-related features as salient indicators.\"}]","Acoustic Analysis of Primary Care Patient-provider Conversations to Screen for Cognitive Impairment | PDF",1790709059,78]