Update README.md

README.md

@@ -25,9 +25,9 @@ As widespread and prevalent as depression is, identifying and treating depressio
 ## Model Overview
 
 DAM is a clinical-grade, speech-based model designed to screen for signs of depression and anxiety using voice biomarkers.
-To the best of our knowledge, it is the first model developed explicitly for clinical-grade mental health assessment from speech without reliance on linguistic content or transcription.
+To the best of our knowledge, it is the first model developed explicitly for clinical-grade mental health assessment from speech without reliance on linguistic content or transcription and has been peer-reviewed in the largest voice biomarker study by the Annals of Family Medicine [5].
 The model operates exclusively on the acoustic properties of the speech signal, extracting depression- and anxiety-specific voice biomarkers rather than semantic or lexical information.
-Numerous studies [5–7] have demonstrated that paralinguistic features – such as spectral entropy, pitch variability, fundamental frequency, and related acoustic measures – exhibit strong correlations with depression and anxiety.
+Numerous studies [6–8] have demonstrated that paralinguistic features – such as spectral entropy, pitch variability, fundamental frequency, and related acoustic measures – exhibit strong correlations with depression and anxiety.
 Building on this body of evidence, DAM extends prior approaches by leveraging deep learning to learn fine-grained vocal biomarkers directly from the raw speech signal, yielding representations that demonstrate greater predictive power than hand-engineered paralinguistic features.
 DAM analyzes spoken audio to estimate depression and anxiety severity scores which can be subsequently mapped to standardized clinical scales, such as **PHQ-9** (Patient Health Questionnaire-9) for depression and **GAD-7** (Generalized Anxiety Disorder-7) for anxiety.
 
@@ -205,7 +205,7 @@ print(f"Mean absolute error predicting GAD sum on test set based on thresholds o
 
 # Acknowledgments
 
-This model was created through equal contributions by Oleksii Abramenko, Noah Stein, and Colin Vaz while at Kintsugi Health. For a full list of contributors to earlier modeling projects, data collection, clinical, and business matters, see the organization card at https://huggingface.co/KintsugiHealth.
+This model was created through equal contributions by Oleksii Abramenko, Noah Stein, and Colin Vaz during their work at Kintsugi Health. It builds on years of prior modeling, data collection, clinical research, and operational efforts by a broader team. A full list of contributors is available on the Kintsugi Health organization card at https://huggingface.co/KintsugiHealth.
 
 # References
 
@@ -213,6 +213,7 @@ This model was created through equal contributions by Oleksii Abramenko, Noah St
 2. https://www.hopefordepression.org/depression-facts/  
 3. https://nndc.org/facts/  
 4. https://www.psychiatry.org/patients-families/stigma-and-discrimination
-5. https://www.sciencedirect.com/science/article/pii/S1746809423004536
-6. https://pmc.ncbi.nlm.nih.gov/articles/PMC3409931/
-7. https://pmc.ncbi.nlm.nih.gov/articles/PMC11559157
+5. https://www.annfammed.org/content/early/2025/01/07/afm.240091
+6. https://www.sciencedirect.com/science/article/pii/S1746809423004536
+7. https://pmc.ncbi.nlm.nih.gov/articles/PMC3409931/
+8. https://pmc.ncbi.nlm.nih.gov/articles/PMC11559157