Xizhe Zhang
Biographic Data
| ID | 7964636 |
|---|---|
| NAME | Xizhe Zhang |
| GIVEN NAMES | Xizhe |
| FAMILY NAME | Zhang |
| SIGNATURE | ZHANG X |
| AFFILIATIONS | Nanjing Brain Hospital |
| ORCID | 0000-0002-8684-4591 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Psychiatry in the age of AI: Transforming theory, practice, and medical education
Mental disorders constitute an urgent and escalating global public-health concern. Recent advances in artificial intelligence (AI) have begun to transform both psychiatric theory and clinical practice, generating unprecedented opportunities for precision diagnosis, mechanistic insight and personalized intervention. Here, we present a narrative review that examines the current landscape of AI-enhanced psychiatry, evaluates AI's capacity to refine …
Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning
The vocal acoustic features can not only effectively classify the major depression and the healthy control groups, but also accurately predict the severity of depressive symptoms
Fast and accurate assessment of depression based on voice acoustic features: A cross-sectional and longitudinal study
Voice acoustic features can effectively and rapidly predict the severity of depression, providing a low-cost and efficient method for screening patients with depression on a large scale. Our study also identified potential acoustic features that may be significantly related to specific treatment options for depression
No prominent works on this page.
Fast and accurate assessment of depression based on voice acoustic features: A cross-sectional and longitudinal study
Voice acoustic features can effectively and rapidly predict the severity of depression, providing a low-cost and efficient method for screening patients with depression on a large scale. Our study also identified potential acoustic features that may be significantly related to specific treatment options for depression
Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning
The vocal acoustic features can not only effectively classify the major depression and the healthy control groups, but also accurately predict the severity of depressive symptoms
Psychiatry in the age of AI: Transforming theory, practice, and medical education
Mental disorders constitute an urgent and escalating global public-health concern. Recent advances in artificial intelligence (AI) have begun to transform both psychiatric theory and clinical practice, generating unprecedented opportunities for precision diagnosis, mechanistic insight and personalized intervention. Here, we present a narrative review that examines the current landscape of AI-enhanced psychiatry, evaluates AI's capacity to refine …
Audiology (2 works) · Clinical Psychology (2 works) · Clinical Psychology (2 works) · Depression (economics (2 works) · Emotion and Mood Recognition (2 works) · Medicine (2 works) · Psychiatry (2 works) · Psychology (2 works) · Anxiety (1 works) · Artificial Intelligence (1 works)