Evidence map›Paper›PMID 40021674›Full record

ReviewSchizophrenia (Heidelberg, Germany)2025

Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia?

Shijie Jiang, Qiyu Jia, Zhenlei Peng, Qixuan Zhou, Zhiguo An, Jianhua Chen, Qizhong Yi

Abstract readReview
In one paragraph

Review in Schizophrenia (Heidelberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Shijie Jiang *Department of Medical Psychology, the first Affiliated Hospital of Xinjiang Medical University, Xinjiang Clinical Research Center for Mental Health, Urumqi, 830011, Xinjiang, China.
Qiyu Jia *Department of Trauma Orthopaedics, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, Xinjiang, China.
Zhenlei Peng *Department of Medical Psychology, the first Affiliated Hospital of Xinjiang Medical University, Xinjiang Clinical Research Center for Mental Health, Urumqi, 830011, Xinjiang, China.
Qixuan ZhouDepartment of Medical Psychology, the first Affiliated Hospital of Xinjiang Medical University, Xinjiang Clinical Research Center for Mental Health, Urumqi, 830011, Xinjiang, China.
Zhiguo AnDepartment of Medical Psychology, the first Affiliated Hospital of Xinjiang Medical University, Xinjiang Clinical Research Center for Mental Health, Urumqi, 830011, Xinjiang, China. 37989344@qq.com.
Jianhua ChenShanghai Institute of Traditional Chinese Medicine for Mental Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China. jianhua.chen@smhc.org.cn.
Qizhong YiDepartment of Medical Psychology, the first Affiliated Hospital of Xinjiang Medical University, Xinjiang Clinical Research Center for Mental Health, Urumqi, 830011, Xinjiang, China. qizhongyi@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluated the potential of artificial intelligence (AI) in the diagnosis, treatment, and prognostic assessment of schizophrenia (SZ) and explored collaborative directions for AI applications in future medical innovations. SZ is a severe mental disorder that causes significant suffering and imposes challenges on patients. With the rapid advancement of machine learning and deep learning technologies, AI has demonstrated notable advantages in the early diagnosis of high-risk populations. By integrating multidimensional biomarkers and linguistic behavior data of patients, AI can provide further objective and precise diagnostic criteria. Moreover, it aids in formulating personalized treatment plans, enhancing therapeutic outcomes, and offering new therapeutic strategies for patients with treatment-resistant SZ. Furthermore, AI excels in developing individualized prognostic plans, which enables the rapid identification of disease progression, accurate prediction of disease trajectory, and timely adjustment of treatment strategies, thereby improving prognosis and facilitating recovery. Despite the immense potential of AI in SZ management, its role as an auxiliary tool must be emphasized, with clinical judgment and compassionate care from healthcare professionals remaining crucial. Future research should focus on optimizing human-machine interactions to achieve efficient AI application in SZ management. The in-depth integration of AI technology into clinical practice will advance the field of SZ, ultimately improving the quality of life and treatment outcomes of patients.

Identifiers

PMID40021674
PMCPMC11871033

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.