Evidence map›Paper›PMID 41659054›Full record

ArticleDigital health

Comparing AI and physician medical advice quality and humanistic care on social media platforms.

Zhaorui Wang, Youfu He, Linlin Guo, Yu Qian, Liqiong Ai, Jing Huang, Ruiting Feng, Qiang Wu

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Zhaorui WangTranslational Medicine Research Center, The Fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, China.ORCID https://orcid.org/0009-0009-1244-1827
Youfu HeDepartment of Cardiology, Guizhou Provincial People's Hospital, Guiyang, China.ORCID https://orcid.org/0000-0003-4926-1706
Linlin GuoThe Second Department of Radiotherapy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yu QianDepartment of Cardiology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Liqiong AiDepartment of Dentistry, Guiyang Borui Dental Clinic, Guiyang, China.
Jing HuangDepartment of Cardiology, Guizhou Provincial People's Hospital, Guiyang, China.
Ruiting FengTranslational Medicine Research Center, The Fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, China.
Qiang WuDepartment of Cardiology, Guizhou Provincial People's Hospital, Guiyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To evaluate the level of quality, professionalism, and humanistic care in medical advice given by artificial intelligence (AI) and healthcare experts on social media platforms, with an emphasis on short video content and in-person question-and-answer (Q&A) sessions. Methods: The mDISCERN, GQS, and VIQI scores were used to assess the quality and creator opinion trends of the short videos on AI-assisted diagnosis that were gathered from YouTube, TikTok, and Bilibili. Patient inquiries from Dingxiang Health, a significant Chinese online consultation platform, were selected across medical departments. Simulated answers were produced by Deep Seek R1 and Chat-GPT O3-mini-high, and Bedside Manner Rating were used to evaluate the humanistic and high-quality treatment. Results: Across platforms, 60.8% of videos preferred AI's diagnosis powers above medical professionals (Bilibili: 74.29%, TikTok: 56.67%, YouTube: 53.75%). Bilibili videos, particularly those produced by professionals, scored highest in quality ( Conclusions: Higher-quality medical content is available on Bilibili, and AI shows benefits in terms of humanistic care and diagnostic quality in text-based, simulated consultations. Future research should explore examine how doctors and AI might work together to enhance healthcare delivery, with doctors using digital communication while also exercising professional judgment in real-world clinical settings.

Indexed as

artificial intelligenceempathyhumanistic caremedical advice qualityphysicianshort videoSocial mediasocial media platforms

Identifiers

PMID41659054
PMCPMC12881337

What OpenQuestion holds

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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 OpenQuestion graph.