Evidence map›Paper›PMID 42281165›Full record

ArticleBiochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology

Evaluation of DeepSeek-Generated Biochemistry Clinical Cases for Teaching Purpose.

Shuai Tan, Shijie Ni, Kun Zhang, Yuhan Yang, Hao Tan, Liang Li, Yiran Sun, Quekun Peng

Abstract read
In one paragraph

Article in Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology. 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.

Shuai TanDepartment of Endocrinology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Chongqing, China.ORCID https://orcid.org/0000-0003-4268-6429
Shijie NiSchool of Finance and Accounting, Chengdu Jincheng College, Chengdu, China.
Kun ZhangSchool of Biosciences and Technology, Sichuan Higher Education Institute Key Laboratory of Major Disease Target Discovery and Protein Drug Development, Chengdu Medical College, Chengdu, China.ORCID https://orcid.org/0000-0001-5891-0400
Yuhan YangSchool of Biosciences and Technology, Sichuan Higher Education Institute Key Laboratory of Major Disease Target Discovery and Protein Drug Development, Chengdu Medical College, Chengdu, China.
Hao TanSchool of Biosciences and Technology, Sichuan Higher Education Institute Key Laboratory of Major Disease Target Discovery and Protein Drug Development, Chengdu Medical College, Chengdu, China.
Liang LiTeaching Center of General Courses, Chengdu Medical College, Chengdu, China.
Yiran SunSchool of Pharmacy, Key Laboratory of Sichuan Province for Specific Structure of Small Molecule Drugs, Chengdu Medical College, Chengdu, China.ORCID https://orcid.org/0000-0002-5574-6518
Quekun PengSchool of Biosciences and Technology, Sichuan Higher Education Institute Key Laboratory of Major Disease Target Discovery and Protein Drug Development, Chengdu Medical College, Chengdu, China.ORCID https://orcid.org/0000-0002-4065-1137

Funding

2024 Teaching Reform Research Foundation of Chengdu Medical College (Key Project) JG2024065National First-Class Undergraduate Course Construction Project 2023232793Natural Science Foundation of Chengdu Medical College CYZYB24-02
6 · The paper itself

Abstract

To overcome limitations in traditional case-based teaching, this study developed an AI-driven workflow using DeepSeek to generate contemporary, interdisciplinary clinical biochemistry cases. Through human-AI collaboration, eight structured cases covering key topics such as carbohydrate and lipid metabolism were created, each including a clinical description, molecular mechanisms, and Q&A, followed by instructor review. Fifteen medical students and 15 instructors evaluated the cases using a 5-point Likert scale across multiple dimensions, while student performance was compared between a group using AI-generated cases and a control group. The AI generated each case in 10-15 min, significantly faster than manual development. Cases presented a logical progression from molecular mechanism to clinical management and incorporated recent advances such as CRISPR and PCSK9 inhibitors. Content integration received the highest ratings, though instructors scored pedagogical applicability lower. Error analysis indicated that AI excelled in maintaining logical consistency, whereas human reviewers enhanced precision in clinical details. Students who used the AI-generated cases achieved significantly higher examination scores than the control group (p < 0.05). In conclusion, DeepSeek-generated cases are efficient, interdisciplinary, and innovative. Human review remains essential for ensuring clinical rigor, particularly in nuanced scenarios. This collaborative approach enhances both the efficiency of case development and the educational quality of biochemistry teaching materials.

Indexed as

Artificial IntelligenceBiochemistryTeachingEducational MeasurementHumansStudents, MedicalAI‐human collaborative optimization modelartificial intelligenceclinical case writingmedical education

Identifiers

PMID42281165
PMCPMC13418689

What OpenQuestion holds

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Registered trials

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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.