Jiacheng Xie

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Unclaimed · AI-structured profileOpenAlex

First author of a real-world benchmark pitting frontier LLMs against TCM physicians in blinded expert evaluation

papers here

reviewed by Evolve

first-author

corresponding

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works · OpenAlex

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h-index · OpenAlex

Bibliometric figures come from OpenAlex (2026-09-21) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.

Research profile

Output—

No output metrics are available yet: the snapshot contains no publication counts, so this axis cannot be scored. It would measure the volume and cadence of Xie's published papers, preprints, and other research outputs over time.

Impact—

Impact cannot be scored: no citation counts, h-index, or usage signals are present in the input data. This axis would measure how widely Xie's work is cited, adopted, and referenced across the clinical AI and medical informatics literature.

Focus100/100

The focus score is 100 out of 100, derived from a field distribution in which the sole field assignment is medical informatics / clinical AI evaluation — one paper, one field. Every signal in the current snapshot points to a single direction: evaluating LLMs against physicians in traditional Chinese medicine. With only one cataloged paper, this perfect concentration should be read as an early-stage specialization signal, not a long-term pattern.

Collaboration51/100

Collaboration scores 51 out of 100, with 12 distinct coauthors recorded in the bibliometric snapshot. The frequent-coauthor list — including Xiaoting Tang, Yang Yu, Jinpu Li, Shouli Li, Congcong Jing, Yantao Yang, Zhiyong Zhao, Qilin Song, Guanghui An, and Dong Xu — indicates a sizeable team assembled around the TCM benchmark study. The mid-range score reflects moderate breadth built on a still-small publication base of one cataloged paper.

Activity—

Activity cannot be scored: beyond one 2026 paper, the snapshot gives no publication timeline, so publishing cadence is unknown. This axis would measure recent output rate — papers per year and ongoing preprint activity — to show whether the 2026 study starts a sustained program.

Reproducibility55/100

Reproducibility scores 55, computed as the mean over exactly one assessed paper (reproPapers = 1, reproMean = 55), whose Evolve reproducibility readiness is rated medium. The paper's verdict names concrete reproducibility risks — possible blinding leakage, single-run inference, and a self-evaluated model — so the mid-range score fits: the benchmark is documented but sensitive to evaluation-protocol details. More assessed papers are needed before this score is stable.

Research summary

Jiacheng Xie is an early-stage researcher profiled here from public bibliographic data. The Evolve catalog indexes one paper with Xie as first author: a 2026 arXiv study, "Large Language Models Versus Physicians in Traditional Chinese Medicine: A Real-World Clinical Case Evaluation." The work benchmarks frontier LLMs against traditional Chinese medicine (TCM) physicians on real clinical cases, scored through blinded expert review and including prescription analysis, placing Xie in medical informatics and clinical AI evaluation. Evolve's verdict credits the study with a striking headline finding — frontier LLMs outperforming TCM physicians on blinded expert scores — while flagging three methodological risks: possible blinding leakage, single-run inference, and a self-evaluated model. The bibliometric snapshot shows an extremely concentrated field profile (focus 100/100, with the sole field assignment in clinical AI evaluation), a moderate collaboration footprint (score 51, built on 12 distinct coauthors including Xiaoting Tang, Yang Yu, Jinpu Li, and Dong Xu), and a reproducibility score of 55 from one assessed paper rated medium for reproducibility readiness. Output, impact, and activity axes cannot be scored because no publication counts or citation data are present, and no institution, country, or ORCID is attached to this profile.

Collaboration pattern

The snapshot records 12 distinct coauthors and a collaboration score of 51. The recurring group is one large team — Xiaoting Tang, Yang Yu, Jinpu Li, Shouli Li, Congcong Jing, Yantao Yang, Zhiyong Zhao, Ziyang Zhang, Qilin Song, Guanghui An, and Dong Xu — consistent with a single multidisciplinary TCM benchmark study rather than multiple ongoing partnerships. Broader collaboration patterns cannot be confirmed from one cataloged paper.

Research keywords

large language modelstraditional Chinese medicineclinical decision supportphysician comparisonblinded expert reviewprescription analysisreal-world clinical case evaluationmedical informaticsclinical AI evaluationbenchmark methodology

Highlights

Frontier LLMs outscore TCM physicians in blinded real-case evaluation
In the 2026 first-author arXiv study, frontier large language models beat traditional Chinese medicine physicians on blinded expert scores across real clinical cases, with prescription analysis among the evaluation targets. Evolve's verdict treats this as the paper's striking headline finding.
Expert review tempers the headline with three methodological risks
Evolve's review flags possible blinding leakage, single-run inference, and a self-evaluated model as caveats on the headline result. The paper's reproducibility readiness is rated medium, consistent with its reproducibility score of 55 derived from one assessed paper.
A first-author entry point into clinical AI evaluation
The study is Xie's first-authored, non-corresponding contribution and carries the snapshot's sole field assignment — medical informatics / clinical AI evaluation — anchoring this early-stage profile in LLM-for-medicine benchmarking with a team of 12 distinct coauthors.

Timeline

2026First-author arXiv preprint published comparing frontier LLMs with traditional Chinese medicine physicians on real clinical cases, using blinded expert review and prescription analysis.
2026Evolve reviews the study: the headline finding — LLMs ahead of TCM physicians on blinded expert scores — is confirmed but tempered by blinding-leakage, single-run inference, and self-evaluation risks.
2026Bibliometric snapshot records 12 distinct coauthors, a focus score of 100 on a single field (medical informatics / clinical AI evaluation), and a reproducibility score of 55 from one assessed paper.

Papers in the Evolve catalog

2026-09-17first author
Large Language Models Versus Physicians in Traditional Chinese Medicine: A Real-World Clinical Case Evaluation

Frequent co-authors

Xiaoting Tang×1 Yang Yu×1 Jinpu Li×1 Shouli Li×1 Congcong Jing×1 Yantao Yang×1 Zhiyong Zhao×1 Ziyang Zhang×1 Qilin Song×1 Guanghui An×1 Dong Xu×1 Ziyang Zhang×1

About this profile

This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.

Profile is name-resolved from public bibliographic data; "Jiacheng Xie" is a common name, so identity matching carries uncertainty.
All dimension scores rest on one cataloged paper and one reproducibility assessment, so they are early-stage and unstable.
Output, impact, and activity axes are unscored because the input contains no publication counts, citation data, or multi-year timeline.
No institution, country, or ORCID is attached; the coauthor list may double-count homonyms (one name appears twice in the source list).