Evidence map›Paper›PMID 42147073›Full record

ReviewCyborg and bionic systems (Washington, D.C.)2026

Embodied Cross-Domain Intelligence in Biomedical Microrobots: A Review.

Zongcai Tan, Lan Wei, Kangyi Lu, Dandan Zhang

Abstract readReview
In one paragraph

Review in Cyborg and bionic systems (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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

4 authors.

Zongcai TanDepartment of Bioengineering, Imperial-X AI Initiative, Imperial College London W12 0BZ, London, UK.ORCID https://orcid.org/0009-0005-3671-8254
Lan WeiDepartment of Bioengineering, Imperial-X AI Initiative, Imperial College London W12 0BZ, London, UK.
Kangyi LuDepartment of Bioengineering, Imperial-X AI Initiative, Imperial College London W12 0BZ, London, UK.
Dandan ZhangDepartment of Bioengineering, Imperial-X AI Initiative, Imperial College London W12 0BZ, London, UK.ORCID https://orcid.org/0000-0001-7649-7605

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microrobots are emerging as transformative tools for biomedical applications, including minimally invasive diagnostics, targeted therapy, and microsurgical intervention. However, achieving reliable performance and adaptation to diverse tasks and environments requires capabilities that exceed any single form of intelligence. Here, we introduce embodied cross-domain intelligence, a framework for synergistic coupling across physical, biological, computational, and human intelligence, enabling multifunctional, collaborative, and adaptive microrobotic behavior in dynamic, safety-critical biological settings. Unlike prior reviews that address these intelligence domains in isolation, this review aims to provide a unified framework, outlining each domain's principles, recent advances, and limitations; analyzing the interfaces that foster synergy; and mapping representative domain combinations to major biomedical applications. We further identify core challenges in integration, control, safety, and validation and outline future research directions to accelerate clinical translation. By framing biomedical microrobot development as a cross-domain synergy challenge, this review aims to guide interdisciplinary efforts toward systems capable of executing complex, multistage tasks across their operational life cycle. The associated project is available on the online project page (https://nuounuou.github.io/Embodied-Cross-Domain-Intelligence-in-Biomedical-Microrobots-A-Review/).

Identifiers

PMID42147073
PMCPMC13172582

What Socratic holds

Textmetadata
LicenceCC BY
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.