Evidence map›Paper›PMID 36345294›Full record

ArticleIEEE robotics and automation letters2022

Tactile Robotic Telemedicine for Safe Remote Diagnostics in Times of Corona: System Design, Feasibility and Usability Study.

Abdeldjallil Naceri, Jean Elsner, Mario Trobinger, Hamid Sadeghian, Lars Johannsmeier, Florian Voigt, Xiao Chen, Daniela Macari, Christoph Jahne, Maximilian Berlet and 5 more

Abstract read
In one paragraph

Article in IEEE robotics and automation letters, 2022. 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. Article
  2. 6G in medical robotics: development of network allocation strategies for a telerobotic examination system.International journal of computer assisted radiology and surgery · 2025
    Article
  3. 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

15 authors.

Abdeldjallil NaceriMunich Institute of Robotics and Machine Intelligence, Technical University of Munich 80992 Munich Germany.ORCID https://orcid.org/0000-0002-8832-0978
Jean ElsnerMunich Institute of Robotics and Machine Intelligence, Technical University of Munich 80992 Munich Germany.ORCID https://orcid.org/0000-0003-2691-0099
Mario TrobingerMunich Institute of Robotics and Machine Intelligence, Technical University of Munich 80992 Munich Germany.ORCID https://orcid.org/0000-0002-3492-3431
Hamid SadeghianMunich Institute of Robotics and Machine Intelligence, Technical University of Munich 80992 Munich Germany.ORCID https://orcid.org/0000-0001-8390-3480
Lars JohannsmeierMunich Institute of Robotics and Machine Intelligence, Technical University of Munich 80992 Munich Germany.ORCID https://orcid.org/0000-0003-4599-7636
Florian VoigtMunich Institute of Robotics and Machine Intelligence, Technical University of Munich 80992 Munich Germany.ORCID https://orcid.org/0000-0003-3650-0130
Xiao ChenMunich Institute of Robotics and Machine Intelligence, Technical University of Munich 80992 Munich Germany.ORCID https://orcid.org/0000-0003-1949-4672
Daniela MacariFranka Emika GmbH 80797 Munich Germany.
Christoph JahneFranka Emika GmbH 80797 Munich Germany.ORCID https://orcid.org/0000-0002-1210-438X
Maximilian BerletMITI Group, University Hospital Rechts der IsarTech. University Munich 80992 Munich Germany.ORCID https://orcid.org/0000-0002-4652-0904
Jonas FuchtmannMITI Group, University Hospital Rechts der IsarTech. University Munich 80992 Munich Germany.
Luis FigueredoMunich Institute of Robotics and Machine Intelligence, Technical University of Munich 80992 Munich Germany.ORCID https://orcid.org/0000-0002-0759-3000
Hubertus FeusnerMITI Group, University Hospital Rechts der IsarTech. University Munich 80992 Munich Germany.
Dirk WilhelmMITI Group, University Hospital Rechts der IsarTech. University Munich 80992 Munich Germany.
Sami HaddadinMunich Institute of Robotics and Machine IntelligenceTechnical University of Munich 80992 Munich Germany.ORCID https://orcid.org/0000-0001-7696-4955

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The current crisis surrounding the COVID-19 pandemic demonstrates the amount of responsibility and the workload on our healthcare system and, above all, on the medical staff around the world. In this work, we propose a promising approach to overcome this problem using robot-assisted telediagnostics, which allows medical experts to examine patients from distance. The designed telediagnostic system consists of two robotic arms. Each robot is located at the doctor and patient sites. Such a system enables the doctor to have a direct conversation via telepresence and to examine patients through robot-assisted inspection (guided tactile and audiovisual contact). The proposed bilateral teleoperation system is redundant in terms of teleoperation control algorithms and visual feedback. Specifically, we implemented two main control modes: joint-based and displacement-based teleoperation. The joint-based mode was implemented due to its high transparency and ease of mapping between

Indexed as

Covid-19medical robotstactile robotictelediagnosticsTelemedicine

Identifiers

PMID36345294
PMCPMC9454265

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.