Evidence map›Paper›PMID 37943340›Full record

ArticleAnnals of biomedical engineering2024

Explainable AI Elucidates Musculoskeletal Biomechanics: A Case Study Using Wrist Surgeries.

Isaly Tappan, Erica M Lindbeck, Jennifer A Nichols, Joel B Harley

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of biomedical engineering, 2024. 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. Human-AI Cooperation in Healthcare and Rehabilitation.Delaware journal of public health · 2026
    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.

Isaly TappanDepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA.ORCID http://orcid.org/0009-0000-2003-7896
Erica M LindbeckDepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA.
Jennifer A Nichols *J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA.
Joel B Harley *Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA. joel.harley@ufl.edu.

Funding

A Transfer Learning Framework for Creating Subject-Specific Musculoskeletal Models of the HandR21EB030068 · NIBIB · UNIVERSITY OF FLORIDA · PI NICHOLS, JENNIFER A. · 2020 to 2020
$561k
NIBIB NIH HHS R21 EB030068NIBIB NIH HHS R21EB030068
6 · The paper itself

Abstract

As datasets increase in size and complexity, biomechanists have turned to artificial intelligence (AI) to aid their analyses. This paper explores how explainable AI (XAI) can enhance the interpretability of biomechanics data derived from musculoskeletal simulations. We use machine learning to classify the simulated lateral pinch data as belonging to models with healthy or one of two types of surgically altered wrists. This simulation-based classification task is analogous to using biomechanical movement and force data to clinically diagnose a pathological state. The XAI describes which musculoskeletal features best explain the classifications and, in turn, the pathological states, at both the local (individual decision) level and global (entire algorithm) level. We demonstrate that these descriptions agree with assessments in the literature and additionally identify the blind spots that can be missed with traditional statistical techniques.

Indexed as

Artificial IntelligenceWristAlgorithmsBiomechanical PhenomenaMachine LearningBiomechanicsExplainable artificial intelligenceHandMachine learningMusculoskeletal modeling

Identifiers

PMID37943340
PMCPMC11293275

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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.