Asma Salhi

904 total citations · 1 hit paper
15 papers, 456 citations indexed

About

Asma Salhi is a scholar working on Surgery, Epidemiology and Biomedical Engineering. According to data from OpenAlex, Asma Salhi has authored 15 papers receiving a total of 456 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Surgery, 7 papers in Epidemiology and 6 papers in Biomedical Engineering. Recurrent topics in Asma Salhi's work include Shoulder Injury and Treatment (9 papers), Shoulder and Clavicle Injuries (7 papers) and COVID-19 diagnosis using AI (3 papers). Asma Salhi is often cited by papers focused on Shoulder Injury and Treatment (9 papers), Shoulder and Clavicle Injuries (7 papers) and COVID-19 diagnosis using AI (3 papers). Asma Salhi collaborates with scholars based in Australia, France and South Africa. Asma Salhi's co-authors include Mohammed A. Fadhel, Laith Alzubaidi, Yuantong Gu, José Santamaría, A. S. Albahri, Chun Ouyang, Ashish Gupta, Jinshuai Bai, O. S. Albahri and Alhamzah Alnoor and has published in prestigious journals such as PLoS ONE, Journal of Shoulder and Elbow Surgery and Information Fusion.

In The Last Decade

Asma Salhi

12 papers receiving 437 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion

2023 372 citations A. S. Albahri, Mohammed A. Fadhel et al. Information Fusion profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Asma Salhi Australia	6	185	124	68	51	46	15	456
Katharina Holzinger Austria	7	160 0.9×	124 1.0×	50 0.7×	57 1.1×	49 1.1×	8	458
Arnaldo Mendez Canada	5	99 0.5×	108 0.9×	80 1.2×	99 1.9×	27 0.6×	7	418
Claudia Mazo Ireland	9	332 1.8×	170 1.4×	159 2.3×	25 0.5×	14 0.3×	21	624
Arturo Moncada‐Torres Netherlands	9	156 0.8×	38 0.3×	92 1.4×	54 1.1×	23 0.5×	15	440
Daniel Rückert Germany	8	450 2.4×	194 1.6×	205 3.0×	89 1.7×	17 0.4×	17	821
Gorka Epelde Spain	10	236 1.3×	58 0.5×	29 0.4×	36 0.7×	6 0.1×	45	509
Oleg Sysoev Sweden	9	71 0.4×	53 0.4×	32 0.5×	95 1.9×	16 0.3×	34	477
André Carrington Canada	3	203 1.1×	100 0.8×	59 0.9×	11 0.2×	7 0.2×	8	352
Nazish Khalid Pakistan	5	98 0.5×	95 0.8×	49 0.7×	34 0.7×	5 0.1×	12	291
Ashwin Belle United States	12	122 0.7×	38 0.3×	63 0.9×	122 2.4×	79 1.7×	28	643

Countries citing papers authored by Asma Salhi

Since Specialization

Citations

This map shows the geographic impact of Asma Salhi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Asma Salhi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Asma Salhi more than expected).

Fields of papers citing papers by Asma Salhi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Asma Salhi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Asma Salhi. The network helps show where Asma Salhi may publish in the future.

Co-authorship network of co-authors of Asma Salhi

This figure shows the co-authorship network connecting the top 25 collaborators of Asma Salhi. A scholar is included among the top collaborators of Asma Salhi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Asma Salhi. Asma Salhi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

Salhi, Asma, et al.. (2025). Trustworthy deep learning for the automated quantification of the fatty infiltration of the rotator cuff muscles using magnetic resonance imaging. JSES International. 9(6). 1999–2007.

Alzubaidi, Laith, Asma Salhi, Mohammed A. Fadhel, et al.. (2024). Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images. PLoS ONE. 19(3). e0299545–e0299545. 19 indexed citations

Alzubaidi, Laith, Mohammed A. Fadhel, Asma Salhi, et al.. (2024). SSP: self-supervised pertaining technique for classification of shoulder implants in x-ray medical images: a broad experimental study. Artificial Intelligence Review. 57(10). 10 indexed citations

Gupta, Ashish, Ruth Delaney, Asma Salhi, et al.. (2024). Medial scapular body (MSB) Goutallier classification: MRI-based reliability and validity of evaluation of the Goutallier classification for grading fatty infiltration of the rotator cuff. Journal of Shoulder and Elbow Surgery. 33(12). e641–e651. 2 indexed citations

Salhi, Asma, et al.. (2024). Comparison of range of motion between 2-year clinical outcomes and predictions of a static scapula preoperative planning software for reverse shoulder arthroplasty. Seminars in Arthroplasty JSES. 34(3). 770–778.

Salhi, Asma, et al.. (2023). Does forearm referencing using a retroversion guide achieve the targeted retroversion of the humeral component in reverse shoulder arthroplasty?. Journal of Shoulder and Elbow Surgery. 33(7). 1555–1562.

Albahri, A. S., Mohammed A. Fadhel, Alhamzah Alnoor, et al.. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion. 96. 156–191. 372 indexed citations breakdown →

Salhi, Asma, et al.. (2023). Restoration of glenoid joint line: a three-dimensional analysis of scapular landmarks. JSES International. 7(3). 478–484. 6 indexed citations

Alzubaidi, Laith, Mohammed A. Fadhel, A. S. Albahri, et al.. (2023). Domain Adaptation and Feature Fusion for the Detection of Abnormalities in X-Ray Forearm Images. PubMed. 2023. 1–5. 5 indexed citations

10.

Tewari, Ravi Prakash, et al.. (2022). A Musculoskeletal Model Customized for Sagittal and Frontal Knee Kinematics With Improved Knee Joint Stability. Journal of Biomechanical Engineering. 144(7). 2 indexed citations

11.

Salhi, Asma, et al.. (2022). Single-Stage Revision Reverse Shoulder Arthroplasty: Preoperative Planning, Surgical Technique, and Mixed Reality Execution. Journal of Clinical Medicine. 11(24). 7422–7422. 5 indexed citations

12.

Dardenne, Guillaume, et al.. (2020). Statistical Shape Modeling to Determine the Anterior Pelvic Plane for Total Hip Arthroplasty. PubMed. 2020. 1364–1367. 1 indexed citations

13.

Salhi, Asma, Valérie Burdin, Sylvain Brochard, Tinashe Mutsvangwa, & Bhushan Borotikar. (2020). Clinical relevance of augmented statistical shape model of the scapula in the glenoid region. Medical Engineering & Physics. 76(1). 88–94. 2 indexed citations

14.

Salhi, Asma, et al.. (2019). Statistical Shape Modeling Approach to Predict Missing Scapular Bone. Annals of Biomedical Engineering. 48(1). 367–379. 24 indexed citations

15.

Salhi, Asma, et al.. (2017). Subject-specific shoulder muscle attachment region prediction using statistical shape models: A validity study. PubMed. 2017. 1640–1643. 8 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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