Akhil Vaid

4.0k total citations · 2 hit papers
48 papers, 1.2k citations indexed

About

Akhil Vaid is a scholar working on Cardiology and Cardiovascular Medicine, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Akhil Vaid has authored 48 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Cardiology and Cardiovascular Medicine, 13 papers in Artificial Intelligence and 8 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Akhil Vaid's work include ECG Monitoring and Analysis (9 papers), Machine Learning in Healthcare (9 papers) and Cardiovascular Function and Risk Factors (9 papers). Akhil Vaid is often cited by papers focused on ECG Monitoring and Analysis (9 papers), Machine Learning in Healthcare (9 papers) and Cardiovascular Function and Risk Factors (9 papers). Akhil Vaid collaborates with scholars based in United States, Israel and Germany. Akhil Vaid's co-authors include Girish N. Nadkarni, B. Keith Jenkins, Rama Chellappa, Benjamin S. Glicksberg, Alexander W. Charney, Eyal Klang, Edgar Argulian, Vera Sorin, Dana Brin and Jagat Narula and has published in prestigious journals such as Nature, Circulation and Nature Medicine.

In The Last Decade

Akhil Vaid

38 papers receiving 1.1k 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 foundation model for clinician-centered drug repurposing

2024 72 citations Kexin Huang, Payal Chandak et al. Nature Medicine profile →
Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study

2025 15 citations Evangelos K. Oikonomou, Akhil Vaid et al. The Lancet Digital Health profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Akhil Vaid United States	16	311	286	264	203	202	48	1.2k
Abdelǎali Hassaïne United Kingdom	16	137 0.4×	508 1.8×	62 0.2×	295 1.5×	66 0.3×	37	1.1k
Michael Moor Switzerland	12	90 0.3×	608 2.1×	384 1.5×	92 0.5×	394 2.0×	25	1.5k
Alistair Shilton Australia	14	97 0.3×	414 1.4×	124 0.5×	118 0.6×	124 0.6×	36	1.3k
Benjamin Shickel United States	15	167 0.5×	837 2.9×	288 1.1×	64 0.3×	225 1.1×	58	1.5k
Lu Huang China	14	333 1.1×	149 0.5×	66 0.3×	136 0.7×	718 3.6×	57	1.5k
Mauricio Villarroel United Kingdom	18	912 2.9×	509 1.8×	39 0.1×	85 0.4×	215 1.1×	35	2.3k
Timothy J. W. Dawes United Kingdom	17	531 1.7×	165 0.6×	76 0.3×	534 2.6×	615 3.0×	39	1.5k
Inga Strümke Norway	9	96 0.3×	271 0.9×	144 0.5×	102 0.5×	168 0.8×	35	892
Ali Jalali United States	16	138 0.4×	361 1.3×	50 0.2×	170 0.8×	49 0.2×	55	943
Fang Chen China	19	34 0.1×	218 0.8×	65 0.2×	204 1.0×	399 2.0×	94	1.4k

Countries citing papers authored by Akhil Vaid

Since Specialization

Citations

This map shows the geographic impact of Akhil Vaid'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 Akhil Vaid with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akhil Vaid more than expected).

Fields of papers citing papers by Akhil Vaid

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Akhil Vaid. 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 Akhil Vaid. The network helps show where Akhil Vaid may publish in the future.

Co-authorship network of co-authors of Akhil Vaid

This figure shows the co-authorship network connecting the top 25 collaborators of Akhil Vaid. A scholar is included among the top collaborators of Akhil Vaid 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 Akhil Vaid. Akhil Vaid is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Lampert, Joshua, Deepak L. Bhatt, Akhil Vaid, et al.. (2025). Calibration of ECG-Based Deep-Learning Algorithm Scores for Patients Flagged as High Risk for Hypertrophic Cardiomyopathy. NEJM AI. 2(5). 3 indexed citations

Lee, Denise, Akhil Vaid, Robert Freeman, et al.. (2025). Using Large Language Models to Automate Data Extraction From Surgical Pathology Reports: Retrospective Cohort Study. JMIR Formative Research. 9. e64544–e64544.

Holmes, Emma, Akhil Vaid, Alexander W. Charney, et al.. (2025). InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records. Nature Communications. 16(1). 8475–8475.

Oikonomou, Evangelos K., Akhil Vaid, Gregory Holste, et al.. (2025). Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study. The Lancet Digital Health. 7(2). e113–e123. 15 indexed citations breakdown →

Mayourian, Joshua, William La Cava, Akhil Vaid, et al.. (2024). Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease. Journal of the American College of Cardiology. 84(9). 815–828. 13 indexed citations

Vaid, Akhil, et al.. (2024). A Hybrid Framework for Dynamic Clustering and Anomaly Detection in SAP ERP Systems. International Journal of Computer Science and Mobile Computing. 13(12). 23–34.

Huang, Kexin, Payal Chandak, Qianwen Wang, et al.. (2024). A foundation model for clinician-centered drug repurposing. Nature Medicine. 30(12). 3601–3613. 72 indexed citations breakdown →

Jayaraman, Pushkala, Brian Y. Soong, Alexandra S. Reynolds, et al.. (2024). Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension. npj Digital Medicine. 7(1). 233–233. 5 indexed citations

Sharma, Jyotirmay, Akhil Vaid, Girish N. Nadkarni, & Monica Kraft. (2024). Diagnosis of Chronic Obstructive Pulmonary Disease Using Deep-learning on Electrocardiograms. A4522–A4522.

10.

Mayourian, Joshua, William La Cava, Akhil Vaid, et al.. (2024). Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling. Circulation. 149(12). 917–931. 24 indexed citations

11.

Vaid, Akhil, Joy Jiang, Stamatios Lerakis, et al.. (2023). A foundational vision transformer improves diagnostic performance for electrocardiograms. npj Digital Medicine. 6(1). 108–108. 58 indexed citations

12.

Vaid, Akhil, Mayte Suárez‐Fariñas, Sanjeev Kaul, et al.. (2023). Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings. Annals of Internal Medicine. 176(10). 1358–1369. 23 indexed citations

13.

Duong, Son Q., Akhil Vaid, Ha My T. Vy, et al.. (2023). Quantitative Prediction of Right Ventricular Size and Function From the ECG. Journal of the American Heart Association. 13(1). e031671–e031671. 8 indexed citations

14.

Vaid, Akhil, Edgar Argulian, Stamatios Lerakis, et al.. (2023). Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction. SHILAP Revista de lepidopterología. 3(1). 24–24. 15 indexed citations

15.

Vaid, Akhil & Chetan Sharma. (2023). Data-driven predictive maintenance and analytics in SAP environments enhanced by machine learning. World Journal of Advanced Research and Reviews. 17(2). 926–932.

16.

Vaid, Akhil & Chetan Sharma. (2022). Leveraging SAP and Artificial Intelligence for optimized enterprise resource planning: enhancing efficiency, automation, and decision-making. World Journal of Advanced Research and Reviews. 14(3). 762–769. 1 indexed citations

17.

Chaudhary, Kumardeep, Akhil Vaid, Áine Duffy, et al.. (2020). Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury. Clinical Journal of the American Society of Nephrology. 15(11). 1557–1565. 66 indexed citations

18.

Russak, Adam, Farhan Chaudhry, Jessica K. De Freitas, et al.. (2020). Machine Learning in Cardiology—Ensuring Clinical Impact Lives Up to the Hype. Journal of Cardiovascular Pharmacology and Therapeutics. 25(5). 379–390. 12 indexed citations

19.

Sharma, Chetan & Akhil Vaid. (2020). The role of SAP in supporting the retail industry through pandemic-induced (COVID-19) challenges. International Journal of Science and Research Archive. 1(1). 96–109. 1 indexed citations

20.

Mehta, Sameer, et al.. (2019). Abstract 15866: Beyond Apple 4 Watch and Af - Is Artificial Intelligence Ready to Take on ST Elevation Mi Diagnosis?. Circulation. 1 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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