James Hillis

2.3k total citations
60 papers, 1.4k citations indexed

About

James Hillis is a scholar working on Cognitive Neuroscience, Atomic and Molecular Physics, and Optics and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, James Hillis has authored 60 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Cognitive Neuroscience, 13 papers in Atomic and Molecular Physics, and Optics and 10 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in James Hillis's work include Visual perception and processing mechanisms (19 papers), Color Science and Applications (12 papers) and Color perception and design (9 papers). James Hillis is often cited by papers focused on Visual perception and processing mechanisms (19 papers), Color Science and Applications (12 papers) and Color perception and design (9 papers). James Hillis collaborates with scholars based in United States, United Kingdom and Canada. James Hillis's co-authors include Martin S. Banks, Michael S. Landy, Marc O. Ernst, Simon J. Watt, David H. Brainard, Francis G. Szele, Osama Al‐Dalahmah, Bernardo C. Bizzo, Mayara Vieira Mundim and Massimiliano Di Luca and has published in prestigious journals such as Science, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

James Hillis

52 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James Hillis United States 18 881 325 207 187 169 60 1.4k
Takahiro Kawabe Japan 20 879 1.0× 359 1.1× 284 1.4× 119 0.6× 184 1.1× 114 1.3k
John A. Greenwood United Kingdom 23 1.2k 1.4× 129 0.4× 72 0.3× 32 0.2× 195 1.2× 68 1.8k
Siegfried Wahl Germany 21 455 0.5× 87 0.3× 82 0.4× 247 1.3× 162 1.0× 164 2.5k
Yoshihiro Miyake Japan 23 665 0.8× 166 0.5× 351 1.7× 111 0.6× 215 1.3× 252 2.3k
Sieu K. Khuu Australia 22 670 0.8× 94 0.3× 132 0.6× 74 0.4× 90 0.5× 106 1.4k
Mitchell Tyler United States 27 1.2k 1.4× 281 0.9× 97 0.5× 319 1.7× 78 0.5× 62 2.1k
Johannes Burge United States 17 1.1k 1.2× 244 0.8× 216 1.0× 61 0.3× 177 1.0× 48 1.3k
Toshio Inui Japan 24 1.2k 1.3× 171 0.5× 566 2.7× 39 0.2× 110 0.7× 109 1.9k
Katja Doerschner Türkiye 17 725 0.8× 149 0.5× 336 1.6× 40 0.2× 151 0.9× 62 940
Satoshi Shioiri Japan 20 997 1.1× 135 0.4× 214 1.0× 154 0.8× 278 1.6× 112 1.3k

Countries citing papers authored by James Hillis

Since Specialization
Citations

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

Fields of papers citing papers by James Hillis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James Hillis

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

All Works

20 of 20 papers shown
1.
Mercaldo, Sarah, James Hillis, & Jeffrey D. Blume. (2025). Evaluating the Performance and Clinical Utility of AI-driven Diagnostic Tools in Radiology. Radiology. 317(2). e243935–e243935.
2.
McCarthy, Davis J., et al.. (2025). Artificial Intelligence-Enabled Devices in Neurology: Mapping the Present and Future. Seminars in Neurology. 46(1). 57–66.
3.
Mercaldo, Sarah, Sandeep Hedgire, Nandini M. Meyersohn, et al.. (2025). Evaluation of an artificial intelligence model for opportunistic Agatston scoring on non-gated chest computed tomography. Scientific Reports. 15(1). 39535–39535.
4.
Buch, Karen, John Conklin, William A. Mehan, et al.. (2024). Evaluation of an Artificial Intelligence Model for Identification of Mass Effect and Vasogenic Edema on CT of the Head. American Journal of Neuroradiology. 45(10). 1528–1535. 1 indexed citations
5.
Hillis, James, Sarah Mercaldo, Subba R. Digumarthy, et al.. (2024). The Potential Clinical Utility of an Artificial Intelligence Model for Identification of Vertebral Compression Fractures in Chest Radiographs. Journal of the American College of Radiology. 22(2). 220–229. 1 indexed citations
6.
Hillis, James, Jacob J. Visser, Edward R. Scheffer Cliff, et al.. (2024). The lucent yet opaque challenge of regulating artificial intelligence in radiology. npj Digital Medicine. 7(1). 69–69. 13 indexed citations
7.
Hillis, James, Bernardo C. Bizzo, Sarah Mercaldo, et al.. (2024). Evaluation of an Artificial Intelligence Model for Identification of Intracranial Hemorrhage Subtypes on Computed Tomography of the Head. SHILAP Revista de lepidopterología. 4(4). e001223–e001223. 2 indexed citations
8.
Bizzo, Bernardo C., Lina Karout, Shadi Ebrahimian, et al.. (2023). Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm. Diagnostics. 13(4). 778–778.
9.
Bridge, Christopher P., Bernardo C. Bizzo, James Hillis, et al.. (2022). Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging. Scientific Reports. 12(1). 2154–2154. 12 indexed citations
10.
Bizzo, Bernardo C., Subba R. Digumarthy, Lina Karout, et al.. (2022). Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT. Diagnostics. 12(8). 1844–1844. 3 indexed citations
11.
Hillis, James, Bernardo C. Bizzo, Sarah Mercaldo, et al.. (2022). Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs. JAMA Network Open. 5(12). e2247172–e2247172. 29 indexed citations
12.
McIntosh, Christopher S., et al.. (2019). 3‐1: Psychophysical Evaluation of Persistence‐ and Frequency‐Limited Displays for Virtual and Augmented Reality. SID Symposium Digest of Technical Papers. 50(1). 1–4. 9 indexed citations
13.
Hillis, James, Julie Davies, Mayara Vieira Mundim, Osama Al‐Dalahmah, & Francis G. Szele. (2016). Cuprizone demyelination induces a unique inflammatory response in the subventricular zone. Journal of Neuroinflammation. 13(1). 190–190. 46 indexed citations
14.
Hillis, James, et al.. (2012). ‘Care Factor’: engaging medical students with their well‐being. Medical Education. 46(5). 509–510. 4 indexed citations
15.
Hillis, James, et al.. (2010). Painting the picture: Australasian medical student views on wellbeing teaching and support services. The Medical Journal of Australia. 192(4). 188–190. 26 indexed citations
16.
Schreiber, Kai, et al.. (2008). The surface of the empirical horopter. Journal of Vision. 8(3). 7–7. 41 indexed citations
17.
Hillis, James & David H. Brainard. (2007). Distinct Mechanisms Mediate Visual Detection and Identification. Current Biology. 17(19). 1714–1719. 42 indexed citations
18.
Hillis, James, Simon J. Watt, Michael S. Landy, & Martin S. Banks. (2004). Slant from texture and disparity cues: Optimal cue combination. Journal of Vision. 4(12). 1–1. 337 indexed citations
19.
Banks, Martin S., Tandra Ghose, & James Hillis. (2003). Relative image size, not eye position, determines eye dominance switches. Vision Research. 44(3). 229–234. 30 indexed citations
20.
Hillis, James & Martin S. Banks. (2001). Are corresponding points fixed?. Vision Research. 41(19). 2457–2473. 46 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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