Adam Yala
- Artificial Intelligence top 1%
- Radiology, Nuclear Medicine and Imaging top 2%
- Pulmonary and Respiratory Medicine top 10%
- Oncology top 10%
- Health Informatics top 0.5%
- Co-authors
- Regina BarzilayConstance D. LehmanTal SchusterBrian N. DontchosRandy C. MilesKevin S. HughesManisha BahlKyle Swanson
- Topics
- AI in cancer detection (12 papers)Global Cancer Incidence and Screening (8 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)
- Partner nations
- United StatesTaiwanChile
In The Last Decade
Adam Yala
22 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 111
- Artificial Intelligence 920
- Radiology, Nuclear Medicine and Imaging 746
- Pulmonary and Respiratory Medicine 332
- Oncology 272
- Health Informatics 256
Countries citing papers authored by Adam Yala
This map shows the geographic impact of Adam Yala'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 Adam Yala with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adam Yala more than expected).
Fields of papers citing papers by Adam Yala
This network shows the impact of papers produced by Adam Yala. 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 Adam Yala. The network helps show where Adam Yala may publish in the future.
Co-authorship network of co-authors of Adam Yala
This figure shows the co-authorship network connecting the top 25 collaborators of Adam Yala. A scholar is included among the top collaborators of Adam Yala 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 Adam Yala. Adam Yala is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomographybreakdown → | 114 |
| 2 | 1 | |
| 3 | 57 | |
| 4 | 135 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 33 | |
| 8 | 4 | |
| 9 | 14 | |
| 10 | 10 | |
| 11 | 27 | |
| 12 | 1 | |
| 13 | 4 | |
| 14 | A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Predictionbreakdown → | 442 |
| 15 | 20 | |
| 16 | 22 | |
| 17 | 1 | |
| 18 | 188 | |
| 19 | 72 | |
| 20 | 76 |
About Adam Yala
Adam Yala is a scholar working on Health Informatics, Artificial Intelligence and Oncology, having authored 22 papers that have together received 1.4k indexed citations. Recurring topics across this work include AI in cancer detection (12 papers), Global Cancer Incidence and Screening (8 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). The work is most often cited by research in Health Informatics (256 citations), Radiology, Nuclear Medicine and Imaging (746 citations) and Artificial Intelligence (920 citations). Adam Yala has collaborated with scholars based in United States, Taiwan and Chile. Frequent co-authors include Regina Barzilay, Constance D. Lehman, Tal Schuster, Brian N. Dontchos, Randy C. Miles, Kevin S. Hughes, Manisha Bahl, Kyle Swanson, Yung‐Liang Wan and Karthik Narasimhan. Their work appears in journals such as Nature Medicine, Journal of Clinical Oncology and Cancer Research.
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.