Adam Yala

2.7k total citations · 2 hit papers
22 papers, 1.4k citations indexed

About

Adam Yala is a scholar working on Artificial Intelligence, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Adam Yala has authored 22 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 10 papers in Oncology and 5 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Adam Yala's 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). Adam Yala is often cited by papers focused on AI in cancer detection (12 papers), Global Cancer Incidence and Screening (8 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Adam Yala collaborates with scholars based in United States, Taiwan and Chile. Adam Yala's 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 and has published in prestigious journals such as Nature Medicine, Journal of Clinical Oncology and Cancer Research.

In The Last Decade

Adam Yala

22 papers receiving 1.3k 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 Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction

2019 442 citations Adam Yala, Constance D. Lehman et al. Radiology profile →
Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography

2023 114 citations Peter G. Mikhael, Jeremy Wohlwend et al. Journal of Clinical Oncology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Adam Yala United States	13	920	746	332	272	256	22	1.4k
Narmin Ghaffari Laleh Germany	15	603 0.7×	542 0.7×	125 0.4×	260 1.0×	400 1.6×	20	1.3k
Tal Schuster United States	9	732 0.8×	493 0.7×	225 0.7×	172 0.6×	174 0.7×	17	982
Robert MacDonald Australia	6	517 0.6×	429 0.6×	254 0.8×	159 0.6×	146 0.6×	15	986
Shidan Wang United States	20	559 0.6×	550 0.7×	376 1.1×	337 1.2×	103 0.4×	45	1.5k
Jana Lipková United States	14	561 0.6×	662 0.9×	118 0.4×	136 0.5×	257 1.0×	30	1.4k
Meyke Hermsen Netherlands	13	1.1k 1.2×	734 1.0×	227 0.7×	225 0.8×	125 0.5×	22	1.6k
Jonas Teuwen Netherlands	15	671 0.7×	962 1.3×	268 0.8×	120 0.4×	180 0.7×	62	1.4k
Yongbei Zhu China	14	418 0.5×	1.2k 1.7×	559 1.7×	320 1.2×	129 0.5×	25	1.5k
Thomas de Bel Netherlands	10	641 0.7×	459 0.6×	245 0.7×	159 0.6×	103 0.4×	16	1.0k
Vera Sorin Israel	17	462 0.5×	564 0.8×	219 0.7×	85 0.3×	544 2.1×	59	1.2k

Countries citing papers authored by Adam Yala

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Mikhael, Peter G., Jeremy Wohlwend, Adam Yala, et al.. (2023). Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography. Journal of Clinical Oncology. 41(12). 2191–2200. 114 indexed citations breakdown →

Harrison, Jon M., Adam Yala, Peter G. Mikhael, et al.. (2023). Successful Development of a Natural Language Processing Algorithm for Pancreatic Neoplasms and Associated Histologic Features. Pancreas. 52(4). e219–e223. 1 indexed citations

Yala, Adam, Peter G. Mikhael, Constance D. Lehman, et al.. (2022). Optimizing risk-based breast cancer screening policies with reinforcement learning. Nature Medicine. 28(1). 136–143. 57 indexed citations

Yala, Adam, Peter G. Mikhael, Fredrik Strand, et al.. (2021). Toward robust mammography-based models for breast cancer risk. Science Translational Medicine. 13(578). 135 indexed citations

Ortiz‐López, Rocío, Regina Barzilay, Víctor Treviño, et al.. (2021). COVID-19 classification using thermal images. 1–5. 2 indexed citations

Lehman, Constance D., Adam Yala, Leslie R. Lamb, & Regina Barzilay. (2021). Abstract SP080: Hidden clues in the mammogram: How AI can improve early breast cancer detection. Cancer Research. 81(4_Supplement). SP080–SP080. 1 indexed citations

Dontchos, Brian N., et al.. (2020). External Validation of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice. Academic Radiology. 28(4). 475–480. 33 indexed citations

Santus, Enrico, Tal Schuster, Amir Tahmasebi, et al.. (2020). Exploiting Rules to Enhance Machine Learning in Extracting Information From Multi-Institutional Prostate Pathology Reports. JCO Clinical Cancer Informatics. 4(4). 865–874. 4 indexed citations

Tang, Rong, Francisco Acevedo, Suzanne B. Coopey, et al.. (2019). Incidental breast carcinoma: incidence, management, and outcomes in 4804 bilateral reduction mammoplasties. Breast Cancer Research and Treatment. 177(3). 741–748. 14 indexed citations

10.

Coopey, Suzanne B., Adam Yala, Regina Barzilay, et al.. (2019). Atypical ductal hyperplasia in men with gynecomastia: what is their breast cancer risk?. Breast Cancer Research and Treatment. 175(1). 1–4. 10 indexed citations

11.

Yala, Adam, Tal Schuster, Regina Barzilay, et al.. (2019). Deep Learning Model to Assess Cancer Risk on the Basis of a Breast MR Image Alone. American Journal of Roentgenology. 213(1). 227–233. 27 indexed citations

12.

Acevedo, Francisco, Zhengyi Deng, Rong Tang, et al.. (2019). Incidental atypical hyperplasia/LCIS in mammoplasty specimens and subsequent risk of breast cancer.. Journal of Clinical Oncology. 37(15_suppl). 1561–1561. 1 indexed citations

13.

Arbour, Kathryn C., Luu Anh Tuan, Hira Rizvi, et al.. (2019). ml-RECIST: Machine learning to estimate RECIST in patients with NSCLC treated with PD-(L)1 blockade.. Journal of Clinical Oncology. 37(15_suppl). 9052–9052. 4 indexed citations

14.

Yala, Adam, et al.. (2019). A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction. Radiology. 292(1). 60–66. 442 indexed citations breakdown →

15.

Acevedo, Francisco, Zhengyi Deng, Rong Tang, et al.. (2018). Pathologic findings in reduction mammoplasty specimens: a surrogate for the population prevalence of breast cancer and high-risk lesions. Breast Cancer Research and Treatment. 173(1). 201–207. 20 indexed citations

16.

Tang, Rong, Lizhi Ouyang, Yue He, et al.. (2018). Machine learning to parse breast pathology reports in Chinese. Breast Cancer Research and Treatment. 169(2). 243–250. 22 indexed citations

17.

Acevedo, Francisco, Rong Tang, Suzanne B. Coopey, et al.. (2018). Pathologic findings in reduction mammoplasty procedures identified by natural language processing of breast pathology reports: A surrogate for the population incidence of cancer and high risk lesions.. Journal of Clinical Oncology. 36(15_suppl). e13569–e13569. 1 indexed citations

18.

Lehman, Constance D., Adam Yala, Tal Schuster, et al.. (2018). Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation. Radiology. 290(1). 52–58. 188 indexed citations

19.

Yala, Adam, Regina Barzilay, Laura Salama, et al.. (2016). Using machine learning to parse breast pathology reports. Breast Cancer Research and Treatment. 161(2). 203–211. 72 indexed citations

20.

Narasimhan, Karthik, Adam Yala, & Regina Barzilay. (2016). Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning. 2355–2365. 76 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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