Lisha Ai
Impact in
- Oncology top 5%
- CAR-T cell therapy research
- Inflammatory Biomarkers in Disease Prognosis
- Hematology top 5%
- Multiple Myeloma Research and Treatments
Papers in
- Hematology 11
- Multiple Myeloma Research and Treatments 8
- Oncology 21
- CAR-T cell therapy research 5
- Inflammatory Biomarkers in Disease Prognosis 5
- Peptidase Inhibition and Analysis 4
- Bone health and treatments 3
- Co-authors
- Yu HuShidai MuChunyan SunYou QinFengjuan FanZhang‐Bo ChuHeng MeiLei Chen
- Journals
- Cancer Cell International (4 papers)Frontiers in Immunology (2 papers)Annals of Medicine (2 papers)International Journal of Cancer (2 papers)International Journal of Molecular Sciences (2 papers)
- Partner nations
- ChinaUnited StatesSaudi Arabia
In The Last Decade
Lisha Ai
45 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 111
- Oncology 577
- Hematology 219
- Cancer Research 264
- Biomaterials 160
- Immunology 248
Countries citing papers authored by Lisha Ai
This map shows the geographic impact of Lisha Ai'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 Lisha Ai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lisha Ai more than expected).
Fields of papers citing papers by Lisha Ai
This network shows the impact of papers produced by Lisha Ai. 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 Lisha Ai. The network helps show where Lisha Ai may publish in the future.
Co-authors
The 25 scholars most cited alongside Lisha Ai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 2 | |
| 2 | 2024 | 6 | |
| 3 | 2024 | 0 | |
| 4 | 2023 | 16 | |
| 5 | 2022 | 11 | |
| 6 | 2021 | 11 | |
| 7 | 2021 | 13 | |
| 8 | 2021 | 6 | |
| 9 | 2020 | 2 | |
| 10 | 2020 | 14 | |
| 11 | 2020 | 12 | |
| 12 | 2019 | 67 | |
| 13 | 2019 | 91 | |
| 14 | 2019 | 122 | |
| 15 | 2018 | 74 | |
| 16 | 2018 | 18 | |
| 17 | 2016 | 48 | |
| 18 | 2012 | 179 | |
| 19 | 2012 | 36 | |
| 20 | 2011 | 30 |
About Lisha Ai
Lisha Ai is a scholar working on Hematology, Oncology, Surfaces, Coatings and Films, Biomaterials and Immunology, having authored 46 papers that have together received 1.5k indexed citations. Recurring topics across this work include Multiple Myeloma Research and Treatments (8 papers), Silk-based biomaterials and applications (5 papers), CAR-T cell therapy research (5 papers), Inflammatory Biomarkers in Disease Prognosis (5 papers), Lymphoma Diagnosis and Treatment (5 papers), Peptidase Inhibition and Analysis (4 papers), Polymer Surface Interaction Studies (4 papers) and Bone health and treatments (3 papers). The work is most often cited by research in Oncology (577 citations), Hematology (219 citations), Cancer Research (264 citations), Biomaterials (160 citations) and Immunology (248 citations). Lisha Ai has collaborated with scholars based in China, United States and Saudi Arabia. Frequent co-authors include Yu Hu, Shidai Mu, Chunyan Sun, You Qin, Fengjuan Fan, Zhang‐Bo Chu, Heng Mei, Lei Chen, Huafang Wang and Yadan Wang. Their work appears in journals such as Cancer Cell International, Frontiers in Immunology, Annals of Medicine, International Journal of Cancer and International Journal of Molecular Sciences.
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.