Lisa Adams

Short CV

Lisa Adams studied medicine at Charité - Universitätsmedizin Berlin, completing her MD thesis in 2016. She received board certification in Diagnostic and Interventional Radiology in 2021, followed by a postdoctoral fellowship at Stanford University (2022-2023). Since July 2023, she has been an Attending Radiologist at TUM University Hospital. She completed her 'Habilitation' in Experimental Radiology at Charité in 2020 and transferred this qualification to TUM in 2024. She has secured funding from the German Research Foundation (DFG), European Union, Wilhelm Sander Foundation, Bayern Innovativ, and Berlin Institute of Health. Committed to mentoring, she has supervised seven doctoral students to completion since 2020, with two more nearing thesis submission. From 2021 to 2022, she was an active member of the Commission for Young Academics at Charité. In addition, she serves as a Scientific Editor for European Radiology and as a Trainee Editorial Board Member for Radiology: Artificial Intelligence. Lisa Adams is also on the executive board of the AG Methodik & Forschung of the German Radiological Society (DRG).

Selected Awards

• 2023: Walter-Friedrich-Prize, Deutsche Röntgengesellschaft (DRG)

• 2021: Alavi Mandell Award

• 2017: Invest in the Youth Stipend, ECR, Vienna

Research Interests

Lisa Adams works on AI for radiology diagnostics. She integrates oncological and molecular imaging knowledge with machine learning to improve diagnosis across different organ systems. Her research covers both preclinical and clinical areas, developing AI methods to detect diseases early, assess risks, and personalize care. She focuses on enhancing quantitative imaging biomarkers and multimodal analysis, connecting research to clinical use. Her work includes areas like body composition analysis and estimating organ-specific biological age to better assess health and make predictions. She emphasizes ethical, patient-centered approaches in healthcare.

Selected Publications

Google Scholar Profil: https://scholar.google.de/citations?user=n-AEUgsAAAAJ&hl=en&oi=ao

1.       Adams LC, Truhn D, Busch F, Dorfner F, Nawabi J, Makowski MR, Llama 3 Challenges Proprietary State-of-the-Art Large Language Models in Radiology Board-style Examination Question, Radiology, 2024, In Press.

2.       Han T*, Adams LC*, Bressem KK, Busch F, Nebelung S, Truhn D, Comparative Analysis of Multimodal Large Language Model Performance on Clinical Vignette Questions, JAMA (Journal of the American Medical Association), 2024; doi: 10.1001/jama.2023.27861. *Shared first-authorship.

3.       Busch F, Bressem KK, Suwalski P, Hoffmann L, Niehues SM, Poddubnyy D, Makowski MR, Aerts HJWL, Zhukov A, Adams LC, Open Access Data and Deep Learning for Cardiac Device Identification on Standard DICOM and Smartphone-based Chest Radiographs, Radiology: Artificial Intelligence, 2024; doi: 10.1148/ryai.230502.

4.       Suryadevara* V, Hajipour MJ*, Adams LC*, Aissaoui NM, Rashidi A, Kiru L, Theruvath AJ, Huang CH, Maruyama M, Tsubosaka M, Lyons JK, Wu WE, Roudi R, Goodman SB, Daldrup-Link HE, MegaPro, a clinically translatable nanoparticle for in vivo tracking of stem cell implants in pig cartilage defects, Theranostics, 2023; doi: 10.7150/thno.82620. *Shared first-authorship

5.       Adams LC, Truhn D, Busch F, Kader A, Niehues SM, Makowski MR, Bressem KK, Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study, Radiology, 2023; doi: 10.1148/radiol.230725 (4^th most cited publication in Radiology in 2023)

6.       Makowski MR*, Bressem KK*, Franz L, Kader A, Niehues SM, Keller S, Rueckert D, Adams LC, De Novo Radiomics Approach Using Image Augmentation and Features From T1 Mapping to Predict Gleason Scores in Prostate Cancer. Investigative Radiology. 2021; doi: 10.1097/RLI.0000000000000788.

7.       Niehues SM*, Adams LC*, Gaudin RA, Erxleben C, Keller S, Makowski MR, Vahldiek JL, Bressem KK, Deep-learning based diagnosis of bedside chest X-ray in intensive care and emergency medicine, Investigative Radiology, 2021; doi: 10.1097/RLI.0000000000000771. *Shared first-authorship

8.       Petersen A, Bressem K, Albrecht J, Thieß HM, Vahldiek J, Hamm B, Makowski MR, Niehues A, Niehues SM, Adams LC. The role of visceral adiposity in the severity of COVID-19: Highlights from a unicenter cross-sectional pilot study in Germany, Metabolism: Clinical and Experimental, 2020, doi: 10.1016/j.metabol.2020.154317.

9.       Bressem KK*, Adams LC*, Gaudin RA, Tröltzsch D, Hamm B, Makowski MR, Schüle CY, Vahldiek JL, Niehues SM. Highly accurate classification of chest radiographic reports using a deep learning natural language model pretrained on 3.8 million text reports. Bioinformatics, 2020; doi: 10.1093/bioinformatics/btaa668. *Shared first-authorship.

10.   Adams LC, Ralla B, Jurmeister P, Bressem KK, Fahlenkamp UL, Hamm B, Busch J, Makowski MR. Native T1 Mapping as an In Vivo Biomarker for the Identification of Higher-Grade Renal Cell Carcinoma: Correlation with Histopathological Findings. Investigative Radiology, 2019; doi: 10.1097/RLI.0000000000000515.

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