Research Summary

Dr. Andreas Rauschecker is a neuroradiologist who cares for both adults and children. He specializes in using advanced imaging technologies, such as CT and MRI, to diagnose nervous system disorders. He also treats patients by means of image-guided procedures and uses his expertise to assist colleagues in managing and treating their patients.

In research, Rauschecker focuses on using modern technology to understand the brain in conditions of health and disease. Specifically, he uses artificial intelligence and advanced image-processing techniques to investigate the brain and its appearance in relation to disease, with the goal of improving the information he can provide to patients and other doctors.

Education

Fellowship in Neuroradiology, 06/2020 - Neuroradiology, University of California, San Francisco
02/2020 - Diversity, Equity, and Inclusion Champion Training, University of California
Residency, 06/2018 - Diagnostic Radiology, University of Pennsylvania
MD PhD, 06/2013 - Neuroscience, Medical School, Stanford University
M.Sc., 08/2005 - Neuroscience, Oxford University
B.S., 06/2004 - Biology & Psychology, Georgetown University

Honors & Awards

  • UCSF Chen Scholar, Tianqiao and Chrissy Chen Institute, 2024-2026
  • UCSF Weill Award for Clinician-Scientists, UCSF Weill Institute for Neurosciences, 2023
  • Rahul Desikan Award, UCSF Department of Radiology, 2021
  • ASNR Trainee Award, 2020
  • RSNA Research Scholar Award, 2020
  • UCSF School of Medicine Inquiry Funding Office Long Term Mentor Award, UCSF, 2020
  • ASNR/ASfNR MIT-E Scholarship, 2019
  • RSNA Roentgen Fellow Research Award, 2019
  • Roger A. Bauman, MD Award, SIIM, 2019
  • ACR-AUR Research Scholar Program, 2018
  • NVIDIA GPU Seed Grant, 2018
  • RSNA Roentgen Resident Research Award, 2018
  • RSNA Resident Research Grant, 2017
  • RSNA Magna Cum Laude Award, 2016
  • RSNA Certificate of Merit Award, 2015
  • Mary Duke Biddle Clinical Scholars Program, Stanford University, 2013
  • Stanford Bio-X Graduate Student Fellowship, Stanford University, 2008
  • Clarendon Fund Scholarship, Oxford University, 2004
  • Howard Hughes Undergraduate Research Scholar, Georgetown University, 2000-2004

Selected Publications

  1. Weinert DA, Rauschecker AM. Enhancing Large Language Models with Retrieval-Augmented Generation: A Radiology-Specific Approach. Radiol Artif Intell. 2025 May; 7(3):e240313.  View on PubMed
  2. Tejani AS, Rauschecker AM. One System to Rule Them All? Task- and Data-specific Considerations for Automated Data Extraction. Radiol Artif Intell. 2025 05; 7(3):e250175.  View on PubMed
  3. Poole S, Sisodia N, Koshal K, Henderson K, Wijangco J, Paredes D, Chen C, Rowles W, Akula A, Wuerfel J, Sharma V, UCSF Multiple Sclerosis and Neuroinflammation Center clinicians, Rauschecker AM, Henry RG, Bove R. Detecting New Lesions Using a Large Language Model: Applications in Real-World Multiple Sclerosis Datasets. Ann Neurol. 2025 Apr 25.  View on PubMed
  4. Geng H, Mo S, Chen L, Ballapuram A, Tsang M, Lu M, Rauschecker AM, Wen KW, Devine WP, Solomon DA, Rubenstein JL. Identification of genomic biomarkers of disease progression and survival in primary CNS lymphoma. Blood Adv. 2025 Mar 11; 9(5):1117-1131.  View on PubMed
  5. Haddadi Avval A, Banerjee S, Zielke J, Kann B, Mueller S, Rauschecker AM. Applications of Artificial Intelligence and Advanced Imaging in Pediatric Diffuse Midline Glioma. Neuro Oncol. 2025 Mar 03.  View on PubMed
  6. Rauschecker AM, Nedelec P, Pan S, Olaru M, Nillo RM, Palmer CE, Pecheva D, Dale AM, Jernigan TL, Sugrue LP. Neurocognitive and brain structure correlates of reading and television habits in early adolescence. Sci Rep. 2025 Feb 20; 15(1):6235.  View on PubMed
  7. Eghbali R, Nedelec P, Weiss D, Bhalerao R, Xie L, Rudie JD, Liu C, Sugrue LP, Rauschecker AM. Automated Lesion and Feature Extraction Pipeline for Brain MRIs with Interpretability. Neuroinformatics. 2025 Jan 09; 23(1):2.  View on PubMed
  8. Moawad AW, Janas A, Baid U, Ramakrishnan D, Saluja R, Ashraf N, Maleki N, Jekel L, Yordanov N, Fehringer P, Gkampenis A, Amiruddin R, Manteghinejad A, Adewole M, Albrecht J, Anazodo U, Aneja S, Anwar SM, Bergquist T, Chiang V, Chung V, Conte GM, Dako F, Eddy J, Ezhov I, Khalili N, Farahani K, Iglesias JE, Jiang Z, Johanson E, Kazerooni AF, Kofler F, Krantchev K, LaBella D, Van Leemput K, Li HB, Linguraru MG, Liu X, Meier Z, Menze BH, Moy H, Osenberg K, Piraud M, Reitman Z, Shinohara RT, Wang C, Wiestler B, Wiggins W, Shafique U, Willms K, Avesta A, Bousabarah K, Chakrabarty S, Gennaro N, Holler W, Kaur M, LaMontagne P, Lin M, Lost J, Marcus DS, Maresca R, Merkaj S, Cassinelli Pedersen G, von Reppert M, Sotiras A, Teytelboym O, Tillmans N, Westerhoff M, Youssef A, Godfrey D, Floyd S, Rauschecker A, Villanueva-Meyer J, Pflüger I, Cho J, Bendszus M, Brugnara G, Cramer J, Perez-Carillo GJG, Johnson DR, Kam A, Kwan BYM, Lai L, Lall NU, Memon F, Krycia M, Patro SN, Petrovic B, So TY, Thompson G, Wu L, Schrickel EB, Bansal A, Barkhof F, Besada C, Chu S, Druzgal J, Dusoi A, Farage L, Feltrin F, Fong A, Fung SH, Gray RI, Ikuta I, Iv M, Postma AA, Mahajan A, Joyner D, Krumpelman C, Letourneau-Guillon L, Lincoln CM, Maros ME, Miller E, Morón FEA, Nimchinsky EA, Ozsarlak O, Patel U, Rohatgi S, Saha A, Sayah A, Schwartz ED, Shih R, Shiroishi MS, Small JE, Tanwar M, Valerie J, Weinberg BD, White ML, Young R, Zohrabian VM, Azizova A, Brüßeler MMT, Ghonim M, Ghonim M, Okar A, Pasquini L, Sharifi Y, Singh G, Sollmann N, Soumala T, Taherzadeh M, Vollmuth P, Foltyn-Dumitru M, Malhotra A, Abayazeed AH, Dellepiane F, Lohmann P, Pérez-García VM, Elhalawani H, de Verdier MC, Al-Rubaiey S, Armindo RD, Ashraf K, Asla MM, Badawy M, Bisschop J, Lomer NB, Bukatz J, Chen J, Cimflova P, Corr F, Crawley A, Deptula L, Elakhdar T, Shawali IH, Faghani S, Frick A, Gulati V, Haider MA, Hierro F, Dahl RH, Jacobs SM, Hsieh KJ, Kandemirli SG, Kersting K, Kida L, Kollia S, Koukoulithras I, Li X, Abouelatta A, Mansour A, Maria-Zamfirescu RC, Marsiglia M, Mateo-Camacho YS, McArthur M, McDonnell O, McHugh M, Moassefi M, Morsi SM, Munteanu A, Nandolia KK, Naqvi SR, Nikanpour Y, Alnoury M, Nouh AMA, Pappafava F, Patel MD, Petrucci S, Rawie E, Raymond S, Roohani B, Sabouhi S, Sanchez-Garcia LM, Shaked Z, Suthar PP, Altes T, Isufi E, Dhemesh Y, Gass J, Thacker J, Tarabishy AR, Turner B, Vacca S, Vilanilam GK, Warren D, Weiss D, Worede F, Yousry S, Lerebo W, Aristizabal A, Karargyris A, Kassem H, Pati S, Sheller M, Link KEE, Calabrese E, Tahon NH, Nada A, Velichko YS, Bakas S, Rudie JD, Aboian M. The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI. ArXiv. 2024 Dec 09.  View on PubMed
  9. Rauschecker AM. Standardizing qualitative assessments of developing myelination on brain MRI. Eur J Paediatr Neurol. 2024 Sep; 52:A1.  View on PubMed
  10. Lew CO, Calabrese E, Chen JV, Tang F, Chaudhari G, Lee A, Faro J, Juul S, Mathur A, McKinstry RC, Wisnowski JL, Rauschecker A, Wu YW, Li Y. Artificial Intelligence Outcome Prediction in Neonates with Encephalopathy (AI-OPiNE). Radiol Artif Intell. 2024 Sep; 6(5):e240076.  View on PubMed
  11. Fields BKK, Calabrese E, Mongan J, Cha S, Hess CP, Sugrue LP, Chang SM, Luks TL, Villanueva-Meyer JE, Rauschecker AM, Rudie JD. The University of California San Francisco Adult Longitudinal Post-Treatment Diffuse Glioma MRI Dataset. Radiol Artif Intell. 2024 Jul; 6(4):e230182.  View on PubMed
  12. LaBella D, Khanna O, McBurney-Lin S, Mclean R, Nedelec P, Rashid AS, Tahon NH, Altes T, Baid U, Bhalerao R, Dhemesh Y, Floyd S, Godfrey D, Hilal F, Janas A, Kazerooni A, Kent C, Kirkpatrick J, Kofler F, Leu K, Maleki N, Menze B, Pajot M, Reitman ZJ, Rudie JD, Saluja R, Velichko Y, Wang C, Warman PI, Sollmann N, Diffley D, Nandolia KK, Warren DI, Hussain A, Fehringer JP, Bronstein Y, Deptula L, Stein EG, Taherzadeh M, Portela de Oliveira E, Haughey A, Kontzialis M, Saba L, Turner B, Brüßeler MMT, Ansari S, Gkampenis A, Weiss DM, Mansour A, Shawali IH, Yordanov N, Stein JM, Hourani R, Moshebah MY, Abouelatta AM, Rizvi T, Willms K, Martin DC, Okar A, D'Anna G, Taha A, Sharifi Y, Faghani S, Kite D, Pinho M, Haider MA, Alonso-Basanta M, Villanueva-Meyer J, Rauschecker AM, Nada A, Aboian M, Flanders A, Bakas S, Calabrese E. A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation. Sci Data. 2024 May 15; 11(1):496.  View on PubMed
  13. Roy E, Van Rinsveld A, Nedelec P, Richie-Halford A, Rauschecker AM, Sugrue LP, Rokem A, McCandliss BD, Yeatman JD. Differences in educational opportunity predict white matter development. Dev Cogn Neurosci. 2024 06; 67:101386.  View on PubMed
  14. Rudie JD, Saluja R, Weiss DA, Nedelec P, Calabrese E, Colby JB, Laguna B, Mongan J, Braunstein S, Hess CP, Rauschecker AM, Sugrue LP, Villanueva-Meyer JE. The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset. Radiol Artif Intell. 2024 03; 6(2):e230126.  View on PubMed
  15. Chen JV, Li Y, Tang F, Chaudhari G, Lew C, Lee A, Rauschecker AM, Haskell-Mendoza AP, Wu YW, Calabrese E. Automated neonatal nnU-Net brain MRI extractor trained on a large multi-institutional dataset. Sci Rep. 2024 02 26; 14(1):4583.  View on PubMed
  16. Roy E, Richie-Halford A, Kruper J, Narayan M, Bloom D, Nedelec P, Rauschecker AM, Sugrue LP, Brown TT, Jernigan TL, McCandliss BD, Rokem A, Yeatman JD. White matter and literacy: A dynamic system in flux. Dev Cogn Neurosci. 2024 Feb; 65:101341.  View on PubMed
  17. Tsui B, Calabrese E, Zaharchuk G, Rauschecker AM. Reducing Gadolinium Contrast With Artificial Intelligence. J Magn Reson Imaging. 2024 Sep; 60(3):848-859.  View on PubMed
  18. Wahlig SG, Nedelec P, Weiss DA, Rudie JD, Sugrue LP, Rauschecker AM. 3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies. Front Neurosci. 2023; 17:1188336.  View on PubMed
  19. Ma SX, Dhanaliwala AH, Rudie JD, Rauschecker AM, Roberts-Wolfe D, Haddawy P, Kahn CE. Bayesian Networks in Radiology. Radiol Artif Intell. 2023 Nov; 5(6):e210187.  View on PubMed
  20. George E, Rauschecker AM. Beyond the AJR: Deep Learning Shows Promise in the Detection of Retinal Hemorrhage on Pediatric Head CT. AJR Am J Roentgenol. 2024 04; 222(4):e2330096.  View on PubMed

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