Evaluating large language models in echocardiography reporting: opportunities and challenges
EchoGPT can generate draft reports for human review and approval, helping to streamline the workflow. However, scalable evaluation approaches dedicated to echo reports remains necessary.
Authors
- Francisco López-Jiménez (ORCID: https://orcid.org/0000-0001-5788-9734)
- Bradley J. Erickson (ORCID: https://orcid.org/0000-0001-7926-6095)
- Garvan C. Kane (ORCID: https://orcid.org/0000-0002-4308-6356)
- Jean-Benoit Delbrouck
- Imon Banerjee (ORCID: https://orcid.org/0000-0002-3327-8004)
- Reza Arsanjani (ORCID: https://orcid.org/0000-0001-7081-4286)
- Zachi Itzhak Attia (ORCID: https://orcid.org/0000-0002-9706-7900)
- Chieh-Ju Chao
- Andrew S. Tseng (ORCID: https://orcid.org/0000-0002-9181-1970)
- Jae K. Oh (ORCID: https://orcid.org/0000-0002-8303-5780)
- Curtis P. Langlotz (ORCID: https://orcid.org/0000-0002-8972-8051)
- Li Fei-Fei (ORCID: https://orcid.org/0000-0002-7481-0810)
- Chadi Ayoub (ORCID: https://orcid.org/0000-0001-5877-7478)
- Ehsan Adeli
Institutions
- Mayo Clinic (US)
- Association for the Advancement of Artificial Intelligence (US)
- WinnMed (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
- Stanford University (US)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1093/ehjdh/ztae086
- Citations
- 12
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.85