Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Pavithra, A.; Monica, R.; Manimekala, B. Title A name given to the resource BCI Radiology Images Converting into Report Using BERT and GPT Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1354 LNNS;pp.135-143 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4880-1_11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4880-1_11</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019298664?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019298664?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Pavithra A., Christ University, Karnataka, Bengaluru, India; Monica R., Christ University, Karnataka, Bengaluru, India; Manimekala B., Christ University, Karnataka, Bengaluru, India Description An account of the resource The construction of precise radiology reports from medical images is an essential aspect of Contemporary healthcare. Medical images such as X-rays, MRIs, CT scans, or ultrasounds. Also, it can make use of medical reports. Medical report has a bunch of details about each patients medical history, diagnosis, treatment plan, lab results, and more. This paper represents a theoretical examination. The paper mainly focuses on two prominent NLP models. One is BERT (Bidirectional Encoder Representations from Transformers) and the other one is GPT (Generative Pre-trained Transformer). This paper is going to validate their applicability to transforming brain-computer interfaces (BCI). This paper will utilize these radiology images in perfectly framed medical reports. By differentiating these models based on their Architectural properties, Linguistic processing abilities, and capability for clinical integration, this papers goal is to establish the most effective method for automated medical reporting. Merging of these insights from existing studies recommends that when BERT leads in context-based precision and getting an idea of complex medical terminology, GPT offers outstanding text-generation potential. This paper proposes that an intermixture procedure taking advantage of the strengths of both models may offer the most supreme solution for automated medical reporting, balancing precision with readability and clinical applicability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource BCI; BERT; GPT; MRI; NLP Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964879-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online