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  1. Article ; Online: Imagine there is no paperwork… it's easy if you try.

    Martín-Noguerol, Teodoro / López-Úbeda, Pilar / Luna, Antonio

    The British journal of radiology

    2024  Volume 97, Issue 1156, Page(s) 744–746

    Abstract: Artificial Intelligence (AI) applied to radiology is so vast that it provides applications ranging from becoming a complete replacement for radiologists (a potential threat) to an efficient paperwork-saving time assistant (an evident strength). Nowadays, ...

    Abstract Artificial Intelligence (AI) applied to radiology is so vast that it provides applications ranging from becoming a complete replacement for radiologists (a potential threat) to an efficient paperwork-saving time assistant (an evident strength). Nowadays, there are AI applications developed to facilitate the diagnostic process of radiologists without directly influencing (or replacing) the proper diagnostic decision step. These tools may help to reduce administrative workload, in different scenarios ranging from assisting in scheduling, study prioritization, or report communication, to helping with patient follow-up, including recommending additional exams. These are just a few of the highly time-consuming tasks that radiologists have to deal with every day in their routine workflow. These tasks hinder the time that radiologists should spend evaluating images and caring for patients, which will have a direct and negative impact on the quality of reports and patient attention, increasing the delay and waiting list of studies pending to be performed and reported. These types of AI applications should help to partially face this worldwide shortage of radiologists.
    MeSH term(s) Humans ; Artificial Intelligence ; Radiology/methods ; Radiologists ; Workflow ; Workload
    Language English
    Publishing date 2024-02-08
    Publishing country England
    Document type Journal Article
    ZDB-ID 2982-8
    ISSN 1748-880X ; 0007-1285
    ISSN (online) 1748-880X
    ISSN 0007-1285
    DOI 10.1093/bjr/tqae035
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: AI in radiology: Legal responsibilities and the car paradox.

    Martín-Noguerol, Teodoro / López-Úbeda, Pilar / Luna, Antonio

    European journal of radiology

    2024  Volume 175, Page(s) 111462

    Abstract: The integration of AI in radiology raises significant legal questions about responsibility for errors. Radiologists fear AI may introduce new legal challenges, despite its potential to enhance diagnostic accuracy. AI tools, even those approved by ... ...

    Abstract The integration of AI in radiology raises significant legal questions about responsibility for errors. Radiologists fear AI may introduce new legal challenges, despite its potential to enhance diagnostic accuracy. AI tools, even those approved by regulatory bodies like the FDA or CE, are not perfect, posing a risk of failure. The key issue is how AI is implemented: as a stand-alone diagnostic tool or as an aid to radiologists. The latter approach could reduce undesired side effects. However, it's unclear who should be held liable for AI failures, with potential candidates ranging from engineers and radiologists involved in AI development to companies and department heads who integrate these tools into clinical practice. The EU's AI Act, recognizing AI's risks, categorizes applications by risk level, with many radiology-related AI tools considered high risk. Legal precedents in autonomous vehicles offer some guidance on assigning responsibility. Yet, the existing legal challenges in radiology, such as diagnostic errors, persist. AI's potential to improve diagnostics raises questions about the legal implications of not using available AI tools. For instance, an AI tool improving the detection of pediatric fractures could reduce legal risks. This situation parallels innovations like car turn signals, where ignoring available safety enhancements could lead to legal problems. The debate underscores the need for further research and regulation to clarify AI's role in radiology, balancing innovation with legal and ethical considerations.
    Language English
    Publishing date 2024-04-10
    Publishing country Ireland
    Document type Letter
    ZDB-ID 138815-0
    ISSN 1872-7727 ; 0720-048X
    ISSN (online) 1872-7727
    ISSN 0720-048X
    DOI 10.1016/j.ejrad.2024.111462
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  3. Article ; Online: Reply to the letter to the editor: "A critical evaluation on the use of large language model for radiology research".

    López-Úbeda, Pilar / Martín-Noguerol, Teodoro / Luna, Antonio

    European radiology

    2023  Volume 33, Issue 12, Page(s) 9464–9465

    MeSH term(s) Humans ; Radiography ; Radiology ; Language
    Language English
    Publishing date 2023-10-17
    Publishing country Germany
    Document type Letter ; Comment
    ZDB-ID 1085366-2
    ISSN 1432-1084 ; 0938-7994 ; 1613-3749
    ISSN (online) 1432-1084
    ISSN 0938-7994 ; 1613-3749
    DOI 10.1007/s00330-023-10333-8
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  4. Article ; Online: Radiology, explicability and AI: closing the gap.

    López-Úbeda, Pilar / Martín-Noguerol, Teodoro / Luna, Antonio

    European radiology

    2023  Volume 33, Issue 12, Page(s) 9466–9468

    MeSH term(s) Humans ; Radiology ; Radiography ; Radiologists
    Language English
    Publishing date 2023-07-06
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1085366-2
    ISSN 1432-1084 ; 0938-7994 ; 1613-3749
    ISSN (online) 1432-1084
    ISSN 0938-7994 ; 1613-3749
    DOI 10.1007/s00330-023-09902-8
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  5. Article ; Online: Radiology in the era of large language models: the near and the dark side of the moon.

    López-Úbeda, Pilar / Martín-Noguerol, Teodoro / Luna, Antonio

    European radiology

    2023  Volume 33, Issue 12, Page(s) 9455–9457

    MeSH term(s) Humans ; Radiography ; Radiology ; Moon
    Language English
    Publishing date 2023-07-06
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1085366-2
    ISSN 1432-1084 ; 0938-7994 ; 1613-3749
    ISSN (online) 1432-1084
    ISSN 0938-7994 ; 1613-3749
    DOI 10.1007/s00330-023-09901-9
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  6. Article ; Online: Artificial Intelligence in Radiology: A Fast-Food Versus Slow-Food Question?

    Martín-Noguerol, Teodoro / López-Úbeda, Pilar / Luna, Antonio

    Journal of the American College of Radiology : JACR

    2023  Volume 21, Issue 5, Page(s) 810–811

    MeSH term(s) Artificial Intelligence ; Humans ; Radiology
    Language English
    Publishing date 2023-07-13
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2274861-1
    ISSN 1558-349X ; 1546-1440
    ISSN (online) 1558-349X
    ISSN 1546-1440
    DOI 10.1016/j.jacr.2023.04.023
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  7. Article ; Online: Automatic generation of conclusions from neuroradiology MRI reports through natural language processing.

    López-Úbeda, Pilar / Martín-Noguerol, Teodoro / Escartín, Jorge / Luna, Antonio

    Neuroradiology

    2024  Volume 66, Issue 4, Page(s) 477–485

    Abstract: Purpose: The conclusion section of a radiology report is crucial for summarizing the primary radiological findings in natural language and essential for communicating results to clinicians. However, creating these summaries is time-consuming, repetitive, ...

    Abstract Purpose: The conclusion section of a radiology report is crucial for summarizing the primary radiological findings in natural language and essential for communicating results to clinicians. However, creating these summaries is time-consuming, repetitive, and prone to variability and errors among different radiologists. To address these issues, we evaluated a fine-tuned Text-To-Text Transfer Transformer (T5) model for abstractive summarization to automatically generate conclusions for neuroradiology MRI reports in a low-resource language.
    Methods: We retrospectively applied our method to a dataset of 232,425 neuroradiology MRI reports in Spanish. We compared various pre-trained T5 models, including multilingual T5 and those newly adapted for Spanish. For precise evaluation, we employed BLEU, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics alongside expert radiologist assessments.
    Results: The findings are promising, with the models specifically fine-tuned for neuroradiology MRI achieving scores of 0.46, 0.28, 0.52, 2.45, and 0.87 in the BLEU-1, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics, respectively. In the radiological experts' evaluation, they found that in 75% of the cases evaluated, the conclusions generated by the system were as good as or even better than the manually generated conclusions.
    Conclusion: The methods demonstrate the potential and effectiveness of customizing state-of-the-art pre-trained models for neuroradiology, yielding automatic MRI report conclusions that nearly match expert quality. Furthermore, these results underscore the importance of designing and pre-training a dedicated language model for radiology report summarization.
    MeSH term(s) Humans ; Natural Language Processing ; Retrospective Studies ; Language ; Magnetic Resonance Imaging ; Radiology
    Language English
    Publishing date 2024-02-21
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 123305-1
    ISSN 1432-1920 ; 0028-3940
    ISSN (online) 1432-1920
    ISSN 0028-3940
    DOI 10.1007/s00234-024-03312-3
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  8. Article ; Online: Reply to the Letter to the Editor: "Radiology in the era of large language models: additional facts to consider in the near and the dark side of the moon".

    López-Úbeda, Pilar / Martín-Noguerol, Teodoro / Luna, Antonio

    European radiology

    2023  Volume 33, Issue 12, Page(s) 9460–9461

    MeSH term(s) Humans ; Radiography ; Radiology ; Language
    Language English
    Publishing date 2023-11-04
    Publishing country Germany
    Document type Letter ; Comment
    ZDB-ID 1085366-2
    ISSN 1432-1084 ; 0938-7994 ; 1613-3749
    ISSN (online) 1432-1084
    ISSN 0938-7994 ; 1613-3749
    DOI 10.1007/s00330-023-10331-w
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  9. Article ; Online: Evaluation of large language models performance against humans for summarizing MRI knee radiology reports: A feasibility study.

    López-Úbeda, Pilar / Martín-Noguerol, Teodoro / Díaz-Angulo, Carolina / Luna, Antonio

    International journal of medical informatics

    2024  Volume 187, Page(s) 105443

    Abstract: Objectives: This study addresses the critical need for accurate summarization in radiology by comparing various Large Language Model (LLM)-based approaches for automatic summary generation. With the increasing volume of patient information, accurately ... ...

    Abstract Objectives: This study addresses the critical need for accurate summarization in radiology by comparing various Large Language Model (LLM)-based approaches for automatic summary generation. With the increasing volume of patient information, accurately and concisely conveying radiological findings becomes crucial for effective clinical decision-making. Minor inaccuracies in summaries can lead to significant consequences, highlighting the need for reliable automated summarization tools.
    Methods: We employed two language models - Text-to-Text Transfer Transformer (T5) and Bidirectional and Auto-Regressive Transformers (BART) - in both fine-tuned and zero-shot learning scenarios and compared them with a Recurrent Neural Network (RNN). Additionally, we conducted a comparative analysis of 100 MRI report summaries, using expert human judgment and criteria such as coherence, relevance, fluency, and consistency, to evaluate the models against the original radiologist summaries. To facilitate this, we compiled a dataset of 15,508 retrospective knee Magnetic Resonance Imaging (MRI) reports from our Radiology Information System (RIS), focusing on the findings section to predict the radiologist's summary.
    Results: The fine-tuned models outperform the neural network and show superior performance in the zero-shot variant. Specifically, the T5 model achieved a Rouge-L score of 0.638. Based on the radiologist readers' study, the summaries produced by this model were found to be very similar to those produced by a radiologist, with about 70% similarity in fluency and consistency between the T5-generated summaries and the original ones.
    Conclusions: Technological advances, especially in NLP and LLM, hold great promise for improving and streamlining the summarization of radiological findings, thus providing valuable assistance to radiologists in their work.
    Language English
    Publishing date 2024-04-04
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 1466296-6
    ISSN 1872-8243 ; 1386-5056
    ISSN (online) 1872-8243
    ISSN 1386-5056
    DOI 10.1016/j.ijmedinf.2024.105443
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  10. Article ; Online: Natural Language Processing in Radiology: Update on Clinical Applications.

    López-Úbeda, Pilar / Martín-Noguerol, Teodoro / Juluru, Krishna / Luna, Antonio

    Journal of the American College of Radiology : JACR

    2022  Volume 19, Issue 11, Page(s) 1271–1285

    Abstract: Radiological reports are a valuable source of information used to guide clinical care and support research. Organizing and managing this content, however, frequently requires several manual curations because of the more common unstructured nature of the ... ...

    Abstract Radiological reports are a valuable source of information used to guide clinical care and support research. Organizing and managing this content, however, frequently requires several manual curations because of the more common unstructured nature of the reports. However, manual review of these reports for clinical knowledge extraction is costly and time-consuming. Natural language processing (NLP) is a set of methods developed to extract structured meaning from a body of text and can be used to optimize the workflow of health care professionals. Specifically, NLP methods can help radiologists as decision support systems and improve the management of patients' medical data. In this study, we highlight the opportunities offered by NLP in the field of radiology. A comprehensive review of the most commonly used NLP methods to extract information from radiological reports and the development of tools to improve radiological workflow using this information is presented. Finally, we review the important limitations of these tools and discuss the relevant observations and trends in the application of NLP to radiology that could benefit the field in the future.
    MeSH term(s) Humans ; Natural Language Processing ; Radiology ; Radiography ; Radiologists ; Research Report
    Language English
    Publishing date 2022-08-25
    Publishing country United States
    Document type Review ; Journal Article
    ZDB-ID 2274861-1
    ISSN 1558-349X ; 1546-1440
    ISSN (online) 1558-349X
    ISSN 1546-1440
    DOI 10.1016/j.jacr.2022.06.016
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