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The application of chatbots and NLP in radiology is an emerging field, currently characterized by a growing body of research. An umbrella review has been proposed utilizing a standardized checklist and quality control procedure for including scientific papers. This review explores the early developments and potential future impact of these technologies in radiology. The current literature, comprising 15 systematic reviews, highlights potentialities, opportunities, areas needing improvements, and recommendations. This umbrella review offers a comprehensive overview of the current landscape of natural language processing (NLP) and natural language models (NLMs), including chatbots, in healthcare. These technologies show potential for improving clinical decision-making, patient engagement, and communication across various medical fields. However, significant challenges remain, particularly the lack of standardized protocols, which raises concerns about the reliability and consistency of these tools in different clinical contexts. Without uniform guidelines, variability in outcomes may hinder the broader adoption of NLP/NLM technologies by healthcare providers. Moreover, the limited research on how these technologies intersect with medical devices (MDs) is a notable gap in the literature. Future research must address these challenges to fully realize the potential of NLP/NLM applications in healthcare. Key future research directions include the development of standardized protocols to ensure the consistent and safe deployment of NLP/NLM tools, particularly in high-stake areas like radiology. Investigating the integration of these technologies with MD workflows will be crucial to enhance clinical decision-making and patient care. Ethical concerns, such as data privacy, informed consent, and algorithmic bias, must also be explored to ensure responsible use in clinical settings. Longitudinal studies are needed to evaluate the long-term impact of these technologies on patient outcomes, while interdisciplinary collaboration between healthcare professionals, data scientists, and ethicists is essential for driving innovation in an ethically sound manner. Addressing these areas will advance the application of NLP/NLM technologies and improve patient care in this emerging field.
Lastrucci et al. (Mon,) studied this question.