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The facial nerve supplies motor, sensory, and parasympathetic innervation to the head and neck, and its paralysis can have significant physical and psychological impacts. This study discusses a compelling case involving a 21-year-old male who developed delayed facial nerve palsy (DFNP) on the eighth day after cortical mastoid surgery. Through conservative management, the patient achieved a full recovery by the 52nd day. Our experience underscores the importance of approaching DFNP with patience, emphasizing the need for thorough counseling of both the patient and their family members.
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Background Differential diagnosis in radiology is a critical aspect of clinical decision-making. Radiologists in the early stages may find difficulties in listing the differential diagnosis from image patterns. In this context, the emergence of large language models (LLMs) has introduced new opportunities as these models have the capacity to access and contextualize extensive information from text-based input. Objective The objective of this study was to explore the utility of four LLMs-ChatGPT3.5, Google Bard, Microsoft Bing, and Perplexity-in providing most important differential diagnoses of cardiovascular and thoracic imaging patterns. Methods We selected 15 unique cardiovascular ( n = 5) and thoracic ( n = 10) imaging patterns. We asked each model to generate top 5 most important differential diagnoses for every pattern. Concurrently, a panel of two cardiothoracic radiologists independently identified top 5 differentials for each case and came to consensus when discrepancies occurred. We checked the concordance and acceptance of LLM-generated differentials with the consensus differential diagnosis. Categorical variables were compared by binomial, chi-squared, or Fisher's exact test. Results A total of 15 cases with five differentials generated a total of 75 items to analyze. The highest level of concordance was observed for diagnoses provided by Perplexity (66.67%), followed by ChatGPT (65.33%) and Bing (62.67%). The lowest score was for Bard with 45.33% of concordance with expert consensus. The acceptance rate was highest for Perplexity (90.67%), followed by Bing (89.33%) and ChatGPT (85.33%). The lowest acceptance rate was for Bard (69.33%). Conclusion Four LLMs-ChatGPT3.5, Google Bard, Microsoft Bing, and Perplexity-generated differential diagnoses had high level of acceptance but relatively lower concordance. There were significant differences in acceptance and concordance among the LLMs. Hence, it is important to carefully select the suitable model for usage in patient care or in medical education.
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Background Radiology is among India's top five speciality choices pursued by meritorious medical graduates. With the advent of the subspecialization degree courses, fellowships, the requirement of senior residency as faculty eligibility criteria, and the lucrative option of private practice, the final-year postgraduates are given opportunities to choose from but with little guidance on what and how to choose. This study aims to analyze the views of the final-year radiology postgraduate residents in India regarding their understanding of how to proceed in their professional life with options available. Materials and Methods A cross-sectional, questionnaire-based study was conducted online via Google forms distributed via email and social media platforms. The questionnaire was prepared after going through previous literature, video blogs, and media available on the Internet and was further validated for content. Results About half (48%) of them wished to pursue higher studies in the form of Doctorate of Medicine (DM) degree courses or fellowships. Almost three-quarters of the participants preferred short-term subspecialization via fellowships over 3-year DM courses (28%). Regarding clinical practice, 61.9% preferred to take up senior residency, while slightly over one-third (35.7%) expressed their will to move on to private practice. Conclusion A relative conundrum was observed in the decision to take up senior residency or private practice or to go for DM but had to pursue a fellowship due to limited choice in topics and seats available in the country for subspecialization.