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Natural Language Processing (NLP) and Large Language Models (LLMs) hold largely untapped potential in infectious disease management. This review explores their current use and uncovers areas needing more attention. This analysis followed systematic review procedures, registered with the Prospective Register of Systematic Reviews. We conducted a search across major databases including PubMed, Embase, Web of Science, and Scopus, up to December 2023, using keywords related to NLP, LLM, and infectious diseases. We also employed the Quality Assessment of Diagnostic Accuracy Studies-2 tool for evaluating the quality and robustness of the included studies. Our review identified 15 studies with diverse applications of NLP in infectious disease management. Notable examples include GPT-4's application in detecting urinary tract infections and BERTweet's use in Lyme Disease surveillance through social media analysis. These models demonstrated effective disease monitoring and public health tracking capabilities. However, the effectiveness varied across studies. For instance, while some NLP tools showed high accuracy in pneumonia detection and high sensitivity in identifying invasive mold diseases from medical reports, others fell short in areas like bloodstream infection management. This review highlights the yet-to-be-fully-realized promise of NLP and LLMs in infectious disease management. It calls for more exploration to fully harness AI's capabilities, particularly in the areas of diagnosis, surveillance, predicting disease courses, and tracking epidemiological trends. • AI, especially NLP and LLMs, could enhance infectious disease diagnosis and prediction. • NLP models excel in disease detection and public health surveillance. • Results from included studies show varied NLP effectiveness across diseases. • Challenges in AI and NLP applications include data bias and accuracy. • Further research is essential to maximize AI potential in healthcare and infectious diseases. [ABSTRACT FROM AUTHOR]

Bidirectional Encoder Representations from Transformers (BERT), introduced in 2018, has revolutionized natural language processing. Its bidirectional understanding of word context has enabled innovative applications, notably in radiology. This study aimed to assess BERT's influence and applications within the radiologic domain. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review, searching PubMed for literature on BERT-based models and natural language processing in radiology from January 1, 2018, to February 12, 2023. The search encompassed keywords related to generative models, transformer architecture, and various imaging techniques. Of 597 results, 30 met our inclusion criteria. The remaining were unrelated to radiology or did not use BERT-based models. The included studies were retrospective, with 14 published in 2022. The primary focus was on classification and information extraction from radiology reports, with x-rays as the prevalent imaging modality. Specific investigations included automatic CT protocol assignment and deep learning applications in chest x-ray interpretation. This review underscores the primary application of BERT in radiology for report classification. It also reveals emerging BERT applications for protocol assignment and report generation. As BERT technology advances, we foresee further innovative applications. Its implementation in radiology holds potential for enhancing diagnostic precision, expediting report generation, and optimizing patient care. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) models like GPT-3.5 and GPT-4 have shown promise across various domains but remain underexplored in healthcare. Emergency Departments (ED) rely on established scoring systems, such as NIHSS and HEART score, to guide clinical decision-making. This study aims to evaluate the proficiency of GPT-3.5 and GPT-4 against experienced ED physicians in calculating five commonly used medical scores. This retrospective study analyzed data from 150 patients who visited the ED over one week. Both AI models and two human physicians were tasked with calculating scores for NIH Stroke Scale, Canadian Syncope Risk Score, Alvarado Score for Acute Appendicitis, Canadian CT Head Rule, and HEART Score. Cohen's Kappa statistic and AUC values were used to assess inter-rater agreement and predictive performance, respectively. The highest level of agreement was observed between the human physicians (Kappa = 0.681), while GPT-4 also showed moderate to substantial agreement with them (Kappa values of 0.473 and 0.576). GPT-3.5 had the lowest agreement with human scorers. These results highlight the superior predictive performance of human expertise over the currently available automated systems for this specific medical outcome. Human physicians achieved a higher ROC-AUC on 3 of the 5 scores, but none of the differences were statistically significant. While AI models demonstrated some level of concordance with human expertise, they fell short in emulating the complex clinical judgments that physicians make. The study suggests that current AI models may serve as supplementary tools but are not ready to replace human expertise in high-stakes settings like the ED. Further research is needed to explore the capabilities and limitations of AI in emergency medicine. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring. A systematic search was conducted in PubMed and Web of Science databases from January 2016 to December 2022. The search strategy was carried out by using key words and MeSH (Medical Subject Headings) terms. Studies were included if they reported the accuracy of convolutional neural network models for embryo evaluation using time-lapse monitoring. The review was registered with PROSPERO (International Prospective Register of Systematic Reviews; identification number CRD42021275916). Two reviewer authors independently screened results using the Covidence systematic review software. The full-text articles were reviewed when studies met the inclusion criteria or in any uncertainty. Nonconsensus was resolved by a third reviewer. Risk of bias and applicability were evaluated using the QUADAS-2 tool and the modified Joanna Briggs Institute or JBI checklist. Following a systematic search of the literature, 22 studies were identified as eligible for inclusion. All studies were retrospective. A total of 522,516 images of 222,998 embryos were analyzed. Three main outcomes were evaluated: successful in vitro fertilization, blastocyst stage classification, and blastocyst quality. Most studies reported >80% accuracy, and embryologists were outperformed in some. Ten studies had a high risk of bias, mostly because of patient bias. The application of artificial intelligence in time-lapse monitoring has the potential to provide more efficient, accurate, and objective embryo evaluation. Models that examined blastocyst stage classification showed the best predictions. Models that predicted live birth had a low risk of bias, used the largest databases, and had external validation, which heightens their relevance to clinical application. Our systematic review is limited by the high heterogeneity among the included studies. Researchers should share databases and standardize reporting. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) in young adults is an uncommon and not well studied entity. Consecutive patients aged 18–45 years admitted to internal or cardiology services in a large tertiary medical center (January 1, 2009 through December 31, 2019) were included. Clinical, electrocardiographic, and echocardiographic data were compared between patients with and without AF at baseline. Predictors of new-onset AF in the young were identified using multivariate Cox regression model among patients free of baseline AF. Final cohort included 16,432 patients with median age of 34 (IQR 26–41) years of whom 8914 (56 %) were men. Patients with AF at baseline (N = 366; 2 %) were older, more likely to be men, and had higher proportion of comorbidities and electrocardiographic conduction disorders. Male sex, increased age, obesity, heart failure, congenital heart disease (CHD) and the presence of left or right bundle branch block were all independently associated with baseline AF in a multivariate model (p < 0.001 for all). Sub-analysis of 10,691 (98 %) patients free of baseline AF identified 85 cases of new-onset AF during a median follow up of 3.5 (IQR 1.5–6.5) years. Multivariate model identified increased age, heart failure, and CHD as independent predictors of new-onset AF. Finally, the CHARGE-AF risk score outperformed the CHA 2 DS 2 -VASc score in AF prediction [AUC of ROC 0.75 (0.7–0.8) vs. 0.56 (0.48–0.65)]. AF among hospitalized young adults is not rare. Screening for new-onset AF in young post hospitalization patients may be guided by specific clinical predictors and the CHARGE-AF risk score. [Display omitted] • Little is known about the clinical profile of and risk factors for young-onset atrial fibrillation (AF). • We describe features of existing and variables predictive of young-onset AF. • The CHARGE-AF score is of possible benefit in predicting young-onset AF. • Our findings may lead to early diagnosis and treatment of AF in the young. [ABSTRACT FROM AUTHOR]

The quality of radiology referrals influences patient management and imaging interpretation by radiologists. The aim of this study was to evaluate ChatGPT-4 as a decision support tool for selecting imaging examinations and generating radiology referrals in the emergency department (ED). Five consecutive clinical notes from the ED were retrospectively extracted, for each of the following pathologies: pulmonary embolism, obstructing kidney stones, acute appendicitis, diverticulitis, small bowel obstruction, acute cholecystitis, acute hip fracture, and testicular torsion. A total of 40 cases were included. These notes were entered into ChatGPT-4, requesting recommendations on the most appropriate imaging examinations and protocols. The chatbot was also asked to generate radiology referrals. Two independent radiologists graded the referral on a scale ranging from 1 to 5 for clarity, clinical relevance, and differential diagnosis. The chatbot's imaging recommendations were compared with the ACR Appropriateness Criteria (AC) and with the examinations performed in the ED. Agreement between readers was assessed using linear weighted Cohen's κ coefficient. ChatGPT-4's imaging recommendations aligned with the ACR AC and ED examinations in all cases. Protocol discrepancies between ChatGPT and the ACR AC were observed in two cases (5%). ChatGPT-4-generated referrals received mean scores of 4.6 and 4.8 for clarity, 4.5 and 4.4 for clinical relevance, and 4.9 from both reviewers for differential diagnosis. Agreement between readers was moderate for clinical relevance and clarity and substantial for differential diagnosis grading. ChatGPT-4 has shown potential in aiding imaging study selection for select clinical cases. As a complementary tool, large language models may improve radiology referral quality. Radiologists should stay informed about this technology and be mindful of potential challenges and risks. [Display omitted] [ABSTRACT FROM AUTHOR]

This study aimed to investigate the impact of a designated pharmacist (DPha) intervention in a hemato-oncology unit, focusing on reducing drug treatment costs and improving technical efficiency (TE). Data from an 8-month intervention in the Israeli Clalit Health Services hemato-oncology outpatient unit were analyzed. During the study, the DPha reviewed the drug therapies being administered. After the review, a recommendation letter was sent, if relevant, to the treating physician. Data on drug treatment costs and interventions were meticulously collected and analyzed from the perspective of the insurer. A simple design was used to assess the DPha intervention's contribution to TE and cost reduction, which was used to generate credible and transparent estimates. Sensitivity analyses were conducted to assess the robustness of 2 major variables: drug prices and pharmacist salaries. Over 8 months, DPha interventions led to a $279 191 cost reduction for 91 patients, resulting in net savings of $269 420 ($2960 per patient). Noteworthy is the $411 savings for each hour worked by the pharmacist, with a major impact on medications not insurer approved for the patient's condition ($101 151) and discontinuing inappropriate medications ($52 681). Biological drug optimization accounted for 81% of total savings. Sensitivity analyses demonstrated significant cost savings across various drug prices and pharmacist salary scenarios. The study proposes a practical framework for optimizing pharmacist services and reducing the inappropriate use of costly oncology medications. Incorporating a DPha enhances TE and yields significant cost reductions, offering valuable insights for insurers, policy makers, and healthcare professionals. • This study builds on existing knowledge by demonstrating the impact of a designated pharmacist intervention on reducing drug treatment costs in the hemato-oncology department. • This study provides novel insights into how designated pharmacist-led initiatives can streamline workflow and resource utilization by identifying processes such as coordinated drug administration and prescription error correction. • Healthcare professionals can leverage these findings to optimize departmental technical efficiency and improve patient outcomes. This study's findings showcase the potential for substantial cost savings without compromising the quality of care. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: High-resolution computed tomography (HRCT) is paramount in the assessment of interstitial lung disease (ILD). Yet, HRCT interpretation of ILDs may be hampered by inter- and intra-observer variability. Recently, artificial intelligence (AI) has revolutionized medical image analysis. This technology has the potential to advance patient care in ILD. We aimed to systematically evaluate the application of AI for the analysis of ILD in HRCT.Materials and Methods: We searched MEDLINE/PubMed databases for original publications of deep learning for ILD analysis on chest CT. The search included studies published up to March 1, 2021. The risk of bias evaluation included tailored Quality Assessment of Diagnostic Accuracy Studies and the modified Joanna Briggs Institute Critical Appraisal checklist.Results: Data was extracted from 19 retrospective studies. Deep learning techniques included detection, segmentation, and classification of ILD on HRCT. Most studies focused on the classification of ILD into different morphological patterns. Accuracies of 78%-91% were achieved. Two studies demonstrated near-expert performance for the diagnosis of idiopathic pulmonary fibrosis (IPF). The Quality Assessment of Diagnostic Accuracy Studies tool identified a high risk of bias in 15/19 (78.9%) of the studies.Conclusion: AI has the potential to contribute to the radiologic diagnosis and classification of ILD. However, the accuracy performance is still not satisfactory, and research is limited by a small number of retrospective studies. Hence, the existing published data may not be sufficiently reliable. Only well-designed prospective controlled studies can accurately assess the value of existing AI tools for ILD evaluation. [ABSTRACT FROM AUTHOR]

Background and Aim: New York City (NYC) is an epicenter of the COVID-19 pandemic in the United States. Proper triage of patients with possible COVID-19 via chief complaint is critical but not fully optimized. This study aimed to investigate the association between presentation by chief complaints and COVID-19 status.Methods: We retrospectively analyzed adult emergency department (ED) patient visits from five different NYC hospital campuses from March 1, 2020 to May 13, 2020 of patients who underwent nasopharyngeal COVID-19 RT-PCR testing. The positive and negative COVID-19 cohorts were then assessed for different chief complaints obtained from structured triage data. Sub-analysis was performed for patients older than 65 and within chief complaints with high mortality.Results: Of 11,992 ED patient visits who received COVID-19 testing, 6524/11992 (54.4%) were COVID-19 positive. 73.5% of fever, 67.7% of shortness of breath, and 65% of cough had COVID-19, but others included 57.5% of weakness/fall/altered mental status, 55.5% of glycemic control, and 51.4% of gastrointestinal symptoms. In patients over 65, 76.7% of diarrhea, 73.7% of fatigue, and 69.3% of weakness had COVID-19. 45.5% of dehydration, 40.5% of altered mental status, 27% of fall, and 24.6% of hyperglycemia patients experienced mortality.Conclusion: A novel high risk COVID-19 patient population was identified from chief complaint data, which is different from current suggested CDC guidelines, and may help triage systems to better isolate COVID-19 patients. Older patients with COVID-19 infection presented with more atypical complaints warranting special consideration. COVID-19 was associated with higher mortality in a unique group of complaints also warranting special consideration. [ABSTRACT FROM AUTHOR]

The aim of our study was to evaluate the yield of head CT in the ED in different age groups and different referral indications. Records of one large academic tertiary care ED were retrospectively reviewed for consecutive adult patients who underwent a head CT between January 1st 2017 and February 10th 2017. CT referral forms and interpretations were obtained and evaluated for demographics, referral indications, and findings. Scans were divided into three groups: acute findings, chronic findings , and normal. The cohort was divided into three age groups. Associations between referral indications and acute findings were calculated. Overall, 1536 of adult patients with ED head CT were included. Acute findings were found in 239/1536 (15.5%) of the CTs. The frequency of acute findings increased with age (p = 0.027). The most common acute findings were brain hemorrhage (32.6%), infarct (27.6%), and mass (23%). The top three referral indications were focal neurologic deficit (28%), trauma (24.7%), and headache (17.5%). The rates of positive acute findings for different referral indications were seizure 27%, confusion 20%, syncope 19%, focal neurologic deficit 16%, head injury 15%, headache 12%, and dizziness 8%. This study shows the yield of ED head CT for acute findings for different age groups and for different referral indications. The frequency of acute findings increased with age. Suspected seizure had the highest association with an acute finding, whereas dizziness had the lowest association. • The probability of a head CT referral in the ED increases with age. • Different referral indications for head CTs conducted in the ED have different yields for acute findings. • The probability for acute and chronic findings in head CTs conducted in the ED increases with age. [ABSTRACT FROM AUTHOR]