A decision support system for the detection of cutaneous fungal infections using artificial intelligence.

Cutaneous fungal infections are one of the most common skin conditions, hence, the burden of determining fungal elements upon microscopic examination with periodic acid-Schiff (PAS) and Gomori methenamine silver (GMS) stains, is very time consuming. Despite some morphological variability posing challenges to training artificial intelligence (AI)-based solutions, these structures are favored potential targets, enabling the recruitment of promising AI-based technologies. Herein, we present a novel AI solution for identifying skin fungal infections, potentially providing a decision support system for pathologists. Skin biopsies of patients diagnosed with a cutaneous fungal infection at the Sheba Medical Center, Israel between 2014 and 2023, were used. Samples were stained with PAS and GMS and digitized by the Philips IntelliSite scanner. DeePathology® STUDIO fungal elements were annotated and deemed as ground truth data after an overall revision by two specialist pathologists. Subsequently, they were used to create an AI-based solution, which has been further validated in other regions of interests. The study participants were divided into two cohorts. In the first cohort, the overall sensitivity of the algorithm was 0.8, specificity 0.97, F1 score 0.78; in the second, the overall sensitivity of the algorithm was 0.93, specificity 0.99, F1 score 0.95. The results obtained are encouraging as proof of concept for an AI-based fungi detection algorithm. DeePathology® STUDIO can be employed as a decision support system for pathologists when diagnosing a cutaneous fungal infection using PAS and GMS stains, thereby, saving time and money.

Comparative Analysis of Accuracy, Readability, Sentiment, and Actionability: Artificial Intelligence Chatbots (ChatGPT and Google Gemini) versus Traditional Patient Information Leaflets for Local Anesthesia in Eye Surgery.

Background and Aim: Eye surgeries often evoke strong negative emotions in patients, including fear and anxiety. Patient education material plays a crucial role in informing and empowering individuals. Traditional sources of medical information may not effectively address individual patient concerns or cater to varying levels of understanding. This study aims to conduct a comparative analysis of the accuracy, completeness, readability, tone, and understandability of patient education material generated by AI chatbots versus traditional Patient Information Leaflets (PILs), focusing on local anesthesia in eye surgery. Methods: Expert reviewers evaluated responses generated by AI chatbots (ChatGPT and Google Gemini) and a traditional PIL (Royal College of Anaesthetists' PIL) based on accuracy, completeness, readability, sentiment, and understandability. Statistical analyses, including ANOVA and Tukey HSD tests, were conducted to compare the performance of the sources. Results: Readability analysis showed variations in complexity among the sources, with AI chatbots offering simplified language and PILs maintaining better overall readability and accessibility. Sentiment analysis revealed differences in emotional tone, with Google Gemini exhibiting the most positive sentiment. AI chatbots demonstrated superior understandability and actionability, while PILs excelled in completeness. Overall, ChatGPT showed slightly higher accuracy (scores expressed as mean ± standard deviation) (4.71 ± 0.5 vs 4.61 ± 0.62) and completeness (4.55 ± 0.58 vs 4.47 ± 0.58) compared to Google Gemini, but PILs performed best (4.84 ± 0.37 vs 4.88 ± 0.33) in terms of both accuracy and completeness (p-value for completeness <0.05). Conclusion: AI chatbots show promise as innovative tools for patient education, complementing traditional PILs. By leveraging the strengths of both AI-driven technologies and human expertise, healthcare providers can enhance patient education and empower individuals to make informed decisions about their health and medical care.

Cataract surgery in retinitis pigmentosa.

Background: Retinitis pigmentosa (RP) is an inherited retinal dystrophy characterized by progressive vision loss due to photoreceptor degeneration. Complicated cataract formation, particularly posterior subcapsular cataract (PSC), frequently occurs in RP and exacerbates the visual impairment. Cataract surgery may improve vision; however, the distinctive challenges of RP require specific considerations. This mini-review aims to provide a comprehensive overview of the RP-related cataract. Methods: A comprehensive literature review was conducted via PubMed/MEDLINE, spanning the period from January 1976 to June 2024, using the keywords "cataract," "cataract surgery," "cystoid macular edema," "hereditary retinal dystrophy," "retinitis pigmentosa," "posterior subcapsular cataract," "posterior capsular opacification," "zonular weakness," and "artificial intelligence." We aimed to evaluate cataract surgery in patients with RP, focusing on cataract formation, its surgical management, postoperative complications, patient follow-up, and visual outcomes. Relevant review articles, clinical trials, and case reports with related reference lists of these articles were included. Results: A total of 53 articles were examined in detail, including those identified through focused keyword searches and the reference lists of these articles. Cataract surgery in patients with RP generally results in substantial visual improvement. However, surgery can be complicated, particularly by zonular weakness and subluxation of the crystalline lens. These risks can be reduced by using capsular tension rings and employing meticulous surgical technique. Furthermore, postoperative complications, such as cystoid macular edema and posterior capsular opacification, are common. Despite these challenges, regular postoperative follow-up and appropriate management can help mitigate complications. Integrity of the ellipsoid zone and external limiting membrane on preoperative optical coherence tomographic examination are the main predictors of visual outcomes following cataract surgery; however, outcomes can vary. Though many patients experience significant visual improvement, some may experience limited benefits due to pre-existing advanced retinal degeneration. Conclusions: Cataract surgery may offer meaningful visual benefits in patients with RP; however, careful preoperative evaluation and meticulous surgical technique are required to address the possible challenges. Attentive postoperative care and follow-up are essential to optimize visual outcomes. Early surgical intervention can significantly improve the quality of life in selected candidates, and tailored approaches are necessary in patients with RP requiring cataract surgery. Further studies on the potential application of artificial intelligence to monitor postoperative recovery and detect complications may improve surgical outcomes and enhance patient care.

Knowledge, attitude, and practice of artificial intelligence among medical students in Sudan: a cross-sectional study.

Introduction: In this cross-sectional study, the authors explored the knowledge, attitudes, and practices related to artificial intelligence (AI) among medical students in Sudan. With AI increasingly impacting healthcare, understanding its integration into medical education is crucial. This study aimed to assess the current state of AI awareness, perceptions, and practical experiences among medical students in Sudan. The authors aimed to evaluate the extent of AI familiarity among Sudanese medical students by examining their attitudes toward its application in medicine. Additionally, this study seeks to identify the factors influencing knowledge levels and explore the practical implementation of AI in the medical field. Method: A web-based survey was distributed to medical students in Sudan via social media platforms and e-mail during October 2023. The survey included questions on demographic information, knowledge of AI, attitudes toward its applications, and practical experiences. The descriptive statistics, χ2 tests, logistic regression, and correlations were analyzed using SPSS version 26.0. Results: Out of the 762 participants, the majority exhibited a basic understanding of AI, but detailed knowledge of its applications was limited. Positive attitudes toward the importance of AI in diagnosis, radiology, and pathology were prevalent. However, practical application of these methods was infrequent, with only a minority of the participants having hands-on experience. Factors influencing knowledge included the lack of a formal curriculum and gender disparities. Conclusion: This study highlights the need for comprehensive AI education in medical training programs in Sudan. While participants displayed positive attitudes, there was a notable gap in practical experience. Addressing these gaps through targeted educational interventions is crucial for preparing future healthcare professionals to navigate the evolving landscape of AI in medicine. Recommendations: Policy efforts should focus on integrating AI education into the medical curriculum to ensure readiness for the technological advancements shaping the future of healthcare.

Artificial intelligence-assisted ultrasound imaging in hemophilia: research, development, and evaluation of hemarthrosis and synovitis detection.

Background: Joint bleeding can lead to synovitis and arthropathy in people with hemophilia, reducing quality of life. Although early diagnosis is associated with improved therapeutic outcomes, diagnostic ultrasonography requires specialist experience. Artificial intelligence (AI) algorithms may support ultrasonography diagnoses. Objectives: This study will research, develop, and evaluate the diagnostic precision of an AI algorithm for detecting the presence or absence of hemarthrosis and synovitis in people with hemophilia. Methods: Elbow, knee, and ankle ultrasound images were obtained from people with hemophilia from January 2010 to March 2022. The images were used to train and test the AI models to estimate the presence/absence of hemarthrosis and synovitis. The primary endpoint was the area under the curve for the diagnostic precision to diagnose hemarthrosis and synovitis. Other endpoints were the rate of accuracy, precision, sensitivity, and specificity. Results: Out of 5649 images collected, 3435 were used for analysis. The area under the curve for hemarthrosis detection for the elbow, knee, and ankle joints was ≥0.87 and for synovitis, it was ≥0.90. The accuracy and precision for hemarthrosis detection were ≥0.74 and ≥0.67, respectively, and those for synovitis were ≥0.83 and ≥0.74, respectively. Analysis across people with hemophilia aged 10 to 60 years showed consistent results. Conclusion: AI models have the potential to aid diagnosis and enable earlier therapeutic interventions, helping people with hemophilia achieve healthy and active lives. Although AI models show potential in diagnosis, evidence is unclear on required control for abnormal findings. Long-term observation is crucial for assessing impact on joint health.

Corneal densitometry changes after femtosecond laser-assisted intracorneal ring segments implantation in keratoconus.

Background: Intrastromal corneal ring segments are commonly implanted in the corneas of eyes with mild-to-moderate keratoconus; however, changes in corneal densitometry (CD) after implantation are a matter of debate in the current literature. We evaluated the changes in CD 1 and 3 months after femtosecond laser-assisted Keraring implantation. Methods: This retrospective, non-comparative, multicenter, case series study included patients with keratoconus who underwent femtosecond laser-assisted implantation of double segments with 90° and 160° arc lengths or two 160° arc length Keraring segments. Demographic and baseline clinical ophthalmic data were recorded. Corneal topography and tomography data acquired using a Pentacam HR Scheimpflug tomography system (Pentacam High Resolution; Oculus, Wetzlar, Germany) with a best-fit sphere were used as a reference surface. Using the Pentacam HR, CD measurements were acquired over a corneal area of 12 mm in total and at four concentric zones (0-2, 2-6, 6-10, and 10-12 mm) of three corneal stromal depths: 120 µm of the anterior corneal stromal layer, 60 µm of the posterior corneal stromal layer, and the central layer of stroma lying between these two layers. Results: We included 40 eyes of 40 patients, including 8 (20%) male and 32 (80%) female individuals, with a mean (standard deviation) age of 21.0 (6.4) years. We observed a significant improvement in the topographic values of steep keratometry (K), flat K, maximum K, and corneal astigmatism (all P < 0.05), but not in the mean K, thinnest corneal pachymetry, corneal thickness at the apex, back elevation, or front elevation (all P > 0.05). The mean total anterior, central, and posterior CD differed significantly among the time points, with a significant increase from the preoperative to the 1-month and 3-month postoperative visits (all P < 0.05) and no difference between those of the 1-month and 3-month postoperative visits (all P > 0.05). The mean CD for the anterior layer in the central, paracentral, and mid-peripheral zones, and the central layer in all four zones, differed significantly among time points, with a significant increase from the preoperative to the 1-month and 3-month postoperative visits (all P < 0.05), which remained unchanged from the 1-month to the 3-month postoperative visit (all P < 0.05), except for the central 2-6-mm zone, which decreased significantly from the 1-month to the 3-month postoperative visit (P < 0.001). The CD of the central 10-12-mm zone did not differ significantly in each pairwise comparison (all P > 0.05). In contrast, CD for the posterior layer in the paracentral zone decreased significantly from the preoperative to the 1-month and 3-month postoperative visits but increased, to a lesser extent, from the 1-month to the 3-month postoperative visit (all P < 0.05). Conclusions: Femtosecond laser-assisted Keraring implantation significantly changes CD, with improvement in most topography parameters. Further longitudinal studies with larger sample sizes are required to verify these preliminary findings.

AI-Enhanced Dyscalculia Screening: A Survey of Methods and Applications for Children.

New forms of interaction made possible by developments in special educational technologies can now help students with dyscalculia. Artificial intelligence (AI) has emerged as a promising tool in recent decades, particularly between 2001 and 2010, offering avenues to enhance the quality of education for individuals with dyscalculia. Therefore, the implementation of AI becomes crucial in addressing the needs of students with dyscalculia. Content analysis techniques were used to examine the literature covering the influence of AI on dyscalculia and its potential to assist instructors in promoting education for individuals with dyscalculia. The study sought to create a foundation for a more inclusive dyscalculia education in the future through in-depth studies. AI integration has had a big impact on educational institutions as well as people who struggle with dyscalculia. This paper highlights the importance of AI in improving the educational outcomes of students affected by dyscalculia.

Returning value from the All of Us research program to PhD-level nursing students using ChatGPT as programming support: results from a mixed-methods experimental feasibility study.

OBJECTIVE: We aimed to evaluate the feasibility of using ChatGPT as programming support for nursing PhD students conducting analyses using the All of Us Researcher Workbench. MATERIALS AND METHODS: 9 students in a PhD-level nursing course were prospectively randomized into 2 groups who used ChatGPT for programming support on alternating assignments in the workbench. Students reported completion time, confidence, and qualitative reflections on barriers, resources used, and the learning process. RESULTS: The median completion time was shorter for novices and certain assignments using ChatGPT. In qualitative reflections, students reported ChatGPT helped generate and troubleshoot code and facilitated learning but was occasionally inaccurate. DISCUSSION: ChatGPT provided cognitive scaffolding that enabled students to move toward complex programming tasks using the All of Us Researcher Workbench but should be used in combination with other resources. CONCLUSION: Our findings support the feasibility of using ChatGPT to help PhD nursing students use the All of Us Researcher Workbench to pursue novel research directions.

Race- and Ethnicity-Related Differences in Heart Failure With Preserved Ejection Fraction Using Natural Language Processing.

Background: Heart failure with preserved ejection fraction (HFpEF) is the predominant form of HF in older adults. It represents a heterogenous clinical syndrome that is less well understood across different ethnicities. Objectives: This study aimed to compare the clinical presentation and assess the diagnostic performance of existing HFpEF diagnostic tools between ethnic groups. Methods: A validated Natural Language Processing (NLP) algorithm was applied to the electronic health records of a large London hospital to identify patients meeting the European Society of Cardiology criteria for a diagnosis of HFpEF. NLP extracted patient demographics (including self-reported ethnicity and socioeconomic status), comorbidities, investigation results (N-terminal pro-B-type natriuretic peptide, H2FPEF scores, and echocardiogram reports), and mortality. Analyses were stratified by ethnicity and adjusted for socioeconomic status. Results: Our cohort consisted of 1,261 (64%) White, 578 (29%) Black, and 134 (7%) Asian patients meeting the European Society of Cardiology HFpEF diagnostic criteria. Compared to White patients, Black patients were younger at diagnosis and more likely to have metabolic comorbidities (obesity, diabetes, and hypertension) but less likely to have atrial fibrillation (30% vs 13%; P < 0.001). Black patients had lower N-terminal pro-B-type natriuretic peptide levels and a lower frequency of H2FPEF scores ≥6, indicative of likely HFpEF (26% vs 44%; P < 0.0001). Conclusions: Leveraging an NLP-based artificial intelligence approach to quantify health inequities in HFpEF diagnosis, we discovered that established markers systematically underdiagnose HFpEF in Black patients, possibly due to differences in the underlying comorbidity patterns. Clinicians should be aware of these limitations and its implications for treatment and trial recruitment.

Evaluating ChatGPT-4V in chest CT diagnostics: a critical image interpretation assessment.

PURPOSE: To assess the diagnostic accuracy of ChatGPT-4V in interpreting a set of four chest CT slices for each case of COVID-19, non-small cell lung cancer (NSCLC), and control cases, thereby evaluating its potential as an AI tool in radiological diagnostics. MATERIALS AND METHODS: In this retrospective study, 60 CT scans from The Cancer Imaging Archive, covering COVID-19, NSCLC, and control cases were analyzed using ChatGPT-4V. A radiologist selected four CT slices from each scan for evaluation. ChatGPT-4V's interpretations were compared against the gold standard diagnoses and assessed by two radiologists. Statistical analyses focused on accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), along with an examination of the impact of pathology location and lobe involvement. RESULTS: ChatGPT-4V showed an overall diagnostic accuracy of 56.76%. For NSCLC, sensitivity was 27.27% and specificity was 60.47%. In COVID-19 detection, sensitivity was 13.64% and specificity of 64.29%. For control cases, the sensitivity was 31.82%, with a specificity of 95.24%. The highest sensitivity (83.33%) was observed in cases involving all lung lobes. The chi-squared statistical analysis indicated significant differences in Sensitivity across categories and in relation to the location and lobar involvement of pathologies. CONCLUSION: ChatGPT-4V demonstrated variable diagnostic performance in chest CT interpretation, with notable proficiency in specific scenarios. This underscores the challenges of cross-modal AI models like ChatGPT-4V in radiology, pointing toward significant areas for improvement to ensure dependability. The study emphasizes the importance of enhancing these models for broader, more reliable medical use.

Introducing AnatomyGPT: A customized artificial intelligence application for anatomical sciences education.

Artificial intelligence (AI) technologies are poised to become an increasingly important part of education in the anatomical sciences. OpenAI has also introduced generative pretrained transformers (GPTs), which are customizable versions of the standard ChatGPT application. There is little research that has explored the potential of GPTs to serve as intelligent tutoring systems for learning the anatomical sciences. The objective of this study was to describe the design and explore the performance of AnatomyGPT, a customized artificial intelligence application intended for anatomical sciences education. The AnatomyGPT application was configured with GPT Builder by uploading open-source textbooks as knowledge sources and by providing pedagogical instructions for how to interact with users. The performance of AnatomyGPT was compared with ChatGPT by evaluating the responses of both applications to prompts of the National Board of Medical Examiners (NBME) sample items with respect to accuracy, rationales, and citations. AnatomyGPT achieved high scores on the NBME sample items for Gross Anatomy, Embryology, Histology, and Neuroscience and scored comparably to ChatGPT. In addition, AnatomyGPT provided several citations in the responses that it generated, while ChatGPT provided none. Both GPTs provided rationales for all sample items. The customized AnatomyGPT application demonstrated preliminary potential as an intelligent tutoring system by generating responses with increased citations as compared with the standard ChatGPT application. The findings of this study suggest that instructors and students may wish to create their own custom GPTs for teaching and learning anatomy. Future research is needed to further develop and characterize the potential of GPTs for anatomy education.

ChatGPT performance on radiation technologist and therapist entry to practice exams.

BACKGROUND: The aim of this study was to describe the proficiency of ChatGPT (GPT-4) on certification style exams from the Canadian Association of Medical Radiation Technologists (CAMRT), and describe its performance across multiple exam attempts. METHODS: ChatGPT was prompted with questions from CAMRT practice exams in the disciplines of radiological technology, magnetic resonance (MRI), nuclear medicine and radiation therapy (87-98 questions each). ChatGPT attempted each exam five times. Exam performance was evaluated using descriptive statistics, stratified by discipline and question type (knowledge, application, critical thinking). Light's Kappa was used to assess agreement in answers across attempts. RESULTS: Using a passing grade of 65 %, ChatGPT passed the radiological technology exam only once (20 %), MRI all five times (100 %), nuclear medicine three times (60 %), and radiation therapy all five times (100 %). ChatGPT's performance was best on knowledge questions across all disciplines except radiation therapy. It performed worst on critical thinking questions. Agreement in ChatGPT's responses across attempts was substantial within the disciplines of radiological technology, MRI, and nuclear medicine, and almost perfect for radiation therapy. CONCLUSION: ChatGPT (GPT-4) was able to pass certification style exams for radiation technologists and therapists, but its performance varied between disciplines. The algorithm demonstrated substantial to almost perfect agreement in the responses it provided across multiple exam attempts. Future research evaluating ChatGPT's performance on standardized tests should consider using repeated measures.

Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction.

INTRODUCTION: Point-of-care ultrasound has become a universal practice, employed by physicians across various disciplines, contributing to diagnostic processes and decision-making. AIM: To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes. METHODS: Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020. RESULTS: The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083-6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure. CONCLUSION: AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients.

Geriatrics and artificial intelligence in Spain (Ger-IA project): talking to ChatGPT, a nationwide survey.

PURPOSE: The purposes of the study was to describe the degree of agreement between geriatricians with the answers given by an AI tool (ChatGPT) in response to questions related to different areas in geriatrics, to study the differences between specialists and residents in geriatrics in terms of the degree of agreement with ChatGPT, and to analyse the mean scores obtained by areas of knowledge/domains. METHODS: An observational study was conducted involving 126 doctors from 41 geriatric medicine departments in Spain. Ten questions about geriatric medicine were posed to ChatGPT, and doctors evaluated the AI's answers using a Likert scale. Sociodemographic variables were included. Questions were categorized into five knowledge domains, and means and standard deviations were calculated for each. RESULTS: 130 doctors answered the questionnaire. 126 doctors (69.8% women, mean age 41.4 [9.8]) were included in the final analysis. The mean score obtained by ChatGPT was 3.1/5 [0.67]. Specialists rated ChatGPT lower than residents (3.0/5 vs. 3.3/5 points, respectively, P < 0.05). By domains, ChatGPT ​​scored better (M: 3.96; SD: 0.71) in general/theoretical questions rather than in complex decisions/end-of-life situations (M: 2.50; SD: 0.76) and answers related to diagnosis/performing of complementary tests obtained the lowest ones (M: 2.48; SD: 0.77). CONCLUSION: Scores presented big variability depending on the area of knowledge. Questions related to theoretical aspects of challenges/future in geriatrics obtained better scores. When it comes to complex decision-making, appropriateness of the therapeutic efforts or decisions about diagnostic tests, professionals indicated a poorer performance. AI is likely to be incorporated into some areas of medicine, but it would still present important limitations, mainly in complex medical decision-making.

Numerical investigation of the optimal porosity of titanium foam for dental implants.

Background: This paper aims to indicate numerically the accurate porosity used for dental implants, following the emphasis on the preference for titanium foam on pure titanium implants. A 3D-optimized numerical model is created to demonstrate the detailed differences between models. Method: A 3D finite element model was generated using Abaqus for titanium and titanium foam implants with different porosities (50,60,62.5,70, and 80%) fixed in cortical and cancellous bone. The mechanical data for titanium foam is extracted from published literature. We evaluate an artificial intelligent equation for the stress-strain response of titanium foam with various porosities to describe their variations. Results: To evaluate the stress-strain variations for different porosities, exponential artificial intelligence provides high accuracy (>0.99). The numerical results show that titanium foam implants appear to transfer more loads to the bordering bones due to their lower stiffness and higher energy absorption, which can help reduce stress shielding problems. In surrounding bones, the maximum VM stress occurs at the neck region from 5.42 MPa for pure titanium to 21.53 MPa for titanium foam with 80% porosity. Additionally, a porosity of 62.5% appears to be the most suitable since Young's modulus for this porosity (13.82 GPa) is close to the cortical bone's modulus (14.5 GPa). This suitability is shown in FEA by the similarity in stress level between pure titanium and the corresponding porosity. Overall, titanium foam implants appear to be a promising option for improving the effectiveness and longevity of bone implants in surgical dentistry. Conclusion: Systematic numerical studies on titanium foam dental implants with different porosities. Analysis of the FE results shows that titanium foam with a porosity of 62.5% is more beneficial for use in dental implants.

Exploring the role of ChatGPT in clinical decision-making in otorhinolaryngology: a ChatGPT designed study.

PURPOSE: Since the beginning of 2023, ChatGPT emerged as a hot topic in healthcare research. The potential to be a valuable tool in clinical practice is compelling, particularly in improving clinical decision support by helping physicians to make clinical decisions based on the best medical knowledge available. We aim to investigate ChatGPT's ability to identify, diagnose and manage patients with otorhinolaryngology-related symptoms. METHODS: A prospective, cross-sectional study was designed based on an idea suggested by ChatGPT to assess the level of agreement between ChatGPT and five otorhinolaryngologists (ENTs) in 20 reality-inspired clinical cases. The clinical cases were presented to the chatbot on two different occasions (ChatGPT-1 and ChatGPT-2) to assess its temporal stability. RESULTS: The mean score of ChatGPT-1 was 4.4 (SD 1.2; min 1, max 5) and of ChatGPT-2 was 4.15 (SD 1.3; min 1, max 5), while the ENTs mean score was 4.91 (SD 0.3; min 3, max 5). The Mann-Whitney U test revealed a statistically significant difference (p < 0.001) between both ChatGPT's and the ENTs's score. ChatGPT-1 and ChatGPT-2 gave different answers in five occasions. CONCLUSIONS: Artificial intelligence will be an important instrument in clinical decision-making in the near future and ChatGPT is the most promising chatbot so far. Despite needing further development to be used with safety, there is room for improvement and potential to aid otorhinolaryngology residents and specialists in making the most correct decision for the patient.

Potential of CDC25 phosphatases in cancer research and treatment: key to precision medicine.

The global burden of cancer continues to rise, underscoring the urgency of developing more effective and precisely targeted therapies. This comprehensive review explores the confluence of precision medicine and CDC25 phosphatases in the context of cancer research. Precision medicine, alternatively referred to as customized medicine, aims to customize medical interventions by taking into account the genetic, genomic, and epigenetic characteristics of individual patients. The identification of particular genetic and molecular drivers driving cancer helps both diagnostic accuracy and treatment selection. Precision medicine utilizes sophisticated technology such as genome sequencing and bioinformatics to elucidate genetic differences that underlie the proliferation of cancer cells, hence facilitating the development of customized therapeutic interventions. CDC25 phosphatases, which play a crucial role in governing the progression of the cell cycle, have garnered significant attention as potential targets for cancer treatment. The dysregulation of CDC25 is a characteristic feature observed in various types of malignancies, hence classifying them as proto-oncogenes. The proteins in question, which operate as phosphatases, play a role in the activation of Cyclin-dependent kinases (CDKs), so promoting the advancement of the cell cycle. CDC25 inhibitors demonstrate potential as therapeutic drugs for cancer treatment by specifically blocking the activity of CDKs and modulating the cell cycle in malignant cells. In brief, precision medicine presents a potentially fruitful option for augmenting cancer research, diagnosis, and treatment, with an emphasis on individualized care predicated upon patients' genetic and molecular profiles. The review highlights the significance of CDC25 phosphatases in the advancement of cancer and identifies them as promising candidates for therapeutic intervention. This statement underscores the significance of doing thorough molecular profiling in order to uncover the complex molecular characteristics of cancer cells.

Is ChatGPT 'ready' to be a learning tool for medical undergraduates and will it perform equally in different subjects? Comparative study of ChatGPT performance in tutorial and case-based learning questions in physiology and biochemistry.

PURPOSE: Generative AI will become an integral part of education in future. The potential of this technology in different disciplines should be identified to promote effective adoption. This study evaluated the performance of ChatGPT in tutorial and case-based learning questions in physiology and biochemistry for medical undergraduates. Our study mainly focused on the performance of GPT-3.5 version while a subgroup was comparatively assessed on GPT-3.5 and GPT-4 performances. MATERIALS AND METHODS: Answers were generated in GPT-3.5 for 44 modified essay questions (MEQs) in physiology and 43 MEQs in biochemistry. Each answer was graded by two independent examiners. Subsequently, a subset of 15 questions from each subject were selected to represent different score categories of the GPT-3.5 answers; responses were generated in GPT-4, and graded. RESULTS: The mean score for physiology answers was 74.7 (SD 25.96). GPT-3.5 demonstrated a statistically significant (p = .009) superior performance in lower-order questions of Bloom's taxonomy in comparison to higher-order questions. Deficiencies in the application of physiological principles in clinical context were noted as a drawback. Scores in biochemistry were relatively lower with a mean score of 59.3 (SD 26.9) for GPT-3.5. There was no statistically significant difference in the scores for higher and lower-order questions of Bloom's taxonomy. The deficiencies highlighted were lack of in-depth explanations and precision. The subset of questions where the GPT-4 and GPT-3.5 were compared demonstrated a better overall performance in GPT-4 responses in both subjects. This difference between the GPT-3.5 and GPT-4 performance was statistically significant in biochemistry but not in physiology. CONCLUSIONS: The differences in performance across the two versions, GPT-3.5 and GPT-4 across the disciplines are noteworthy. Educators and students should understand the strengths and limitations of this technology in different fields to effectively integrate this technology into teaching and learning.

Eficacia de la capacidad y la eficiencia pronósticas de la herramienta de inteligencia artificial Thoracic Care Suite de GE aplicada a la radiografía torácica de pacientes con neumonía COVID-19 / Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia

Objetivo: La rápida progresión de la neumonía COVID-19 puede implicar la necesidad de recurrir a sistemas de respiración asistida, como la ventilación mecánica no invasiva o la intubación endotraqueal. La introducción de herramientas que detecten la neumonía COVID-19 puede mejorar la atención sanitaria de los pacientes. Nuestro objetivo es evaluar la eficacia y la eficiencia de la herramienta de inteligencia artificial (IA) Thoracic Care Suite de GE Healthcare (que incorpora Lunit Insight CXR) para predecir la necesidad de recurrir a la respiración asistida en función de la progresión de la neumonía en la COVID-19 en radiografías torácicas consecutivas. Métodos: Se incluyó a pacientes ambulatorios con infección por SARS-CoV-2 confirmada, con hallazgos probables o indeterminados de neumonía COVID-19 en la radiografía torácica (RXT) y que necesitaron una segunda RXT debido a la evolución clínica desfavorable. En las 2RXT se evaluaron el número de campos pulmonares afectados mediante la herramienta de IA. Resultados: Se incluyó a 114 pacientes (57,4±14,2 años; 65 de ellos varones, el 57%) de forma retrospectiva; 15 pacientes (el 13,2%) precisaron respiración asistida. La progresión de la diseminación neumónica ≥0,5 campos pulmonares al día en comparación con el inicio de la neumonía, detectada mediante la herramienta TCS, cuadruplicó el riesgo de precisar respiración asistida. El análisis de los resultados de IA precisó 26 segundos. Conclusiones: Aplicar la herramienta de IA, Thoracic Care Suite, a la RXT de pacientes con neumonía COVID-19 nos permite predecir la necesidad de recurrir a la respiración asistida en menos de medio minuto.(AU)

Objective: Rapid progression of COVID-19 pneumonia may put patients at risk of requiring ventilatory support, such as non-invasive mechanical ventilation or endotracheal intubation. Implementing tools that detect COVID-19 pneumonia can improve the patient's healthcare. We aim to evaluate the efficacy and efficiency of the artificial intelligence (AI) tool GE Healthcare's Thoracic Care Suite (featuring Lunit Insight CXR, TCS) to predict the ventilatory support need based on pneumonic progression of COVID-19 on consecutive chest X-rays. Methods: Outpatients with confirmed SARS-CoV-2 infection, with chest X-ray (CXR) findings probable or indeterminate for COVID-19 pneumonia, who required a second CXR due to unfavorable clinical course, were collected. The number of affected lung fields for the 2CXRs was assessed using the AI tool. Results: One hundred fourteen patients (57.4±14.2 years; 65 of them were men, 57%) were retrospectively collected; and 15 (13.2%) required ventilatory support. Progression of pneumonic extension ≥ 0.5 lung fields per day compared to pneumonia onset, detected using the TCS tool, increased the risk of requiring ventilatory support by 4-fold. Analyzing the AI output required 26seconds of radiological time. Conclusions: Applying the AI tool, Thoracic Care Suite, to CXR of patients with COVID-19 pneumonia allows us to anticipate ventilatory support requirements requiring less than half a minute.(AU)

Clasificadores de aprendizaje supervisado no lineales basados en radiómica de la TC cerebral sin contraste para predecir el pronóstico funcional en pacientes con hematoma intracerebral espontáneo / Radiomic-based nonlinear supervised learning classifiers on non-contrast CT to predict functional prognosis in patients with spontaneous intracerebral hematoma

Objetivo: Evaluar si clasificadores de aprendizaje supervisado no lineales basados en radiómica de la TC cerebral sin contraste (TCCSC) pueden predecir el pronóstico funcional al alta en pacientes con hematoma intracerebral espontáneo (HIE). Material y método: Análisis observacional retrospectivo y unicéntrico de pacientes con diagnóstico de HIE confirmado por TCCSC entre enero de 2016 y abril de 2018. Se incluyeron pacientes con HIE >18años y con TCCSC realizado dentro de las primeras 24horas del inicio de los síntomas. Se excluyeron los HIE secundarios y en los que no se disponía de las variables de radiómica. Se recogieron datos clínicos, demográficos y variables al ingreso. Los pacientes se clasificaron según la Escala Modificada de Rankin (mRS) al alta en buen (mRS0-2) y mal pronóstico (mRS3-6). Tras la segmentación manual de la TCCSC de cada HIE se obtuvieron las variables de radiómica. La muestra se dividió en una cohorte de entrenamiento y prueba y otra cohorte de validación (70-30%, respectivamente). Se usaron diferentes métodos de selección de variables y reducción de dimensionalidad, así como diferentes algoritmos para la construcción del modelo. Se realizaron 10 iteraciones de validación cruzada estratificada en la cohorte de entrenamiento y prueba y se calculó la media de los valores de área bajo la curva (AUC). Una vez entrenados los modelos, se calculó la sensibilidad de cada uno para predecir el pronóstico funcional al alta en la cohorte de validación. Resultados: Se analizaron 105 pacientes con HIE. Se evaluaron 105 variables de radiómica de cada paciente. Los algoritmos P-SVM, KNN-E y RF-10, en combinación con el método de selección de variables ANOVA, fueron los clasificadores con mejor rendimiento en la cohorte de entrenamiento y prueba (AUC: 0,798, 0,752 y 0,742, respectivamente)...(AU)

Purpose: To evaluate if nonlinear supervised learning classifiers based on non-contrast cerebral CT can predict functional prognosis at discharge in patients with spontaneous intracerebral hematoma (HIE). Methods: Retrospective, single-center, observational analysis of patients with a diagnosis of spontaneous intracerebral hematoma confirmed by non-contrast CT between January 2016 and April 2018. Patients with HIE >18years and with non-contrast CT performed within the first 24hours of symptom onset were included. Patients with secondary spontaneous intracerebral hematoma and in whom radiomic variables were not available were excluded. Clinical, demographic and admission variables were collected. Patients were classified according to the Modified Rankin Scale (mRS) at discharge into good (mRS0-2) and poor prognosis (mRS3-6). After manual segmentation of each spontaneous intracerebral hematoma, the radiomics variables were obtained. The sample was divided into a training and testing cohort and a validation cohort (70-30%, respectively). Different methods of variable selection and dimensionality reduction were used, and different algorithms were used for model construction. Stratified 10-fold cross-validation were performed on the training and testing cohort and the mean area under the curve (AUC) were calculated. Once the models were trained, the sensitivity of each was calculated to predict functional prognosis at discharge in the validation cohort. Results: 105 patients with spontaneous intracerebral hematoma were analyzed. 105 radiomic variables were evaluated for each patient. P-SVM, KNN-E and RF-10 algorithms, in combination with the ANOVA variable selection method, were the best performing classifiers in the training and testing cohort (AUC: 0.798, 0.752 and 0.742, respectively)...(AU)