发布求助
文献互助 智能选刊 最新文献

Recenti progressi in medicina最新文献

筛选
英文 中文
Performance di large language models su quesiti a risposta multipla per la certificazione in medicina dei viaggi. 大型语言模型在旅行医学认证多项选择题上的表现。
Recenti progressi in medicina Pub Date : 2025-10-01 DOI: 10.1701/4573.45796
Angelo D'Ambrosio, Francesco Baglivo, Luigi De Angelis, Federico Tecchio, Caterina Rizzo
{"title":"Performance di large language models su quesiti a risposta multipla per la certificazione in medicina dei viaggi.","authors":"Angelo D'Ambrosio, Francesco Baglivo, Luigi De Angelis, Federico Tecchio, Caterina Rizzo","doi":"10.1701/4573.45796","DOIUrl":"https://doi.org/10.1701/4573.45796","url":null,"abstract":"<p><p>We benchmarked 40 LLMs on a 40 item travel medicine quiz. Bayesian modelling was used to evaluate accuracy, consistency, parsability, and cost metrics. Accuracy spanned 27.9-97.5%; reasoning tuned frontier models (OpenAI o3, Perplexity Sonar Reasoning) topped the benchmark, whereas local small underperformed. Cost accuracy curves revealed five Pareto optimal systems, with o3 being the current best. These findings confirm the performance of current LLMs as public health knowledge support systems.</p>","PeriodicalId":20887,"journal":{"name":"Recenti progressi in medicina","volume":"116 10","pages":"603-604"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep learning su dati clinici longitudinali e immunologici per la stratificazione della sindrome nefrosica. 深入学习肾病分层的临床纵向和免疫学数据。
Recenti progressi in medicina Pub Date : 2025-10-01 DOI: 10.1701/4573.45781
Giulia Ricci, Silvia Capuzzi, Martina Riganati, Alberto Eugenio Tozzi, Marina Vivarelli, Diana Ferro, Manuela Colucci
{"title":"Deep learning su dati clinici longitudinali e immunologici per la stratificazione della sindrome nefrosica.","authors":"Giulia Ricci, Silvia Capuzzi, Martina Riganati, Alberto Eugenio Tozzi, Marina Vivarelli, Diana Ferro, Manuela Colucci","doi":"10.1701/4573.45781","DOIUrl":"https://doi.org/10.1701/4573.45781","url":null,"abstract":"<p><p>This study explores lymphocyte profiles as non-invasive biomarkers for classification of pediatric nephrotic syndrome (NS). Using retrospective clinical and immunological data from 205 patients, the aim is to develop a predictive model based on Long Short-Term Memory to identify NS subtypes. By comparing models with and without immunological data, the study will assess the value of immune profiles. The goal is to support personalized management while reducing the need for invasive procedures.</p>","PeriodicalId":20887,"journal":{"name":"Recenti progressi in medicina","volume":"116 10","pages":"573-574"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Making the case for digital twins: Italian healthcare needs AI-driven predictive modeling for personalized medicine. 数字孪生案例：意大利医疗保健需要人工智能驱动的个性化医疗预测建模。
Recenti progressi in medicina Pub Date : 2025-10-01 DOI: 10.1701/4573.45777
Diana Ferro, Francesco Baglivo, Luigi De Angelis, Francesco Andrea Causio, Marcello Di Pumpo, Francesca Aurora Sacchi, Giacomo Diedenhofen, Alessio Pivetta, Alessandro Belpiede, Alberto Eugenio Tozzi
{"title":"Making the case for digital twins: Italian healthcare needs AI-driven predictive modeling for personalized medicine.","authors":"Diana Ferro, Francesco Baglivo, Luigi De Angelis, Francesco Andrea Causio, Marcello Di Pumpo, Francesca Aurora Sacchi, Giacomo Diedenhofen, Alessio Pivetta, Alessandro Belpiede, Alberto Eugenio Tozzi","doi":"10.1701/4573.45777","DOIUrl":"https://doi.org/10.1701/4573.45777","url":null,"abstract":"<p><p>Precision medicine seeks to tailor care by integrating genetic, clinical, and environmental data. Digital twins, dynamic, virtual replicas of patients that are updated with longitudinal information, represent a significant step in this direction. Enabled by artificial intelligence, they allow in silico experimentation to simulate therapies, disease trajectories, and adverse events, reducing risk and sharpening personalization. By bridging data and decisions, digital twins can promote earlier diagnosis, targeted treatments, and faster drug discovery, supporting a shift from reactive to predictive and participatory care. Nonetheless, challenges surrounding data integration, privacy, regulation, and equity persist and necessitate collaborative solutions. This viewpoint examines the opportunities and system-level requirements to integrate digital twins into Italian healthcare. Digital twins redefine medicine by turning episodic encounters into continuous, adaptive care. They can anticipate clinical events, simulate individualized treatments, and support shared decision-making, advancing the vision of predictive, preventive, personalized, and participatory medicine. Realizing this potential requires robust governance, interoperable infrastructures, and clinician training, alongside ethical frameworks that protect autonomy and fairness. Public-private partnerships and international collaboration will be crucial for the responsible, inclusive, and transparent adoption of these initiatives. Ultimately, digital twins inaugurate a paradigm in which simulation and clinical reality converge, fostering innovation that is both scientifically rigorous and deeply human.</p>","PeriodicalId":20887,"journal":{"name":"Recenti progressi in medicina","volume":"116 10","pages":"561-566"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Goffredo Fofi, uno spirito rivoluzionario. 戈弗雷多·福菲，革命精神。
Recenti progressi in medicina Pub Date : 2025-10-01 DOI: 10.1701/4573.45805
Domenico Ribatti
{"title":"Goffredo Fofi, uno spirito rivoluzionario.","authors":"Domenico Ribatti","doi":"10.1701/4573.45805","DOIUrl":"https://doi.org/10.1701/4573.45805","url":null,"abstract":"","PeriodicalId":20887,"journal":{"name":"Recenti progressi in medicina","volume":"116 10","pages":"621"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DermatAI: deep learning per la diagnosi del melanoma. 皮肤科:黑色素瘤诊断的深入学习。
Recenti progressi in medicina Pub Date : 2025-10-01 DOI: 10.1701/4573.45786
Giulia Cartei, Fabrizio di Sciorio
{"title":"DermatAI: deep learning per la diagnosi del melanoma.","authors":"Giulia Cartei, Fabrizio di Sciorio","doi":"10.1701/4573.45786","DOIUrl":"https://doi.org/10.1701/4573.45786","url":null,"abstract":"<p><p>DermatAI is the AI-based tool developed for early skin cancer detection, with a focus on Melanoma. Through an ensemble stacking model composed by convolutional neural networks and XGBoost classifier. DermatAI classifies skin lesions with high accuracy, aiding melanoma diagnosis and improving clinical decision support.</p>","PeriodicalId":20887,"journal":{"name":"Recenti progressi in medicina","volume":"116 10","pages":"583-584"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Modello multi-step basato su intelligenza artificiale per il timing chirurgico in oncologia pediatrica. 儿科肿瘤手术时间的人工智能多步模型。
Recenti progressi in medicina Pub Date : 2025-10-01 DOI: 10.1701/4573.45791
Silvia Capuzzi, Federico Baldisseri, Antonella Cacchione, Andrea Carai, Francesco Fabozzi, Antonio Pietrabissa, Angela Mastronuzzi, Alberto Eugenio Tozzi, Diana Ferro
{"title":"Modello multi-step basato su intelligenza artificiale per il timing chirurgico in oncologia pediatrica.","authors":"Silvia Capuzzi, Federico Baldisseri, Antonella Cacchione, Andrea Carai, Francesco Fabozzi, Antonio Pietrabissa, Angela Mastronuzzi, Alberto Eugenio Tozzi, Diana Ferro","doi":"10.1701/4573.45791","DOIUrl":"https://doi.org/10.1701/4573.45791","url":null,"abstract":"<p><p>This study presents a two-phase AI-based model to predict surgical wait times in paediatric oncology patients. Using real-world data from 1478 patients and 6145 surgeries, the model first classifies surgical urgency, then estimates wait times for urgent cases. Random Forest emerged as the best-performing algorithm in both phases, and SHAP analysis identified similar key predictive features. Results support AI's role in improving surgical planning, resource allocation, and clinical decision-making.</p>","PeriodicalId":20887,"journal":{"name":"Recenti progressi in medicina","volume":"116 10","pages":"593-594"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The path to trustworthy medical AI: the evolving role of explainability. 通往可信赖的医疗人工智能之路：可解释性的演变作用。
Recenti progressi in medicina Pub Date : 2025-10-01 DOI: 10.1701/4573.45774
Francesca Aurora Sacchi, Fidelia Cascini, Noemi Conditi, Alice Ravizza, Margherita Daverio, Francesco Andrea Causio, Vittorio De Vita, Alessio Pivetta, Pierpaolo Maio, Luigi De Angelis, Francesco Baglivo, Giacomo Diedenhofen, Marcello Di Pumpo, Alessandro Belpiede, Diana Ferro, Luca Bolognini
{"title":"The path to trustworthy medical AI: the evolving role of explainability.","authors":"Francesca Aurora Sacchi, Fidelia Cascini, Noemi Conditi, Alice Ravizza, Margherita Daverio, Francesco Andrea Causio, Vittorio De Vita, Alessio Pivetta, Pierpaolo Maio, Luigi De Angelis, Francesco Baglivo, Giacomo Diedenhofen, Marcello Di Pumpo, Alessandro Belpiede, Diana Ferro, Luca Bolognini","doi":"10.1701/4573.45774","DOIUrl":"https://doi.org/10.1701/4573.45774","url":null,"abstract":"<p><p>The integration of artificial intelligence (AI) in medicine has applications across several clinical domains, spanning from disease prevention and diagnosis through treatment and long-term care, as well as remote care. However, many AI systems are inherently characterized by limited explainability, meaning the processes behind their outcomes cannot be clearly understood or communicated to humans, whether developers or end users. This viewpoint explores the importance of AI explainability in medicine by first tracing its evolution from a primarily ethical concern to a legal requirement. It then examines the connection between explainability and the trustworthiness of AI systems. Finally, it considers how explainability is approached from a technical standpoint and its inherent tension with achieving high accuracy.</p>","PeriodicalId":20887,"journal":{"name":"Recenti progressi in medicina","volume":"116 10","pages":"546-550"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Valutazione comparativa di modelli linguistici di grandi dimensioni per il supporto all’educazione sanitaria del paziente con BPCO: uno studio pneumologico internazionale delle risposte generate da ChatGPT-4, Claude 3.5 Sonnet e Gemini 1.5 Advanced. 支持BPCO患者健康教育的大型语言模型的比较:一项关于ChatGPT-4、Claude 3.5十四行诗和双子座1.5高级响应的国际肺学研究。
Recenti progressi in medicina Pub Date : 2025-10-01 DOI: 10.1701/4573.45780
Guido Marchi, Giulia Gambini, Giacomo Guglielmi, Francesco Pistelli, Laura Carrozzi
{"title":"Valutazione comparativa di modelli linguistici di grandi dimensioni per il supporto all’educazione sanitaria del paziente con BPCO: uno studio pneumologico internazionale delle risposte generate da ChatGPT-4, Claude 3.5 Sonnet e Gemini 1.5 Advanced.","authors":"Guido Marchi, Giulia Gambini, Giacomo Guglielmi, Francesco Pistelli, Laura Carrozzi","doi":"10.1701/4573.45780","DOIUrl":"https://doi.org/10.1701/4573.45780","url":null,"abstract":"<p><p>Three LLMs - ChatGPT-4, Claude 3.5 Sonnet and Gemini 1.5 Advanced - were evaluated on COPD questions from the GOLD recommendations. Sixty-one pulmonologists from 6 continents rated 90 AI responses for completeness, accuracy, terminology, accessibility, and safety. Gemini outperformed in completeness, Claude in accuracy and terminology, with no differences in accessibility or safety. While promising, clinical use requires caution and further validation to ensure safe, accurate patient education.</p>","PeriodicalId":20887,"journal":{"name":"Recenti progressi in medicina","volume":"116 10","pages":"571-572"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Dalla letteratura
2025 Ottobre. 2025年10月。
Recenti progressi in medicina Pub Date : 2025-10-01 DOI: 10.1701/4573.45772
{"title":"Dalla letteratura<br>2025 Ottobre.","authors":"","doi":"10.1701/4573.45772","DOIUrl":"https://doi.org/10.1701/4573.45772","url":null,"abstract":"","PeriodicalId":20887,"journal":{"name":"Recenti progressi in medicina","volume":"116 10","pages":"541-542"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MedWriter: progettazione di un sistema di intelligenza artificiale per la generazione automatizzata di lettera di dimissione ospedaliera o trasferimento. MedWriter:人工智能系统的设计，用于自动生成医院出院或转移信。
Recenti progressi in medicina Pub Date : 2025-10-01 DOI: 10.1701/4573.45785
Jonathan Montomoli, Simone Iannaccone, Sergio Russo, Albenzo Coletta, Onofrio Cappucci, Mariano Folla, Valerio Placidi, Emanuele Frontoni, Francesco Giuliani
{"title":"MedWriter: progettazione di un sistema di intelligenza artificiale per la generazione automatizzata di lettera di dimissione ospedaliera o trasferimento.","authors":"Jonathan Montomoli, Simone Iannaccone, Sergio Russo, Albenzo Coletta, Onofrio Cappucci, Mariano Folla, Valerio Placidi, Emanuele Frontoni, Francesco Giuliani","doi":"10.1701/4573.45785","DOIUrl":"https://doi.org/10.1701/4573.45785","url":null,"abstract":"<p><p>The MedWriter project aims to create an AI-based clinical decision support system for automated discharge letter generation. Co-funded through Italian Sustainable Growth Fund and the EU, the project will utilize 1.3M patient records from HL7/FHIR-compliant SISWEB platform. The system will employ hybrid neural architectures including CNNs, transformers, and reinforcement learning for accurate clinical narrative synthesis.</p>","PeriodicalId":20887,"journal":{"name":"Recenti progressi in medicina","volume":"116 10","pages":"581-582"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信