Use of Digital Health Technologies for Dementia Care: Bibliometric Analysis and Report by Hebatullah Abdulazeem, Israel Júnior Borges do Nascimento, Ishanka Weerasekara, Amin Sharifan, Victor Grandi Bianco, Ciara Cunningham, Indunil Kularathne, Genevieve Deeken, Jerome de Barros, Brijesh Sathian, Lasse Østengaard, Frederique Lamontagne-Godwin, Joost van Hoof, Ledia Lazeri, Cassie Redlich, Hannah R Marston, Ryan Alistair Dos Santos, Natasha Azzopardi-Muscat, Yongjie Yon, David Novillo-Ortiz

“…ConclusionsOur study shows a steady increase in dementia- and DHT-related publications, particularly in areas such as mobile health, virtual reality, artificial intelligence, and sensor-based technologies interventions. …”
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Satisfactory Evaluation of Call Service Using AI After Ureteral Stent Insertion: Randomized Controlled Trial by Ukrae Cho, Yong Nam Gwon, Seung Ryong Chong, Ji Yeon Han, Do Kyung Kim, Seung Whan Doo, Won Jae Yang, Kyeongmin Kim, Sung Ryul Shim, Jaehun Jung, Jae Heon Kim

“…While the advancement of artificial intelligence (AI) technology has led to its increasing application in the health sector, AI has not been used to provide information on potential complications and to facilitate subsequent measures in the event of such complications. …”
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Unveiling GPT-4V's hidden challenges behind high accuracy on USMLE questions: Observational Study by Zhichao Yang, Zonghai Yao, Mahbuba Tasmin, Parth Vashisht, Won Seok Jang, Feiyun Ouyang, Beining Wang, David McManus, Dan Berlowitz, Hong Yu

“… BackgroundRecent advancements in artificial intelligence, such as GPT-3.5 Turbo (OpenAI) and GPT-4, have demonstrated significant potential by achieving good scores on text-only United States Medical Licensing Examination (USMLE) exams and effectively answering questions from physicians. …”
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Convolutional neural networks for sea surface data assimilation in operational ocean models: test case in the Gulf of Mexico by O. Zavala-Romero, O. Zavala-Romero, A. Bozec, E. P. Chassignet, J. R. Miranda, J. R. Miranda

“…</p> <p>This research uses artificial intelligence to enhance ocean forecasting in the Gulf of Mexico. …”
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Analysis of acute non-pharmaceutical toxic exposures in children: a 5-year retrospective study by Yanning Qu, Rui Tang, Zhuyan Duan, Mengyi Sheng, Hui Wang, Shuang Liu, Jiao Li, Linlin Guo, Linying Guo, Si Zheng, Si Zheng

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Biomarker combinations from different modalities predict early disability accumulation in multiple sclerosis by Vinzenz Fleischer, Tobias Brummer, Muthuraman Muthuraman, Muthuraman Muthuraman, Falk Steffen, Milena Heldt, Maria Protopapa, Muriel Schraad, Gabriel Gonzalez-Escamilla, Sergiu Groppa, Stefan Bittner, Frauke Zipp

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Spatial Characterization of Woody Species Diversity in Tropical Savannas Using GEDI and Optical Data by Franciel Eduardo Rex, Carlos Alberto Silva, Eben North Broadbent, Ana Paula Dalla Corte, Rodrigo Leite, Andrew Hudak, Caio Hamamura, Hooman Latifi, Jingfeng Xiao, Jeff W. Atkins, Cibele Amaral, Ernandes Macedo da Cunha Neto, Adrian Cardil, Angelica M. Almeyda Zambrano, Veraldo Liesenberg, Jingjing Liang, Danilo Roberti Alves De Almeida, Carine Klauberg

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Investigation on Circadian Regulation of Four-Primary LED Display Differed in People by GA by Wenhui Li, Junying Li, Yating Feng, Yi Lin, Haoyang Liu, Ruoping Lian, Qinzhen Lin, Jingyi Cui, Zixin Huang, Yaling Chai, Ziquan Guo, Lili Zheng, Zhong Chen

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Ultrasound and histopathological assessment of benign, borderline, and malignant thyroid tumors in pediatric patients: an illustrative review and literature overview by Dominika Januś, Dominika Januś, Monika Kujdowicz, Monika Kujdowicz, Aleksandra Kiszka-Wiłkojć, Aleksandra Kiszka-Wiłkojć, Konrad Kaleta, Anna Taczanowska-Niemczuk, Anna Taczanowska-Niemczuk, Jan Radliński, Kamil Możdżeń, Zuzanna Nowak, Wojciech Górecki, Wojciech Górecki, Jerzy B. Starzyk, Jerzy B. Starzyk

“…Future studies utilizing artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics may offer new advancements, particularly given the increasing number of pediatric patients with solid thyroid lesions.…”
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Kontrol Level Kecepatan Kipas Melalui Deteksi Gestur Jari Tangan Menggunakan MediaPipe dan Faster-RCNN by Muhammad Aldi Fakhruddin, Heri Pratikno, Musayyanah, Weny Indah Kusumawati

“…In the era of Society 5.0, all applications of technology that have the main goal are not only to facilitate human work but how technology can better understand and understand humans, so that's where Artificial Intelligence is applied. In this study, an interaction control system was applied between the user and the computer to adjust the fan speed level automatically and in real time based on computer vision technology for deep learning through the detection of the shape of the right hand finger gesture and the left hand finger gesture using a webcam. …”
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A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer by Yin Li, Yin Li, Yin Li, Shuang Li, Shuang Li, Ruolin Xiao, Ruolin Xiao, Xi Li, Yongju Yi, Yongju Yi, Liangyou Zhang, Liangyou Zhang, You Zhou, You Zhou, Yun Wan, Chenhua Wei, Liming Zhong, Liming Zhong, Wei Yang, Wei Yang, Lin Yao, Lin Yao, Lin Yao

“…ObjectiveAccurate preoperative evaluation of rectal cancer lung metastases (RCLM) is critical for implementing precise medicine. While artificial intelligence (AI) methods have been successful in detecting liver and lymph node metastases using magnetic resonance (MR) images, research on lung metastases is still limited. …”
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Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging by Oliver Lester Saldanha, Jiefu Zhu, Gustav Müller-Franzes, Zunamys I. Carrero, Nicholas R. Payne, Lorena Escudero Sánchez, Paul Christophe Varoutas, Sreenath Kyathanahally, Narmin Ghaffari Laleh, Kevin Pfeiffer, Marta Ligero, Jakob Behner, Kamarul A. Abdullah, Georgios Apostolakos, Chrysafoula Kolofousi, Antri Kleanthous, Michail Kalogeropoulos, Cristina Rossi, Sylwia Nowakowska, Alexandra Athanasiou, Raquel Perez-Lopez, Ritse Mann, Wouter Veldhuis, Julia Camps, Volkmar Schulz, Markus Wenzel, Sergey Morozov, Alexander Ciritsis, Christiane Kuhl, Fiona J. Gilbert, Daniel Truhn, Jakob Nikolas Kather

“…While this increase poses challenges for radiologists, artificial intelligence (AI) offers potential solutions to manage this workload. …”
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Temporal trends in guideline-recommended medical therapy after an acute heart failure decompensation event: an observational analysis from Generator Heart Failure DataMart by Antonio Gasbarrini, Giuseppe Patti, Filippo Crea, Giovanni Scambia, Domenico D'Amario, Vincenzo Valentini, Gianluigi Savarese, Alfredo Cesario, Jacopo Lenkowicz, Stefano Patarnello, Renzo Laborante, Agni Delvinioti, Andrada Mihaela Tudor, Chiara Iacomini, Antonio Iaconelli, Donato Antonio Paglianiti, Mattia Galli, Daniele Rodolico, Attilio Restivo, Giuseppe Ciliberti, Gaetano Rizzo, Emiliano Bianchini, Matteo Busti, Luca Sensini

“…Objectives To evaluate the trend of prescription of the four foundational therapies, and their impact on 30-day urgent re-admissions and all-cause death in patients with heart failure and reduced ejection fraction (HFrEF) following an acute decompensation event.Design Retrospective.Setting One tertiary referral centre.Participants 999 consecutively patients admitted with a primary diagnosis of HFrEF between January 2020 and June 2023 were identified through a validated, high‐performance technology infrastructure based on artificial intelligence. The entire cohort was divided into three time periods based on two time points: September 2021 (ie, the release of the latest European guidelines) and January 2022 (ie, reimbursement for sodium-glucose cotransporter 2 (SGLT2) inhibitors).Primary and secondary outcome measures Trends and predictors of the prescription of each of the four foundational therapies and of the composite of all-cause death and rehospitalisation for urgent causes at 30 days.Results Among the 999 included patients, β-blockers were prescribed in 93% of patients, ACE inhibitor (ACEi)/angiotensin receptor blocker (ARB)/angiotensin–neprilysin receptor inhibitor (ARNi) in 73%, mineralocorticoid receptor antagonist in 30% and SGLT2 inhibitors in 18%. …”
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Logistic Regression Model in a Machine Learning Application to Predict Elderly Kidney Transplant Recipients with Worse Renal Function One Year after Kidney Transplant: Elderly KTbo... by Ubiracé Fernando Elihimas Júnior, Jamila Pinho Couto, Wallace Pereira, Michel Pompeu Barros de Oliveira Sá, Eduardo Eriko Tenório de França, Filipe Carrilho Aguiar, Diogo Buarque Cordeiro Cabral, Saulo Barbosa Vasconcelos Alencar, Saulo José da Costa Feitosa, Thais Oliveira Claizoni dos Santos, Helen Conceição dos Santos Elihimas, Emilly Pereira Alves, Marcio José de Carvalho Lima, Frederico Castelo Branco Cavalcanti, Paulo Adriano Schwingel

“…The model presented goodness-fit in the evaluation of artificial intelligence metrics (precision: 90%; sensitivity: 71%; and F1 score: 0.79). …”
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Integrated Framework and Technical Path for Multi-level Nested Assessment of Landscape Character by Yuncai WANG, Qizhen DONG

“…The multi-level nested assessment framework and technical path integrating LCA and LPA can accurately grasp the local characteristics of landscape through quantitative assessment and spatial mapping using artificial intelligence technology at different scales of practical needs. …”
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Differentiation of high risk prostate cancer with a facile urinary exosome detection workflow by Shuai Qiu, Yue Li, Zheng Zhang, Chunchang Li, Haoyu Wang, Ao Chen, Zhi Yan, Yang Liu, Zifei Li, Hua Huang, Yi Liu, Yiqi Seow, Ruibing Chen, Jinhong Guo, Simeng Wen, Jing Tian, Hongtuan Zhang, Ranlu Liu, Gang Han, Baolong Wang, Yong Wang, Yuanjie Niu, HaiFang Yin

“…Importantly, a prostate cancer urinary EVLatch-based artificial intelligence diagnostics (PURE-AID) classification system utilizing PCA3, HOXC6, and DLX1 as targets with SPDEF for reference and EEF1A1 for quality checking, trained on 271 patients, achieved an area under the receiver operating characteristic curve (AUROC) of 0.76 in the test set of 351 patients. …”
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Protocol for Home-Based Solution for Remote Atrial Fibrillation Screening to Prevent Recurrence Stroke (HUA-TUO AF Trial): a randomised controlled trial by Jing Tan, Yan Liang, Yong Liu, Wei Jiang, Duo Huang, Ning Tan, Chung-Wah Siu, Jo Jo Hai, Xiaoyu Huang, Jianfeng Ye, Ji-yan Chen, Chun Ka Wong, Mi Zhou, Li-Xue Yin, Ming-Liang Zuo, Kui Kai Lau, Zhen Lei, Yingqing Feng, Bryan P Yan, Wen-Sheng Yue, Wen-Jun Zhang, Yuk-Ming Lau, Hin-Wai LUI, Koon-Ho Chan, Toi Meng Mok, Weng-Chio Tam, Kun-Chong Tam, Xiuhua Feng, Xiaofei Jiang, Guanming Tan, Mario Alberto Evora

“…All remotely obtained data will be automatically transmitted in real-time through the study smartphone application to a secured cloud hosting and analysed using an artificial intelligence-based diagnostic system. When a diagnosis of AF is made with the system, the patients will be called back for a formal cardiology consultation within 1 week. …”
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Navigating Ethical Dilemmas Of Generative AI In Medical Writing by Qurrat Ulain Hamdan, Waleed Umar, Mahnoor Hasan

“…Like the steam engine in the 18th century, electricity in the 19th century, and the Internet at the cusp of the 20th and 21st centuries, the modern era is undergoing a new revolution with the advent of Artificial Intelligence (AI). AI is a field of computer science which deals with the development of programs or computational models that are inspired by the human brain’s ability to learn and adapt.1 Perhaps the most prominent advancement AI contributes is generative AI, which has led to the development of robotic “human-like” tools that can generate text, audio, images, and even video with a simple prompt. …”
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Voice EHR: introducing multimodal audio data for health by James Anibal, James Anibal, Hannah Huth, Ming Li, Lindsey Hazen, Veronica Daoud, Dominique Ebedes, Yen Minh Lam, Hang Nguyen, Phuc Vo Hong, Michael Kleinman, Shelley Ost, Christopher Jackson, Laura Sprabery, Cheran Elangovan, Balaji Krishnaiah, Lee Akst, Lee Akst, Ioan Lina, Iqbal Elyazar, Lenny Ekawati, Stefan Jansen, Richard Nduwayezu, Charisse Garcia, Jeffrey Plum, Jacqueline Brenner, Miranda Song, Emily Ricotta, Emily Ricotta, David Clifton, C. Louise Thwaites, Yael Bensoussan, Bradford Wood

“…IntroductionArtificial intelligence (AI) models trained on audio data may have the potential to rapidly perform clinical tasks, enhancing medical decision-making and potentially improving outcomes through early detection. …”
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Three-dimensional direct lithography of stable quantum dots in hybrid glass by Dezhi Zhu, Shangben Jiang, Ying Wang, Dejun Liu, Weijia Bao, Liwei Liu, Junle Qu, Yiping Wang, Changrui Liao

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