Automated and code-free development of a risk calculator using ChatGPT-4 for predicting diabetic retinopathy and macular edema without retinal imaging by Eun Young Choi, Joon Yul Choi, Tae Keun Yoo

“…The performance of the ChatGPT-4 developed models was comparable to those created using various machine-learning tools. Conclusion By utilizing ChatGPT-4 with code-free prompts, we overcame the technical barriers associated with using coding skills for developing prediction models, making it feasible to build a risk calculator for DR and DME prediction. …”
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Integrating microplastic research in sustainable agriculture: Challenges and future directions for food production by Marcelo Illanes, María-Trinidad Toro, Mauricio Schoebitz, Nelson Zapata, Diego A. Moreno, María Dolores López-Belchí

“…Currently, the application of omics technologies, including genomics, transcriptomics, and metabolomics, offers novel insights into molecular mechanisms that enable the identification of specific biomarkers associated with MP exposure. Furthermore, machine learning algorithms can be employed to analyze complex datasets, enhancing our ability to predict the impacts of MPs on plant health and crop performance under different environmental conditions. …”
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Assessing the Impact of Traffic Emissions on Fine Particulate Matter and Carbon Monoxide Levels in Hanoi through COVID-19 Social Distancing Periods by Nhung H. Le, Bich-Thuy Ly, Phong K. Thai, Gia-Huy Pham, Ich-Hung Ngo, Van-Nguyet Do, Thuy T. Le, Luan V. Nhu, Ha Dang Son, Yen-Lien T. Nguyen, Duong H. Pham, Tuan V. Vu

“…To overcome this challenge, weather normalized concentrations of those pollutants were estimated using the random forest model, a machine learning technique. The normalized weather concentrations showed smaller reductions by 7–10% for PM2.5 and 5–11% for CO, indicating the presence of favorable weather conditions for better air quality during the social distancing period. …”
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Diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian... by Somayyeh Noei Teymoordash, Hoda Zendehdel, Ali Reza Norouzi, Mahdis Kashian

“…Most studies agree that Artificial Neural Networks (ANN) and Machine Learning (ML) models outperform conventional statistics in predicting postoperative outcomes.…”
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Predicting exacerbation of renal function by DNA methylation clock and DNA damage of urinary shedding cells: a pilot study by Akihito Hishikawa, Erina Sugita Nishimura, Norifumi Yoshimoto, Ran Nakamichi, Eriko Yoshida Hama, Wataru Ito, Tomomi Maruki, Kengo Nagashima, Ryoko Shimizu-Hirota, Hiromasa Takaishi, Hiroshi Itoh, Kaori Hayashi

“…Abstract Recent reports have shown the feasibility of measuring biological age from DNA methylation levels in blood cells from specific regions identified by machine learning, collectively known as the epigenetic clock or DNA methylation clock. …”
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In Situ Classification of Original Rocks by Portable Multi-Directional Laser-Induced Breakdown Spectroscopy Device by Mengyang Zhang, Hongbo Fu, Huadong Wang, Feifan Shi, Saifullah Jamali, Zongling Ding, Bian Wu, Zhirong Zhang

“…This device built upon a previous multi-directional optimization scheme and integrated machine learning to classify seven types of original rock samples: mudstone, basalt, dolomite, sandstone, conglomerate, gypsolyte, and shale from oil logging sites. …”
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Enhancing antimicrobial resistance strategies: Leveraging artificial intelligence for improved outcomes by Aeshah M. Mohammed, Mohammed Mohammed, Jawad K. Oleiwi, Azlin F. Osman, Tijjani Adam, Bashir O. Betar, Subash C.B. Gopinath, Falah H. Ihmedee

“…By synthesizing current research and applications, the potential of AI-driven technologies—ranging from machine learning models that predict resistance patterns to algorithms enhancing antibiotic discovery—is illuminated to augment our arsenal against AMR. …”
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Risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer: a systematic review and meta-analysis by You Pu, Jin Yang, Lian Shui, Qianlong Tang, Xianqin Zhang, Guangguo Liu

“…Of these models, most were constructed based on logistic regression, while only two studies used machine learning methods. The area under the receiver operating characteristic curve (AUC) reported values for these models ranged from 0.57 to 0.909, with 13 studies having a combined AUC value of 0.78 (95% CI: 0.74-0.81). …”
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A Survey on Reconfigurable Intelligent Surface for Physical Layer Security of Next-Generation Wireless Communications by Ravneet Kaur, Bajrang Bansal, Sudhan Majhi, Sandesh Jain, Chongwen Huang, Chau Yuen

“…For multiple-input single-output (MISO) case, PLS strategies such as inducing artificial noise (AN), optimization algorithms, alternating optimization (AO), machine learning (ML) and deep learning (DL), and reflect matrices are discussed. …”
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Artificial intelligence driven clustering of blood pressure profiles reveals frailty in orthostatic hypertension by Claire M. Owen, Jaume Bacardit, Maw P. Tan, Nor I. Saedon, Choon‐Hian Goh, Julia L. Newton, James Frith

“…Given the richness of non‐invasive beat‐to‐beat data, artificial intelligence (AI) offers a solution to detect the subtle patterns within it. Applying machine learning to an existing dataset of community‐based adults undergoing postural assessment, we identified three distinct clusters (iOHYPO, OHYPO and OHYPER) akin to initial and classic orthostatic hypotension and orthostatic hypertension, respectively. …”
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Integrated Analysis and Validation of Ferroptosis-Related Genes Associated with Ischemia/Reperfusion Injury in Lung Transplantation by Li Q, Yin J, Lin Q, He J, Shi X, Nie H

“…Weighted gene co-expression network analysis (WGCNA) was used to identify module genes, and overlapping genes were further analyzed using two machine learning algorithms. The CIBERSORT algorithm was applied to assess immune cell infiltration, while Mendelian randomization (MR) analysis was used to investigate causal relationships between candidate genes and PGD. …”
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Identification of a novel immunogenic cell death-related classifier to predict prognosis and optimize precision treatment in hepatocellular carcinoma by Dongjing Zhang, Bingyun Lu, Qianqian Ma, Wen Xu, Qi Zhang, Zhiqi Xiao, Yuanheng Li, Ren Chen, An-jiang Wang

“…A reliable risk model named ICD score was constructed via machine learning algorithms to assess the immunological status, therapeutic responses, and clinical outcomes of individual HCC patients. …”
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Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products by Van Thuy Hoang, Tien-Bach-Thanh do, Jinho Seo, Seung Charlie Kim, Luong Vuong Nguyen, Duong Nguyen Minh Huy, Hyeon-Ju Jeon, O-Joun Lee

“…Recently, various machine learning-based strategies, e.g., image-based methods, have shown remarkable success in predicting the halal status of cosmetics. …”
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A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. by Ilaria Amodeo, Giorgio De Nunzio, Genny Raffaeli, Irene Borzani, Alice Griggio, Luana Conte, Francesco Macchini, Valentina Condò, Nicola Persico, Isabella Fabietti, Stefano Ghirardello, Maria Pierro, Benedetta Tafuri, Giuseppe Como, Donato Cascio, Mariarosa Colnaghi, Fabio Mosca, Giacomo Cavallaro

“…We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical data, to provide neonatal PH as the first outcome and, possibly: favorable response to fetal endoscopic tracheal occlusion (FETO), need for Extracorporeal Membrane Oxygenation (ECMO), survival to ECMO, and death. …”
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Big Data Governance Challenges Arising From Data Generated by Intelligent Systems Technologies: A Systematic Literature Review by Yunusa Adamu Bena, Roliana Ibrahim, Jamilah Mahmood, Arafat Al-Dhaqm, Ahmad Alshammari, Maged Nasser, Muhammed Nura Yusuf, Matthew O. Ayemowa

“…The exponential growth of intelligent systems technologies, including Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), and Smart Connected Products, has intensified the difficulties of data governance. …”
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PreciPalm: An Intelligent System for Calculating Macronutrient Status and Fertilizer Recommendations for Oil Palm on Mineral Soils Based on a Precision Agriculture Approach by Kudang Boro Seminar, Harry Imantho, null Sudradjat, Sudirman Yahya, Sirojul Munir, Indra Kaliana, Fajar Mei Haryadi, Awalia Noor Baroroh, null Supriyanto, Gani Cahyo Handoyo, Arif Kurnia Wijayanto, Cecep Ijang Wahyudin, null Liyantono, Rhavif Budiman, Achmad Bakir Pasaman, Dwi Rusiawan, null Sulastri

“…This research aims to determine macronutrients, specifically nitrogen (N), phosphorus (P), and potassium (K) contents in oil palm leaves based on PA principles using the integration of remote sensing technology and machine learning to quickly obtain the macronutrient status from oil palm plantation areas. …”
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Trends of Soil and Solution Nutrient Sensing for Open Field and Hydroponic Cultivation in Facilitated Smart Agriculture by Md Nasim Reza, Kyu-Ho Lee, Md Rejaul Karim, Md Asrakul Haque, Emmanuel Bicamumakuba, Pabel Kanti Dey, Young Yoon Jang, Sun-Ok Chung

“…Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. …”
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Early warning systems for identifying severe maternal outcomes: findings from the WHO global maternal sepsis studyResearch in context by Yamikani Chimwaza, Alexandra Hunt, Livia Oliveira-Ciabati, Laura Bonnett, Edgardo Abalos, Cristina Cuesta, João Paulo Souza, Mercedes Bonet, Vanessa Brizuela, David Lissauer, Yamikani Chimwaza, Alexandra Hunt, Livia Oliveira-Ciabati, Laura Bonnett, Edgardo Abalos, Cristina Cuesta, João Paulo Souza, Mercedes Bonet, Vanessa Brizuela, David Lissauer, Mohammad Iqbal Aman, Bashir Noormal, Marisa Espinoza, Julia Pasquale, Charlotte Leroy, Kristien Roelens, Griet Vandenberghe, M. Christian Urlyss Agossou, Sourou Goufodji Keke, Christiane Tshabu Aguemon, Patricia Soledad Apaza Peralta, Víctor Conde Altamirano, Rosalinda Hernández Muñoz, José Guilherme Cecatti, Carolina Ribeiro do Valle, Vincent Batiene, Kadari Cisse, Henri Gautier Ouedraogo, Kannitha Cheang, Phirun Lam, Tung Rathavy, Elie Simo, Pierre-Marie Tebeu, Emah Irene Yakana, Javier Carvajal, María Fernanda Escobar, Paula Fernández, Lotte Berdiin Colmorn, Jens Langhoff-Roos, Wilson Mereci, Paola Vélez, Yasser Salah Eldin, Alaa Sultan, Alula M. Teklu, Dawit Worku, Richard Adanu, Philip Govule, Charles Noora Lwanga, William Enrique Arriaga Romero, María Guadalupe Flores Aceituno, Carolina Bustillo, Bredy Lara, Vijay Kumar, Vanita Suri, Sonia Trikha, Irene Cetin, Serena Donati, Carlo Personeni, Guldana Baimussanova, Saule Kabylova, Balgyn Sagyndykova, George Gwako, Alfred Osoti, Zahida Qureshi, Raisa Asylbasheva, Aigul Boobekova, Damira Seksenbaeva, Saad Eddine Itani, Meilė Minkauskienė, Diana Ramašauskaitė, Owen Chikhwaza, Luis Gadama, Eddie Malunga, Haoua Dembele, Hamadoun Sangho, Fanta Eliane Zerbo, Filiberto Dávila Serapio, Nazarea Herrera Maldonado, Juan I. Islas Castañeda, Tatiana Cauaus, Ala Curteanu, Victor Petrov, Yadamsuren Buyanjargal, Seded Khishgee, Bat-Erdene Lkhagvasuren, Amina Essolbi, Rachid Moulki, Zara Jaze, Arlete Mariano, Nafissa Bique Osman, Hla Mya Thway Einda, Thae Maung Maung, Khaing Nwe Tin, Tara Gurung, Amir Babu Shrestha, Sangeeta Shrestha, Kitty Bloemenkamp, Marcus J. Rijken, Thomas Van Den Akker, María Esther Estrada, Néstor J. Pavón Gómez, Olubukola Adesina, Chris Aimakhu, Bukola Fawole, Rizwana Chaudhri, Saima Hamid, M. Adnan Khan, María del Pilar Huatuco Hernández, Nelly M. Zavaleta Pimentel, Maria Lu Andal, Zenaida Dy Recidoro, Carolina Paula Martin, Mihaela Budianu, Lucian Pușcașiu, Léopold Diouf, Dembo Guirassy, Philippe Marc Moreira, Miroslav Borovsky, Ladislav Kovac, Alexandra Kristufkova, Sylvia Cebekhulu, Laura Cornelissen, Priya Soma-Pillay, Vicenç Cararach, Marta López, María José Vidal Benedé, Hemali Jayakody, Kapila Jayaratne, Dhammica Rowel, Wisal Nabag, Sara Omer, Victoria Tsoy, Urunbish Uzakova, Dilrabo Yunusova, Thitiporn Siriwachirachai, Thumwadee Tangsiriwatthana, Catherine Dunlop, Marian Knight, Jhon Roman, Gerardo Vitureira, Dinh Anh Tuan, Luong Ngoc Truong, Nghiem Thi Xuan Hanh, Mugove Madziyire, Thulani Magwali, Stephen Munjanja, Adama Baguiya, Mónica Chamillard, Bukola Fawole, Marian Knight, Seni Kouanda, Pisake Lumbiganon, Ashraf Nabhan, Ruta Nadisauskiene, Linda Bartlett, Fernando Bellissimo-Rodrigues, Shevin T. Jacob, Sadia Shakoor, Khalid Yunis, Liana Campodónico, Hugo Gamerro, Daniel Giordano, Fernando Althabe, A. Metin Gülmezoglu

“…Furthermore, combinations of sepsis markers had very low sensitivity and high specificity using machine learning. Interpretation: No score demonstrated enough diagnostic accuracy to be used alone to identify sepsis. …”
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A Case Study on Multi-Real-Option-Integrated STO-PF Models for Strengthening Capital Structures in Real Estate Development by Jung Kyu Park, Jun Bok Lee, Young Mee Ahn, Ga Young Yoo

“…Additionally, in-depth research is necessary to integrate emerging technologies, such as artificial intelligence and machine learning, into multi-real-option-based financial platforms. …”
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Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT): A preliminary, scenario-based cross-sectional study by İbrahim Sarbay, Göksu Bozdereli Berikol, İbrahim Ulaş Özturan

“…OpenAI’s ChatGPT is a supervised and empowered machine learning-based chatbot. The aim of this study was to determine the performance of ChatGPT in emergency medicine (EM) triage prediction. …”
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