An overview of artificial intelligence based automated diagnosis in paediatric dentistry by Suba B. Rajinikanth, Densingh Samuel Raj Rajkumar, Akshay Rajinikanth, Ponsekar Abraham Anandhapandian, Bhuvaneswarri J.

“…In machine learning a datasets are used for training diagnostic algorithms. This review comprehensively explains the applications of AI in the diagnosis in paediatric dentistry. …”
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Rheumatic diseases as a reason for emergency hospitalization by A. S. Povzun, V. I. Mazurov, E. V. Shchemeleva, K. A. Povzun, E. Yu. Kovalchuk, V. A. Kostenko

“…The study of the prevalence of pain in the projection of joints data, places and roles of rheumatological diseases (RD) allows to outline ways to optimize diagnostic and therapeutic algorithms, reducing the time of diseases diagnosis and optimizing the inpatient stage of treatment duration. …”
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Correlations between blood group and Rh factor in families and autism spectrum disorder: A comprehensive analysis by Adil Abdul-Rehman Siddiq Al-Salihy

“…Using chi-square tests, logistic regression analysis, and machine learning algorithms, the findings revealed that the O + blood group was the most prevalent across all groups. …”
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Convergence of nanotechnology and artificial intelligence in the fight against liver cancer: a comprehensive review by Manjusha Bhange, Darshan Telange

“…This review explores the convergence of nanotechnology and AI in liver cancer treatment, evaluating current progress, identifying existing research gaps, and discussing future directions. We highlight how AI-powered algorithms can optimize nanocarrier design, facilitate real-time monitoring of treatment efficacy, and enhance clinical decision-making. …”
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Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals’ perspectives in the UK by Saoudi CE Nouis, Victoria Uren, Srushti Jariwala

“…These challenges become especially critical in domains such as pathology and blood sciences, where opaque AI algorithms and non-representative datasets can impact clinical outcomes. …”
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Identification and validation of a KRAS-macrophage-associated gene signature as prognostic biomarkers and potential therapeutic targets in melanoma by Beichen Cai, Beichen Cai, Beichen Cai, Qian Lin, Qian Lin, Qian Lin, Ruonan Ke, Ruonan Ke, Ruonan Ke, Jiaqi Yu, Jiaqi Yu, Jiaqi Yu, Lu Chen, Lu Chen, Xuejun Ni, Xuejun Ni, Hekun Liu, Xiuying Shan, Xiuying Shan, Biao Wang, Biao Wang, Biao Wang

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Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data by Rui Li MS, Xiaoyan Hao MS, Yanjun Diao MD, Liu Yang MS, Jiayun Liu MD

“…Additionally, a computational tool for predicting CRC risk based on the optimal model was developed, designed for researchers with programming experience. Conclusion Five ML models for CRC diagnosis, based on ten routine laboratory test items, were developed, achieving higher diagnostic accuracies than traditional CRC biomarkers. …”
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Application of artificial intelligence technologies for the detection of early childhood caries by Priyanka A, Rishi Sreekumar, S Namasivaya Naveen

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Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis by Raut Komal, Balpande Vijaya

“…It is necessary to detect the symptoms of Parkinson's earlier automatically, yet traditional diagnostic methods often lack accuracy. This paper proposed several machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression and Support Vector Machine and design an ensemble of these models to detect and classify Parkinson's disease. …”
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The clinical utility and safety of biomarker-guided immunosuppression withdrawal in liver transplantation: the LIFT prospective RCT by Julien Vionnet, Rosa Miquel, Juan G Abraldes, Juan-Jose Lozano, Pablo Ruiz, Miquel Navasa, Aileen Marshall, Frederik Nevens, William Gelson, Joanna Leithead, Steven Masson, Elmar Jaeckel, Richard Taubert, Phaedra Tachtatzis, Dennis Eurich, Kenneth Simpson, Eliano Bonaccorsi-Riani, James Ferguson, Alberto Quaglia, Maria Elstad, Marc Delord, Abdel Douiri, Alberto Sánchez-Fueyo

“…A previous clinical trial showed that a logistic regression algorithm including the transcript levels of a set of five genes in a liver biopsy could predict the success of immunosuppression withdrawal with high sensitivity and specificity. …”
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CLINICODIAGNOSTIC FEATURES OF OTORHINOSINUSOGENIC INTRACRANIAL PYOINFLAMMATORY DISEASES COMPLICATED BY SEPSIS by A. A. Krivopalov, Yu. K. Yanov, V. A. Shatalov, A. N. Rubin, A. Yu. Shcherbuk, S. A. Artyushkin, Z. N. Shardanov, S. V. Shervashidze

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Efficient control strategy for electric furnace temperature regulation using quadratic interpolation optimization by Serdar Ekinci, Davut Izci, Veysel Gider, Laith Abualigah, Mohit Bajaj, Ievgen Zaitsev

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Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review by Hesameddin Akbarein, Mohammad Hussein Taaghi, Mahyar Mohebbi, Parham Soufizadeh

“…This article presents a comprehensive review of the manifold applications of AI within the domain of veterinary science, categorizing them into four domains: clinical practice, biomedical research, public health, and administration. It also examines the primary machine learning algorithms used in relevant studies, highlighting emerging trends in the field. …”
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The Value of Biomarkers in the Diagnosis and Prognosis of Heart Failure in Older Age by V. N. Larina, V. I. Lunev

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“Clinical Stability” and Propensity Score Matching in Cardiac Surgery: is the clinical evaluation of treatment efficacy algorithmdependent in small sample size settings? by Daniele Bottigliengo, Aslıhan Sentürk Acar, Veronica Sciannameo, Giulia Lorenzoni, Jonida Bejko, Tomaso Bottio, Emanuele Cozzi, Marta Vadori, Jean-Paul Soulillou, Jean Christian Roussel, Thierry Le Torneau, Thomas Senage, Rafael Mañez, Cristina Costa, Vered Padler-Karavani, Sofia Scali, Massimiliano Carrozzini, Emilia Fiorello, Samuel Fusca, Gino Gerosa, Ileana Baldi, Paola Berchialla, Dario Gregori

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Pustular reaction in adult-onset immunodeficiency due to anti-interferon-gamma autoantibodies by Narachai Julanon, Pojsakorn Danpanichkul, Charoen Choonhakarn, Suteeraporn Chaowattanapanit, Siriluck Anunnatsiri, Ploenchan Chetchotisakd, Salin Kiratikanon, Rujira Rujiwetpongstorn, Napatra Tovanabutra, Siri Chiewchanvit, Romanee Chaiwarith, Phichayut Phinyo, Phichayut Phinyo, Mati Chuamanochan, Mati Chuamanochan

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Randomised controlled trial to evaluate the effectiveness of using the RD-1-based C-Tb skin test as a replacement for blood-based interferon-γ release assay for detection of, and i... by Marc Lipman, Ibrahim Abubakar, Chris Griffiths, Penny Whiting, Robert Horne, Alex Ghanouni, Heinke Kunst, Mike Mandelbaum, Molebogeng X Rangaka, Karen Sanders, Peter J White, Angela Crook, Dominik Zenner, Trinh Duong, Vanessa Hack, Julian Surey, Yohhei Hamada, Henry Bern, Joanna Calvert, Marie Francis, Amy Louise Clarke, Ellen Owen-Powell, Helen L Booth

“…Economic analysis will estimate cost-effectiveness of changes to the diagnostic algorithm for LTBI. The protocol was developed with Patient and Public Involvement (PPI), which will continue throughout the trial.Ethics and dissemination Ethics approval has been obtained from The NHS Health Research Authority (269485). …”
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Artificial intelligence and robotic surgery in clinical medicine: progress, challenges, and future directions by Salih Beyaz, Alaaddin Levent Özgözen, Necmettin Turgut, Hakkı Can Ölke

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MAP: Mutation Arranger for Defining Phenotype-Related Single-Nucleotide Variant by In-Pyo Baek, Yong-Bok Jeong, Seung-Hyun Jung, Yeun-Jun Chung

“…Next-generation sequencing (NGS) is widely used to identify the causative mutations underlying diverse human diseases, including cancers, which can be useful for discovering the diagnostic and therapeutic targets. Currently, a number of single-nucleotide variant (SNV)-calling algorithms are available; however, there is no tool for visualizing the recurrent and phenotype-specific mutations for general researchers. …”
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Integrating digital and narrative medicine in modern healthcare: a systematic review by Efthymia Efthymiou

“…This integrated approach supports technological proficiency and humanistic values, for advancements in digital health to improve rather than displace the interpersonal foundations of patient care. Further empirical research might assess long-term outcomes and guide implementation into curricula, faculty development, and institutional policy.Background: The increasing integration of digital technologies in healthcare, such as electronic health records, telemedicine, and diagnostic algorithms, has improved efficiency but raised concerns about the depersonalization of care. …”
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