AI-enabled individual entrepreneurship theory: redefining scale, capability, and sustainability in the digital age by Venkat Ram Reddy Ganuthula

“…Addressing a gap in entrepreneurship frameworks, AIET integrates knowledge- and resource-based perspectives, proposing that AI enhances individual capabilities through interconnected mechanisms: skill augmentation, capital access, and risk reduction. …”
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A Systematic Review of Gen-AI Applications in Education: Rewards, Challenges and Future Prospects by Everleen Nekesa Wanyonyi, Millicent Kathambi Murithi

“…The ability of GenAI algorithms to generate specific feedback on tests, quizzes, and assessments as an instructional instrument to support student success has been a significant motivator. …”
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Randomized comparison of AI enhanced 3D printing and traditional simulations in hepatobiliary surgery by Linqian Li, Shujie Cheng, Jinghua Li, Jihong Yang, Hongguang Wang, Bin Dong, Xiaoping Yin, Hongyun Shi, Shuo Gao, Feng Gu, Zhe Han, Zhi Chen, Jisen Zhao, Quan Zhang, Jie-Zhi Cheng, Yuan Wang, Fengwei Tan, Ke Zhang

“…Results demonstrated that 3DP models were produced rapidly (3.52 h at $152 each) with high precision, AI-assisted reconstruction reduced processing time (303.5 vs. 557 min), and patients using AI-enhanced physical 3DP models experienced less intraoperative blood loss. …”
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AI-Based Linguistic Frameworks for Monitoring Physical Health, Sports Performance, and Educational Contexts by Rakhimov Vladimir, Saidnazarov Ulugbek, Mustafaev Ispandiyar

“…Rather, their perceptual feedback loops and adaptive interpretations are integrated into the iterative refinement of monitoring algorithms. …”
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Large Language Models in Medical Chatbots: Opportunities, Challenges, and the Need to Address AI Risks by James C. L. Chow, Kay Li

“…These include hallucinations (the generation of factually incorrect or misleading content by an AI model), algorithmic biases, privacy risks, and a lack of regulatory clarity. …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…This study proposes a hybrid AI-driven framework that integrates structured (e.g., patient demographics, lab results) and unstructured data (e.g., clinical notes) to detect ADRs using advanced deep learning and NLP methods. …”
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Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria by Adamu Muhammad Ibrahim, Mohamed Mustaf Ahmed, Shuaibu Saidu Musa, Usman Abubakar Haruna, Mohammed Raihanatu Hamid, Olalekan John Okesanya, Aishat Muhammad Saleh, Don Eliso Lucero-Prisno III

“…By integrating AI into Nigeria’s public health infrastructure, early detection and response can be improved, resource allocation optimized, and disease transmission minimized. …”
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Precision in practice: exploring the impact of ai and machine learning on ultrasound guided regional anaesthesia by Noor Ul Huda Bhatti, Syed Ghazi Ali Kirmani, Maryam Butt

“…Considering this, anaesthesiologists nationwide must integrate effective AI models to improve their clinical practice. …”
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Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion by Rocío Aznar-Gimeno, Jose Luis Perez-Lasierra, Pablo Pérez-Lázaro, Irene Bosque-López, Marina Azpíroz-Puente, Pilar Salvo-Ibáñez, Martin Morita-Hernandez, Ana Caren Hernández-Ruiz, Antonio Gómez-Bernal, María de la Vega Rodrigalvarez-Chamarro, José-Víctor Alfaro-Santafé, Rafael del Hoyo-Alonso, Javier Alfaro-Santafé

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Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies by Md. Kabin Hasan Kanchon, Mahir Sadman, Kaniz Fatema Nabila, Ramisa Tarannum, Riasat Khan

“…To the best of our knowledge, this is the first time an integrated learning style detection and content modification system has been developed in this work utilizing efficient AI techniques and a private dataset.…”
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AI for rapid identification of major butyrate-producing bacteria in rhesus macaques (Macaca mulatta) by Annemiek Maaskant, Donghyeok Lee, Huy Ngo, Roy C. Montijn, Jaco Bakker, Jan A. M. Langermans, Evgeni Levin

“…In addition, to improve transparency, we employed explainability analysis to uncover the image features influencing the model’s predictions. Results By integrating fecal image data with corresponding metagenomic sequencing information, the deep learning (DL) and machine learning (ML) algorithms successfully predicted 16 individual bacterial genera (area under the curve (AUC) > 0.7) among the 50 most abundant genera in rhesus macaques (Macaca mulatta). …”
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AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review by Nusrat Jahan Sarna, Farzana Ahmed Rithen, Umme Salma Jui, Sayma Belal, Al Amin, Tasnim Kabir Oishee, A. K. M. Muzahidul Islam

“…Future research should focus on improving the transparency of the AI model, integrating AI with blockchain for secure data sharing, and leveraging federated learning to enhance fraud detection capabilities. …”
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Multimodal AI for Home Wound Patient Referral Decisions From Images With Specialist Annotations by Reza Saadati Fard, Emmanuel Agu, Palawat Busaranuvong, Deepak Kumar, Shefalika Gautam, Bengisu Tulu, Diane Strong

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Deep learning-based detection and classification of acute lymphoblastic leukemia with explainable AI techniques by Debendra Muduli, Sourav Parija, Suhani Kumari, Asmaul Hassan, Harendra S. Jangwan, Abu Taha Zamani, Sk. Mohammed Gouse, Banshidhar Majhi, Nikhat Parveen

“…To improve the interpretability of the leukemia detection process, explainable AI techniques, including Grad-CAM, Score-CAM, and Grad-CAM++, were integrated to vi-sualize critical regions influencing model predictions. …”
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Ethical framework for responsible foundational models in medical imaging by Debesh Jha, Gorkem Durak, Abhijit Das, Jasmer Sanjotra, Onkar Susladkar, Suramyaa Sarkar, Ashish Rauniyar, Nikhil Kumar Tomar, Linkai Peng, Sirui Li, Koushik Biswas, Ertugrul Aktas, Elif Keles, Matthew Antalek, Zheyuan Zhang, Bin Wang, Xin Zhu, Xin Zhu, Hongyi Pan, Deniz Seyithanoglu, Alpay Medetalibeyoglu, Vanshali Sharma, Vedat Cicek, Amir A. Rahsepar, Rutger Hendrix, Rutger Hendrix, A. Enis Cetin, Bulent Aydogan, Mohamed Abazeed, Frank H. Miller, Rajesh N. Keswani, Rajesh N. Keswani, Hatice Savas, Sachin Jambawalikar, Daniela P. Ladner, Amir A. Borhani, Concetto Spampinato, Concetto Spampinato, Michael B. Wallace, Michael B. Wallace, Ulas Bagci

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Continuous audit model: data integration framework by Mauricio Mello Codesso, Paulo Caetano da Silva, Miklos A. Vasarhelyi, Rogério João Lunkes

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Artificial Intelligence in medicine: Potential and Application Possibilities - Comprehensive literature review by Michał Kotowicz, Magdalena Bieniak-Pentchev, Maria Koczkodaj

“…Emergency medicine sees promise in AI’s integration for rapid triage and decision support, though legal and interpretability challenges persist. …”
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AI-powered Somatic Cancer Cell Analysis for Early Detection of Metastasis: The 62 principal Cancer Types by Sandile Buthelezi, Solly Matshonisa Seeletse, Taurai Hungwe, Vimbai Mbirimi-Hungwe

“…The AI algorithms explored the possibility of detecting tumors with a high spread risk. …”
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Microbiology in the era of artificial intelligence: transforming medical and pharmaceutical microbiology by Virna-Maria Tsitou, Dimitrios Rallis, Mariana Tsekova, Nikolay Yanev

“…We critically examine the challenges in AI application, such as ensuring data quality and overcoming algorithmic constraints, and stress the necessity for interpretable AI models that align with medical and ethical standards. …”
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The impact of artificial intelligence on human resource management practices: An investigation by Thandukwazi R. Ncube, Kusangiphila K. Sishi, Jane P. Skinner

“…Research purpose: This study aims to systematically review and analyse the literature on AI’s current integration into HRM practices across industries, focusing on studies published from 2020 to 2024. …”
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