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Journal articles on the topic 'AI in Clinical research'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Majid, Zomana, and Md Islam. "Clinical Research and Artificial Intelligence: How AI Is Changing Clinical Research." American Journal of Artificial Intelligence 9, no. 1 (2025): 68–79. https://doi.org/10.11648/j.ajai.20250901.17.

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Medicine is quickly transitioning as artificial intelligence (AI) adopts the new and improved type of machine learning for better diagnosis and treatment of diseases in the various sub-specialties of practice. The enhancement of computation rate raises the potential of AI algorithms and their value for multiple domains like radiology, where some experts suppose that AI can replace radiologists. These questions are essential when determining whether specific AI applications will eventually replace doctors or only assist them in their work within specific medical specialties. This paper ponders

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Dietzel, Matthias, and Pascal A. T. Baltzer. "Bridging AI research and clinical impact." European Journal of Radiology Artificial Intelligence 2 (June 2025): 100026. https://doi.org/10.1016/j.ejrai.2025.100026.

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Topol, Eric J. "Welcoming new guidelines for AI clinical research." Nature Medicine 26, no. 9 (2020): 1318–20. http://dx.doi.org/10.1038/s41591-020-1042-x.

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Park, Yoonyoung, Gretchen Purcell Jackson, Morgan A. Foreman, Daniel Gruen, Jianying Hu, and Amar K. Das. "Evaluating artificial intelligence in medicine: phases of clinical research." JAMIA Open 3, no. 3 (2020): 326–31. http://dx.doi.org/10.1093/jamiaopen/ooaa033.

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Abstract Increased scrutiny of artificial intelligence (AI) applications in healthcare highlights the need for real-world evaluations for effectiveness and unintended consequences. The complexity of healthcare, compounded by the user- and context-dependent nature of AI applications, calls for a multifaceted approach beyond traditional in silico evaluation of AI. We propose an interdisciplinary, phased research framework for evaluation of AI implementations in healthcare. We draw analogies to and highlight differences from the clinical trial phases for drugs and medical devices, and we present

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Yang, Wei-Hua, and Yan-Wu Xu. "Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023)." International Journal of Ophthalmology 16, no. 9 (2023): 1361–72. http://dx.doi.org/10.18240/ijo.2023.09.02.

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With the upsurge of artificial intelligence (AI) technology in the medical field, its application in ophthalmology has become a cutting-edge research field. Notably, machine learning techniques have shown remarkable achievements in diagnosing, intervening, and predicting ophthalmic diseases. To meet the requirements of clinical research and fit the actual progress of clinical diagnosis and treatment of ophthalmic AI, the Ophthalmic Imaging and Intelligent Medicine Branch and the Intelligent Medicine Committee of Chinese Medicine Education Association organized experts to integrate recent evalu

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Endo, Yutaka, Laura Alaimo, Giovanni Catalano, Odysseas P. Chatzipanagiotou, and Timothy M. Pawlik. "Application of artificial intelligence to hepatobiliary cancer clinical outcomes research." Artificial Intelligence Surgery 4, no. 2 (2024): 59–67. http://dx.doi.org/10.20517/ais.2024.09.

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The rapid evolution of modern technology has made artificial intelligence (AI) an important emerging tool in healthcare. AI, which is a broad field of computer science, can be used to develop systems or machines equipped with the ability to tackle tasks that traditionally necessitate human intelligence. AI can be used to perform multifaceted tasks that involve the synthesis of large amounts of data with the generation of solutions, algorithms, and decision support tools. Various AI approaches, including machine learning (ML) and natural language processing (NLP), are increasingly being used to

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Cunningham, M., and M. Dowsett. "Highlights and Future Directions in AI Clinical Research." MD Conference Express 14, no. 56 (2014): 16. http://dx.doi.org/10.1177/1559897715572118.

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Rao, Sohail. "Artificial Intelligence vs. Evidence-Based Clinical Trials in Humans: A Paradigm Shift in Clinical Research." INNOVAPATH 2, Q2 (2025): 6. https://doi.org/10.63501/xptwta38.

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Prospective interventional clinical studies in humans remain the cornerstone of evidence-based medicine, guiding clinical decision-making and regulatory approvals. However, with the rapid advancement of Artificial Intelligence (AI) and the conceptual emergence of Artificial General Intelligence (AGI), the medical community is beginning to explore whether these technologies can augment or even replace traditional clinical trials. This manuscript critically examines the capabilities of AI and AGI in simulating, predicting, and evaluating clinical interventions. We discuss the methodological, eth

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Sevier, Brian, Daniella Meeker, Christine Chaisson, Roberta Bruhn, and Eric Borchardt. "556 Transforming clinical research administration: The role of generative AI and chatbots." Journal of Clinical and Translational Science 9, s1 (2025): 162–63. https://doi.org/10.1017/cts.2024.1127.

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Objectives/Goals: To explore how generative AI and chatbot technologies can transform clinical research administration by improving operational efficiency, reducing administrative burden, and thereby enhancing overall productivity and accuracy in clinical research environments. Methods/Study Population: This explores AI’s application in enhancing clinical research administration. We specifically address AI’s role in QCT/MCA activities, charge master data cleaning, and generating IRB consent forms from award documents. AI algorithms optimize charge master data for accuracy and compliance. Gener

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Kimura-Ono, Aya, and Takuo Kuboki. "AI research in dentistry and related clinical-study design." Okayama Igakkai Zasshi (Journal of Okayama Medical Association) 133, no. 3 (2021): 184–88. http://dx.doi.org/10.4044/joma.133.184.

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Hamamoto, Ryuji. "3. Medical AI Research and Development for Clinical Application." Nihon Naika Gakkai Zasshi 112, no. 9 (2023): 1636–42. http://dx.doi.org/10.2169/naika.112.1636.

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Researcher. "AI-DRIVEN DATA INTEGRATIONS AND AUTOMATIONS FOR CLINICAL RESEARCH OPERATIONS (CRO)." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 2140–49. https://doi.org/10.5281/zenodo.14348930.

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The complexity of modern clinical research operations (CRO) necessitates advanced solutions for managing vast datasets, streamlining workflows, and ensuring compliance with stringent regulatory requirements. AI-driven data integration and automation technologies have emerged as key enablers of efficiency and accuracy in CRO, facilitating real-time data synchronization, workflow automation, and predictive analytics. This comprehensive article examines the current challenges in clinical research operations, explores AI-driven solutions for data integration, evaluates automation technologies

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Urman, Alexandra, Ching-Kun Wang, Irene Dankwa-Mullan, Ethan Scheinberg, and Michael J. Young. "Harnessing AI for health equity in oncology research and practice." Journal of Clinical Oncology 36, no. 30_suppl (2018): 67. http://dx.doi.org/10.1200/jco.2018.36.30_suppl.67.

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67 Background: Recent advances in artificial intelligence (AI) carry underexplored practical and ethical implications for the practice of clinical oncology. As oncologic applications of AI proliferate, a framework for guiding their ethical implementations and equitable distribution will be crucial. Methods: We reviewed the current landscape of AI applications in oncology research and clinical practice by reviewing the current body of evidence in PubMed and Medline. Key ethical challenges and opportunities to address health equity are critically evaluated and highlighted. Ethical implications f

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Salybekov, Amankeldi A., Markus Wolfien, Waldemar Hahn, Sumi Hidaka, and Shuzo Kobayashi. "Artificial Intelligence Reporting Guidelines’ Adherence in Nephrology for Improved Research and Clinical Outcomes." Biomedicines 12, no. 3 (2024): 606. http://dx.doi.org/10.3390/biomedicines12030606.

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The use of artificial intelligence (AI) in healthcare is transforming a number of medical fields, including nephrology. The integration of various AI techniques in nephrology facilitates the prediction of the early detection, diagnosis, prognosis, and treatment of kidney disease. Nevertheless, recent reports have demonstrated that the majority of published clinical AI studies lack uniform AI reporting standards, which poses significant challenges in interpreting, replicating, and translating the studies into routine clinical use. In response to these issues, worldwide initiatives have created

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Schuurmans, Megan, Natália Alves, Pierpaolo Vendittelli, Henkjan Huisman, and John Hermans. "Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging." Cancers 14, no. 14 (2022): 3498. http://dx.doi.org/10.3390/cancers14143498.

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Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment resp

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Soltani, Madjid, Farshad Moradi Kashkooli, Mohammad Souri, et al. "Enhancing Clinical Translation of Cancer Using Nanoinformatics." Cancers 13, no. 10 (2021): 2481. http://dx.doi.org/10.3390/cancers13102481.

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Application of drugs in high doses has been required due to the limitations of no specificity, short circulation half-lives, as well as low bioavailability and solubility. Higher toxicity is the result of high dosage administration of drug molecules that increase the side effects of the drugs. Recently, nanomedicine, that is the utilization of nanotechnology in healthcare with clinical applications, has made many advancements in the areas of cancer diagnosis and therapy. To overcome the challenge of patient-specificity as well as time- and dose-dependency of drug administration, artificial int

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Bhattamisra, Subrat Kumar, Priyanka Banerjee, Pratibha Gupta, Jayashree Mayuren, Susmita Patra, and Mayuren Candasamy. "Artificial Intelligence in Pharmaceutical and Healthcare Research." Big Data and Cognitive Computing 7, no. 1 (2023): 10. http://dx.doi.org/10.3390/bdcc7010010.

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Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘di

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Lee, Ellen, Helmet Karim, Ipsit Vahia, and Andrea Iaboni. "100 - Artificial Intelligence in Geriatric Mental Health: Recent Advances in Clinical Research." International Psychogeriatrics 33, S1 (2021): 1. http://dx.doi.org/10.1017/s1041610221001307.

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SynopsisWith the rise of wearable sensors, advancement in comprehensible artificial intelligence (AI) algorithms, and growing acceptance of AI in medicine, AI has great potential to more reliably diagnose, prognose, and treat mental illnesses. The rapidly rising number of older adults worldwide presents a unique challenge for clinicians due to increased mental health needs in the setting of a dwindling clinical workforce. AI has enabled researchers to better understand mental illnesses by taking advantage of ‘big data.’This symposium will present an overview of novel research leveraging AI (ma

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Raja, Rohit Singh. "The Impact of Artificial Intelligence on Clinical Research and Healthcare: A Systematic Review." International Scientific Journal of Engineering and Management 03, no. 09 (2024): 1–7. https://doi.org/10.55041/isjem01962.

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Artificial intelligence (AI) is revolutionizing healthcare by enhancing clinical research and patient care. AI employs techniques such as machine learning and deep learning to process extensive datasets efficiently. This enables early diagnoses, personalized treatments, and efficient resource management. AI and its applications extend to drug development, diagnostic imaging, operational efficiency, and public health surveillance. The COVID-19 pandemic further showcased AI’s potential in vaccine development and outbreak monitoring, emphasizing its integral role in reshaping healthcare systems g

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Lehman, C., B. Dontchos, and LR Lamb. "Abstract ES7-3:/AI." Cancer Research 82, no. 4_Supplement (2022): ES7–3—ES7–3. http://dx.doi.org/10.1158/1538-7445.sabcs21-es7-3.

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Abstract The tools of AI offer promise to uncover and amplify data, previously hidden in images of the human body, and expand the field of radiomics to more accurately predict breast cancer risk across diverse patient populations. Although deep learning products trained to predict mammographic breast density and risk of breast cancer are now available, their performance in routine clinical settings is largely unknown. Since the creation of the Gail model in 1989, risk models have supported risk-adjusted screening and prevention, and their continued evolution has been a central pillar of breast

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Pandey, Ashish. "“The Impact of Artificial Intelligence in Dentistry: Transforming Clinical and Laboratory Research”." Journal of Clinical and Laboratory Research 7, no. 8 (2024): 01–16. https://doi.org/10.31579/2768-0487/153.

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Artificial Intelligence (AI) is revolutionizing healthcare, with dentistry as no exception. The integration of AI technologies into both clinical and laboratory aspects of dental research has the potential to drastically improve diagnostics, treatment planning, and patient outcomes. This article provides an in-depth exploration of AI's applications in dentistry, focusing on the latest advancements that are shaping clinical and laboratory research. From AI-driven diagnostic tools, such as machine learning algorithms for detecting oral pathologies, to virtual assistants in prosthodontic and orth

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G, Nihalani. "Applications of Artificial Intelligence in Pharmaceutical Research: An Extensive Review." Bioequivalence & Bioavailability International Journal 8, no. 1 (2024): 1–7. http://dx.doi.org/10.23880/beba-16000231.

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Artificial Intelligence (AI) is transforming the pharmaceutical industry, revolutionizing drug discovery, development, and patient care. This comprehensive review explores the diverse applications of AI in the pharmaceutical sector, discussing its advantages, challenges, and future prospects. AI-driven techniques, including machine learning and deep learning, have accelerated drug discovery processes by expediting target identification, virtual screening, and drug repurposing. Predictive analytics optimize clinical trial design, while personalized medicine leverages patient-specific data for p

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Fokunang, Charles Ntungwen, Marceline Djuidje Ngounoue, Joseph Fokam, et al. "Artificial Intelligence in Medical Research: Ethical and Regulatory Challenges in Developing Economies." Journal of Advances in Medical and Pharmaceutical Sciences 27, no. 6 (2025): 63–89. https://doi.org/10.9734/jamps/2025/v27i6788.

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Introduction: Clinical research is a key area in which the use of AI in healthcare data seen a significant increase, even though met with great ethical, legal and regulatory challenges. Artificial Intelligence (AI) concerns the ability of algorithms encoded in technology to learn from data, to be able to perform automated tasks without every step in the process being explicitly to be programmed by a human. AI development relies on big data collected from clinical trials to train algorithms, that requires careful consideration of consent, data origin and ethical standards. When data is acquired

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Göktepe-Kavis, Pinar, Florence M. Aellen, Sigurd L. Alnes, and Athina Tzovara. "Sleep Research in the Era of AI." Clinical and Translational Neuroscience 8, no. 1 (2024): 13. http://dx.doi.org/10.3390/ctn8010013.

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The field of sleep research is both broad and rapidly evolving. It spans from the diagnosis of sleep-related disorders to investigations of how sleep supports memory consolidation. The study of sleep includes a variety of approaches, starting with the sole focus on the visual interpretation of polysomnography characteristics and extending to the emergent use of advanced signal processing tools. Insights gained using artificial intelligence (AI) are rapidly reshaping the understanding of sleep-related disorders, enabling new approaches to basic neuroscientific studies. In this opinion article,

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Huespe, Ivan A., Jorge Echeverri, Aisha Khalid, et al. "Clinical Research With Large Language Models Generated Writing—Clinical Research with AI-assisted Writing (CRAW) Study." Critical Care Explorations 5, no. 10 (2023): e0975. http://dx.doi.org/10.1097/cce.0000000000000975.

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IMPORTANCE: The scientific community debates Generative Pre-trained Transformer (GPT)-3.5’s article quality, authorship merit, originality, and ethical use in scientific writing. OBJECTIVES: Assess GPT-3.5’s ability to craft the background section of critical care clinical research questions compared to medical researchers with H-indices of 22 and 13. DESIGN: Observational cross-sectional study. SETTING: Researchers from 20 countries from six continents evaluated the backgrounds. PARTICIPANTS: Researchers with a Scopus index greater than 1 were included. MAIN OUTCOMES AND MEASURES: In this stu

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Shamszare, Hamid, and Avishek Choudhury. "Clinicians’ Perceptions of Artificial Intelligence: Focus on Workload, Risk, Trust, Clinical Decision Making, and Clinical Integration." Healthcare 11, no. 16 (2023): 2308. http://dx.doi.org/10.3390/healthcare11162308.

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Artificial intelligence (AI) offers the potential to revolutionize healthcare, from improving diagnoses to patient safety. However, many healthcare practitioners are hesitant to adopt AI technologies fully. To understand why, this research explored clinicians’ views on AI, especially their level of trust, their concerns about potential risks, and how they believe AI might affect their day-to-day workload. We surveyed 265 healthcare professionals from various specialties in the U.S. The survey aimed to understand their perceptions and any concerns they might have about AI in their clinical prac

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Li, Ping, Manyi Zhuang, Jun Tang, and Jian Huang. "Research on Automatic Recognition and Auxiliary Diagnosis of Artificial Intelligence in Skin Diseases." Frontiers in Computing and Intelligent Systems 11, no. 3 (2025): 112–26. https://doi.org/10.54097/a0askn96.

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Artificial intelligence (AI) has made significant strides in skin disease diagnosis, demonstrating impressive potential in the classification and detection of conditions such as skin cancer. However, several challenges remain that hinder the full clinical adoption of AI-driven diagnostic systems. This paper reviews the current advancements and key research areas aimed at optimizing AI models for dermatological applications. Key areas for future research include the development of more advanced deep learning architectures, such as multimodal fusion methods, which integrate dermoscopic images wi

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Yoo, Dong Whi, Hayoung Woo, Sachin R. Pendse, et al. "Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI Research." Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (2024): 1–24. http://dx.doi.org/10.1145/3637372.

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In the mental health domain, patient engagement is key to designing human-centered technologies. CSCW and HCI researchers have delved into various facets of collaboration in AI research; however, previous research neglects the individuals who both produce the data and will be most impacted by the resulting technologies, such as patients. This study examines how interdisciplinary researchers and mental health patients who donate their data for AI research collaborate and how we can improve human-centeredness in mental health AI research. We interviewed patient participants, AI researchers, and

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Corsello, Antonio, and Andrea Santangelo. "May Artificial Intelligence Influence Future Pediatric Research?—The Case of ChatGPT." Children 10, no. 4 (2023): 757. http://dx.doi.org/10.3390/children10040757.

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Background: In recent months, there has been growing interest in the potential of artificial intelligence (AI) to revolutionize various aspects of medicine, including research, education, and clinical practice. ChatGPT represents a leading AI language model, with possible unpredictable effects on the quality of future medical research, including clinical decision-making, medical education, drug development, and better research outcomes. Aim and Methods: In this interview with ChatGPT, we explore the potential impact of AI on future pediatric research. Our discussion covers a range of topics, i

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Aditya, Gadiko. "The Synergy of AI and Human Expertise in Clinical Research: A Path to Optimized Medical Reviews." Journal of Scientific and Engineering Research 10, no. 12 (2023): 173–79. https://doi.org/10.5281/zenodo.11221029.

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The integration of Artificial Intelligence (AI) with human expertise in clinical research promises a revolutionary shift in medical reviews, enhancing efficiency, data integrity, and patient safety. This paper explores the synergy between AI and human expertise, focusing on optimizing medical re- view processes in clinical trials. We highlight the challenges of increasing data volume and complexity, increasing complexity of data issues, pattern recognition, and data analysis, including multimodal data integration. Key to our discourse is the col- laborative model between AI systems and medical

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Julian Y.V, Borges. "Artificial Intelligence in Pain Management: Advancing Translational Science in Digital Health Research from Bench to Bedside." Advances in Machine Learning & Artificial Intelligence 5, no. 3 (2024): 01–06. http://dx.doi.org/10.33140/amlai.05.03.04.

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Artificial Intelligence (AI) is rapidly transforming the landscape of healthcare, with particularly profound implications in the field of pain management. This chapter delves into the integration of AI-driven tools that revolutionize the way pain is assessed, monitored, and treated. Through the use of predictive modeling, real-time monitoring, and personalized treatment plans, AI significantly enhances the precision, efficiency, and effectiveness of pain management strategies. The discussion extends to various AI applications, shedding light on the ethical considerations that accompany these t

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Balti, Ali Asgar, Mohammad Hassanain, Mohd Ajaz, and Williyat Ali. "Impact of generative AI on biochemical research and clinical practices." International Journal of Clinical Biochemistry and Research 12, no. 1 (2025): 68–70. https://doi.org/10.18231/j.ijcbr.2025.010.

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Burstein, Harold J., Ann Alexis Prestrud, Jerome Seidenfeld, et al. "American Society of Clinical Oncology Clinical Practice Guideline: Update on Adjuvant Endocrine Therapy for Women With Hormone Receptor–Positive Breast Cancer." Journal of Clinical Oncology 28, no. 23 (2010): 3784–96. http://dx.doi.org/10.1200/jco.2009.26.3756.

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PurposeTo develop evidence-based guidelines, based on a systematic review, for endocrine therapy for postmenopausal women with hormone receptor–positive breast cancer.MethodsA literature search identified relevant randomized trials. Databases searched included MEDLINE, PREMEDLINE, the Cochrane Collaboration Library, and those for the Annual Meetings of the American Society of Clinical Oncology (ASCO) and the San Antonio Breast Cancer Symposium (SABCS). The primary outcomes of interest were disease-free survival, overall survival, and time to contralateral breast cancer. Secondary outcomes incl

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Srivastava, Ankita, Marco Marabelli, Danielle Blanch-Hartigan, et al. "The Present and Future of AI: Ethical Issues and Research Opportunities." Communications of the Association for Information Systems 56 (2025): 255–73. https://doi.org/10.17705/1cais.05611.

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Healthcare is currently a fast-changing industry with AI and generative AI (GenAI) playing a prominent role in the transformation of clinical as well as managerial practices. Clinical practices involve AI to diagnose diseases and develop new drugs and compounds, while managerial practices concern AI-supporting processes such as billing patients and insurance companies, handling electronic medical records, and supporting remote connections with patients, increasingly using virtual and augmented reality. Yet, all these opportunities offered by AI come with challenges involving potential ethical

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Peek, Niels, Daniel Capurro, Vlada Rozova, and Sabine N. van der Veer. "Bridging the Gap: Challenges and Strategies for the Implementation of Artificial Intelligence-based Clinical Decision Support Systems in Clinical Practice." Yearbook of Medical Informatics 33, no. 01 (2024): 103–14. https://doi.org/10.1055/s-0044-1800729.

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Summary Objectives: Despite the surge in development of artificial intelligence (AI) algorithms to support clinical decision-making, few of these algorithms are used in practice. We reviewed recent literature on clinical deployment of AI-based clinical decision support systems (AI-CDSS), and assessed the maturity of AI-CDSS implementation research. We also aimed to compare and contrast implementation of rule-based CDSS with implementation of AI-CDSS, and to give recommendations for future research in this area. Methods: We searched PubMed and Scopus for publications in 2022 and 2023 that focus

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Athanasopoulou, Konstantina, Glykeria N. Daneva, Panagiotis G. Adamopoulos, and Andreas Scorilas. "Artificial Intelligence: The Milestone in Modern Biomedical Research." BioMedInformatics 2, no. 4 (2022): 727–44. http://dx.doi.org/10.3390/biomedinformatics2040049.

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In recent years, the advent of new experimental methodologies for studying the high complexity of the human genome and proteome has led to the generation of an increasing amount of digital information, hence bioinformatics, which harnesses computer science, biology, and chemistry, playing a mandatory role for the analysis of the produced datasets. The emerging technology of Artificial Intelligence (AI), including Machine Learning (ML) and Artificial Neural Networks (ANNs), is nowadays at the core of biomedical research and has already paved the way for significant breakthroughs in both biologi

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Friedman, Emma, Matthew John Baumann, Shruti Sehgal, et al. "Pragmatic Research and Clinical Duties: Solutions Through Precision AI-Enabled Clinically Embedded Research." American Journal of Bioethics 23, no. 8 (2023): 50–52. http://dx.doi.org/10.1080/15265161.2023.2217126.

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Sak, Jarosław, and Magdalena Suchodolska. "Artificial Intelligence in Nutrients Science Research: A Review." Nutrients 13, no. 2 (2021): 322. http://dx.doi.org/10.3390/nu13020322.

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Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was cond

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Kalbhor, Mr Chaitanya Sunil. "Aritifical Intelligence in Clinical Trials." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2470–77. https://doi.org/10.22214/ijraset.2025.68764.

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Abstract: By improving the effectiveness, precision, and flexibility of clinical research procedures, artificial intelligence (AI) is quickly changing the clinical trial environment. AI provides creative answers to many of the conventional problems encountered in clinical trials, ranging from intelligent patient recruiting and protocol design to real-time data analysis and safety monitoring. Improved patient matching, quicker decision-making, and better trial results have all been made possible by recent developments in machine learning, natural language processing, and predictive analytics. P

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Rugo, Hope, Xianchen Liu, Benjamin Li, et al. "Abstract P3-01-15: Real-world effectiveness of palbociclib plus aromatase inhibitors (AI) in African American (AA) patients with metastatic breast cancer (MBC)." Cancer Research 83, no. 5_Supplement (2023): P3–01–15—P3–01–15. http://dx.doi.org/10.1158/1538-7445.sabcs22-p3-01-15.

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Abstract Background: Randomized clinical trials and a growing body of real-world evidence have demonstrated clinical benefit of cyclin dependent kinase 4/6 inhibitors (CDK4/6i) in combination with endocrine therapy for HR+/HER2- (MBC). In the National Comprehensive Cancer Network (NCCN) guidelines, CDK4/6i + AI or Fulvestrant is recognized as a preferred regimen for HR+/HER2- MBC. Disparities in survival and clinical outcomes between AA and white breast cancer patients are well documented, but AA patients were not well represented in CDK4/6i randomized clinical trials. We compared real-world p

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D, Nandhini. "AI DERMATOLOGY." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46443.

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Abstract - This research investigates the possible change which can be brought about by the application of Artificial Intelligence (AI) in Dermatology with particular emphasis on skin disease prediction. In the face of the rising problem of skin diseases and their early identification and correct diagnosis, the study discusses the rising role of AI-based applications that use deep learning and a vast amount of skin disease images for their identification and prediction. Conventional dermatology depends a lot on the clinical examination and the doctor’s diagnosis which is often subjective and i

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Sáez, Carlos, Pablo Ferri, and Juan M. García-Gómez. "Resilient Artificial Intelligence in Health: Synthesis and Research Agenda Toward Next-Generation Trustworthy Clinical Decision Support." Journal of Medical Internet Research 26 (June 28, 2024): e50295. http://dx.doi.org/10.2196/50295.

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Artificial intelligence (AI)–based clinical decision support systems are gaining momentum by relying on a greater volume and variety of secondary use data. However, the uncertainty, variability, and biases in real-world data environments still pose significant challenges to the development of health AI, its routine clinical use, and its regulatory frameworks. Health AI should be resilient against real-world environments throughout its lifecycle, including the training and prediction phases and maintenance during production, and health AI regulations should evolve accordingly. Data quality issu

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Sequí-Sabater, José Miguel, and Diego Benavent. "Artificial intelligence in rheumatology research: what is it good for?" RMD Open 11, no. 1 (2025): e004309. https://doi.org/10.1136/rmdopen-2024-004309.

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Artificial intelligence (AI) is transforming rheumatology research, with a myriad of studies aiming to improve diagnosis, prognosis and treatment prediction, while also showing potential capability to optimise the research workflow, improve drug discovery and clinical trials. Machine learning, a key element of discriminative AI, has demonstrated the ability of accurately classifying rheumatic diseases and predicting therapeutic outcomes by using diverse data types, including structured databases, imaging and text. In parallel, generative AI, driven by large language models, is becoming a power

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Regina, Odette Agnes. "Artificial Intelligence in Diabetes Care: Transforming Diagnosis, Management, and Research- A Mini Review." IDOSR JOURNAL OF COMPUTER AND APPLIED SCIENCES 9, no. 1 (2024): 11–14. http://dx.doi.org/10.59298/jcas/2024/91.1114000.

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Artificial intelligence (AI) is revolutionizing diabetes care by transforming the landscape of diagnosis, management, and research. This review explores the diverse applications of AI in diabetes, including predictive modeling, personalized treatment strategies, clinical decision support systems, and drug discovery. The integration of AI with advanced data analytics, machine learning algorithms, and big data has enabled more accurate risk prediction, early disease detection, and optimized therapeutic interventions. Challenges such as data privacy, algorithm transparency, and clinical validatio

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Gnanasambandam, Anjali. "Artificial Intelligence in Clinical Trials- Future Prospectives." Bioequivalence & Bioavailability International Journal 7, no. 1 (2023): 1–8. http://dx.doi.org/10.23880/beba-16000196.

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Clinical trials are essential for delivering novel medications, technology, and procedures to the market and clinical practice. Only 10% of these studies complete the entire procedure from the drug design to the four phases of development, because clinical trials are becoming more expensive and difficult to perform. The population's health, standard treatment, health economics, and sustainability suffered greatly from this low completion rate. Artificial intelligence (AI) is one of the tools that could streamline some of the processes which are the most tedious operations, like patient selecti

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Thai, Kelly, Kate H. Tsiandoulas, Elizabeth A. Stephenson, et al. "Perspectives of Youths on the Ethical Use of Artificial Intelligence in Health Care Research and Clinical Care." JAMA Network Open 6, no. 5 (2023): e2310659. http://dx.doi.org/10.1001/jamanetworkopen.2023.10659.

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ImportanceUnderstanding the views and values of patients is of substantial importance to developing the ethical parameters of artificial intelligence (AI) use in medicine. Thus far, there is limited study on the views of children and youths. Their perspectives contribute meaningfully to the integration of AI in medicine.ObjectiveTo explore the moral attitudes and views of children and youths regarding research and clinical care involving health AI at the point of care.Design, Setting, and ParticipantsThis qualitative study recruited participants younger than 18 years during a 1-year period (Oc

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Azmi, Sarfuddin, Faisal Kunnathodi, Haifa F. Alotaibi, et al. "Harnessing Artificial Intelligence in Obesity Research and Management: A Comprehensive Review." Diagnostics 15, no. 3 (2025): 396. https://doi.org/10.3390/diagnostics15030396.

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Purpose: This review aims to explore the clinical and research applications of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in understanding, predicting, and managing obesity. It assesses the use of AI tools to identify obesity-related risk factors, predict outcomes, personalize treatments, and improve healthcare interventions for obesity. Methods: A comprehensive literature search was conducted using PubMed and Google Scholar, with keywords including “artificial intelligence”, “machine learning”, “deep learning”, “obesity”, “obesity management”, and

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Bikash Medhi, Dr. Himanshu Sharma, Dr. Tamanna Kaundal, and Dr. Ajay Prakash. "Artificial Intelligence: A Catalyst for Breakthroughs in Nanotechnology and Pharmaceutical Research." International Journal of Pharmaceutical Sciences and Nanotechnology(IJPSN) 17, no. 4 (2024): 7439–45. http://dx.doi.org/10.37285/ijpsn.2024.17.4.1.

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Artificial intelligence (AI) is revolutionizing nanotechnology and pharmaceutical research by streamlining drug discovery, optimizing formulations, and personalizing treatments through predictive modelling and data analysis. Without AI, the pharmaceutical industry requires more time due to less effective drug discovery, inefficient clinical trials, and prolonged regulatory processes, resulting in higher costs and delayed treatments1. The integration of AI with nanotechnology and pharmaceutical science is revolutionizing medicine, opening up new possibilities for diagnosis, treatment, and perso

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Wu, Chieh-Chen, Md Mohaimenul Islam, Tahmina Nasrin Poly, and Yung-Ching Weng. "Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research." Diagnostics 14, no. 4 (2024): 397. http://dx.doi.org/10.3390/diagnostics14040397.

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Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric analysis is essential. The purposes of this study are to systematically analyze and quantify the scientific output, research trends, and collaborative networks in the application of AI to kidney disease. This study collected AI-related articles published between 2012 and 20 November 2023 from the Web of Science. Des

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Diaconu, A., F. D. Cojocaru, I. Gardikiotis, et al. "Expending the power of artificial intelligence in preclinical research: an overview." IOP Conference Series: Materials Science and Engineering 1254, no. 1 (2022): 012036. http://dx.doi.org/10.1088/1757-899x/1254/1/012036.

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Abstract Artificial intelligence (AI) is described as the joint set of data entry, able to receive inputs, interpret and learn from such feedbacks, and display related and flexible independent actions that help the entity reach a specific aim over a period of time. By extending its health-care applications continuously, the ultimate AI target is to use machine simulation of human intelligence processes such as learning, reasoning, and self-correction, to mimic human behaviour. AI is extensively used in diverse sectors of medicine, including clinical trials, drug discovery and development, unde

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