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Journal articles on the topic 'AI Diagnostics'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Haferlach, Torsten. "AI in Diagnostics." Clinical Lymphoma Myeloma and Leukemia 24 (September 2024): S25—S26. http://dx.doi.org/10.1016/s2152-2650(24)00333-1.

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Hull, Mary Louise. "Can AI Improve Imaging Diagnostics?" Fertility & Reproduction 05, no. 04 (2023): 211. http://dx.doi.org/10.1142/s2661318223740250.

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The 2016 Cochrane Review identified Magnetic Resonance Imaging (MRI) and Transvaginal Ultrasound scans (TVUS) as the most diagnostic non-invasive test for endometriosis. This led to IMAGENDO, which uses Artificial Intelligence (AI) to model digital data from these two imaging modalities to improve the accuracy of endometriosis non-invasive diagnostics. The IMAGENDO team have developed a novel, award winning, multimodal approach to improve the sensitivity and specificity of endometriosis imaging. The talk will describe some of the methods used, the impact AI has had on diagnostic accuracy and t

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Czerwinska, Magdalena, Joanna Ejdys, and Agnieszka Rzepka. "Understanding patient attitudes towards the use of AI in medical diagnosis using Necessary Conditions Analysis." European Conference on Innovation and Entrepreneurship 19, no. 1 (2024): 984–86. http://dx.doi.org/10.34190/ecie.19.1.2864.

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The recent years have brought a rapid growth in the use of artificial intelligence (AI), particularly in medical applications. One of the areas where AI can find application is in medical diagnostics. AI has the potential to revolutionize the field of medical diagnostics by improving the predictive accuracy, speed and efficiency of the diagnostic process. The literature provides many studies on the applications of AI in medical diagnostics, including the attitudes of medical personnel towards this technology. However, few studies focus on evaluating the use of AI in medicine from the perspecti

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Thayil, Jerry John. "AI-Driven Diagnostics: Revolutionizing Healthcare Precision." European Journal of Biology and Medical Science Research 12, no. 3 (2024): 93–99. https://doi.org/10.37745/ejbmsr.2013/vol12n39399.

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Integrating Artificial Intelligence (AI) into healthcare diagnostics has emerged as a transformative force, enhancing precision, efficiency, and patient outcomes. This white paper explores the current challenges faced in traditional diagnostic methodologies, presents AI-driven solutions, and highlights the benefits of these technologies across various healthcare organizations. By harnessing the power of advanced algorithms, healthcare providers can significantly improve diagnostic accuracy, reduce operational inefficiencies, and ultimately transform patient care.

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Ejdys, Joanna, Magdalena Czerwińska, and Romualdas Ginevičius. "Social acceptance of artificial intelligence (AI) application for improving medical service diagnostics." Human Technology 20, no. 1 (2024): 155–77. http://dx.doi.org/10.14254/1795-6889.2024.20-1.8.

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The aim of the conducted research was to assess the attitude of the Polish society towards the use of artificial intelligence in medical diagnostics. In the research process, we sought answers to three research questions: how trust in the use of AI for medical diagnostics can be measured; if societal openness to technology determines trust in the use of AI for medical diagnostics purposes; and if a higher level of trust in the use of AI for medical diagnostics influences the potential improvement in the quality of medical diagnostics as perceived by Poles. The authors' particular focus was on

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Vasilyev, A. O., A. V. Govorov, Yu A. Kim, P. A. Arutyunyan, and D. Yu Pushkar. "Artificial intelligence in early diagnosis of prostate cancer." Experimental and Сlinical Urology 18, no. 1 (2025): 42–49. https://doi.org/10.29188/2222-8543-2025-18-1-42-49.

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Introduction. To assess the prospects of using artificial intelligence (AI) for the diagnosis and treatment of prostate cancer (PCa) and to evaluate the impact of AI on treatment efficacy. Materials and Methods. A systematic literature review was conducted using scientific databases based on key queries related to AI and PCa. Studies from the last 10 years were included. Results. AI algorithms, such as machine learning (ML) and deep learning (DL), improve the quality of PCa diagnostics, minimize subjective factors, and optimize the diagnostic process. The implementation of computer-aided detec

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Emmanuel K., Mugisha. "AI-Powered Diagnostics: Revolutionizing Early Disease Detection." Research Output Journal of Biological and Applied Science 4, no. 3 (2024): 11–14. http://dx.doi.org/10.59298/rojbas/2024/431114.

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Artificial Intelligence (AI) is reshaping the healthcare landscape by enhancing early disease detection and improving diagnostic accuracy. By leveraging machine learning and deep learning techniques, AI can process vast amounts of medical data, identify patterns, and assist clinicians in making faster, more accurate diagnoses. This paper examines the role of AI in medical diagnostics, with a focus on early detection of chronic diseases, such as cancer and cardiovascular conditions, through case studies. It also highlights the challenges, including data privacy concerns, algorithmic bias, and t

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Adeniyi, Michael, Victoria Bukky Ayoola, Temilayo Esther Samuel, and Wuraola Awosan. "Artificial Intelligence-Driven Wearable Electronics and Smart Nanodevices for Continuous Cancer Monitoring and Enhanced Diagnostic Accuracy." International Journal of Scientific Research and Modern Technology (IJSRMT) 3, no. 11 (2024): 3–18. http://dx.doi.org/10.38124/ijsrmt.v3i11.106.

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Artificial Intelligence (AI)-driven wearable electronics and smart nanodevices are transforming cancer diagnostics by offering continuous monitoring and enhanced diagnostic accuracy. Traditional cancer diagnostic methods often suffer from delays in detection and limited real-time data, which can hinder timely treatment. In contrast, AI integration in wearable technologies and nanodevices allows for the continuous tracking of physiological biomarkers, enabling earlier detection of cancer and more precise monitoring of disease progression. This review explores the advancements in AI-powered wear

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Researcher. "HUMAN-AI COLLABORATION IN HEALTHCARE DIAGNOSTICS: ENHANCING ACCURACY AND PATIENT OUTCOMES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 814–28. https://doi.org/10.5281/zenodo.13918695.

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This comprehensive article explores the rapidly evolving landscape of human-AI collaboration in healthcare diagnostics, focusing on its applications, benefits, and challenges. The integration of AI in healthcare is transforming medical processes, particularly in diagnostics and medical imaging. With the global AI healthcare market projected to reach $187.95 billion by 2030, AI-assisted systems demonstrate remarkable accuracy in detecting various conditions, often matching or surpassing human experts. The article delves into key technologies such as Natural Language Processing, Computer Vision,

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Kailash, Alle. "AI in Healthcare: Predictive Analytics and Diagnostics." Journal of Scientific and Engineering Research 7, no. 9 (2020): 233–37. https://doi.org/10.5281/zenodo.13347491.

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Predictive analytics and decision support systems are changing patient care in artificial intelligence (AI) in healthcare. Through the identification of trends and risk variables, predictive analytics ease early illness prevention and diagnosis, improving patient outcomes and enabling cost-effective healthcare. By using unique patient data to create customized therapies that maximize benefits and reduce side effects, machine learning enables individualized treatment strategies. AI-driven algorithms improve diagnostic precision in medical imaging by delivering quick and correct evaluations. Hea

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Samar, A. Aldugeshem, K. Ainosah Turki, F. Alanazi Khalaf, A. Alqahtany Loai, M. Alenazi Abdullah, and A. Hassan Jalal. "Laboratory Specialists' Perspectives on Integrating Artificial Intelligence in Diagnostics: Challenges, Opportunities, and Future Directions." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 9, no. 4 (2021): 1–11. https://doi.org/10.5281/zenodo.13778060.

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This qualitative study explores the perspectives of laboratory specialists on the integration of artificial intelligence (AI) in diagnostics, focusing on the perceived benefits, challenges, and ethical concerns. Semi-structured interviews with 20 laboratory specialists revealed four key themes: improved diagnostic accuracy and efficiency, lack of training and trust in AI systems, shifting roles and responsibilities, and ethical concerns around data privacy and algorithmic bias. While participants were optimistic about AI’s potential to enhance diagnostic workflows, they expressed concern

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Mirbabaie, Milad, Stefan Stieglitz, and Nicholas R. J. Frick. "Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction." Health and Technology 11, no. 4 (2021): 693–731. http://dx.doi.org/10.1007/s12553-021-00555-5.

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AbstractThe diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet bee

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Nishida, Naoshi. "Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease—Applications and Challenges in Personalized Care." Bioengineering 11, no. 12 (2024): 1243. https://doi.org/10.3390/bioengineering11121243.

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Liver disease can significantly impact life expectancy, making early diagnosis and therapeutic intervention critical challenges in medical care. Imaging diagnostics play a crucial role in diagnosing and managing liver diseases. Recently, the application of artificial intelligence (AI) in medical imaging analysis has become indispensable in healthcare. AI, trained on vast datasets of medical images, has sometimes demonstrated diagnostic accuracy that surpasses that of human experts. AI-assisted imaging diagnostics are expected to contribute significantly to the standardization of diagnostic qua

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Meenakshi, Rani. "The Role of AI in Microbiological Diagnostics: Innovations and Future Prospects." International Journal of Pharmaceutical Sciences 3, no. 2 (2025): 327–28. https://doi.org/10.5281/zenodo.14810967.

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The evolution of microbiological diagnostics has significantly enhanced microbial identification and antibiotic susceptibility testing. This article explores advancements in automation and artificial intelligence (AI) within microbiology, emphasizing their influence on diagnostic precision, laboratory efficiency, and clinical outcomes. Additionally, it examines emerging AI applications such as real-time pathogen detection, predictive analytics for outbreak prevention, and AI-assisted antimicrobial stewardship. AI-based approaches are revolutionizing microbiological diagnostics, reducing turnar

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Krylov, A. P., and M. V. Sheblaev. "Artificial Intelligence in clinical laboratory diagnostics." Terapevt (General Physician), no. 11 (November 20, 2024): 39–44. https://doi.org/10.33920/med12-2411-04.

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The use of AI in medicine opens up new opportunities for improving the efficiency and quality of medical care. AI technologies can automate routine tasks, improve diagnostic accu racy, speed up the analysis of medical data, and support doctors in making clinical decisions. One of the most promising areas of AI application in healthcare is clinical laboratory diagnostics.

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Hazzazi, Ahmad Mohammad Tyhan, Hussain Ali Halawi, Fuad Ali Abdu Jabbari, et al. "The Integration of Artificial Intelligence in Histopathological Diagnostics: Review of Methodologies, Efficacy, and Future Directions in Clinical Practice." Journal of Ecohumanism 3, no. 8 (2024): 12791–98. https://doi.org/10.62754/joe.v3i8.6090.

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The integration of artificial intelligence (AI) in histopathological diagnostics represents a transformative advancement in healthcare, facilitating enhanced accuracy in disease detection and treatment planning. AI technologies, including machine learning and deep learning, have the potential to analyze complex data sets, improving diagnostic capabilities across various medical fields.This review systematically evaluates current literature on the application of AI in histopathological diagnostics, focusing on its methodologies, efficacy, and integration within clinical workflows. A comprehensi

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Ahmad, S. Rehan. "Artificial Intelligence: Use in Clinical and Genomic Diagnostics." Emerging Trends in Nutraceuticals 1, no. 2 (2022): 42–50. http://dx.doi.org/10.18782/2583-4606.111.

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The development of computer systems that are capable of carrying out tasks that typically require human intelligence is known as artificial intelligence (AI). Recent and quickly rising interest in medical AI applications is a result of AI software and technology improvements, especially deep learning algorithms and the graphics processing units (GPUs) that enable their training. While other AI subtypes have started to show similar promise in different diagnostic modalities, AI-based computer vision methods are poised to change image-based diagnostics in clinical diagnostics. Large and complica

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Naili, Yuris Tri, Iis Setiawan Mangkunegara, Purwono, and Muhammad Ahmad Baballe. "Regulatory challenges in ai-based diagnostics: Legal implications of ai use in medical diagnostics." BIO Web of Conferences 152 (2025): 01034. https://doi.org/10.1051/bioconf/202515201034.

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Artificial intelligence (AI) is being used more and more in medical diagnostics, with the potential to increase operational efficiency and diagnosis accuracy. But the use of AI also brings with it legal and regulatory ramifications, such as concerns about ethics, patient consent, and liability. The purpose of this study is to investigate how the legal system might be modified to clearly define obligations for healthcare professionals and technology innovators while defending patient rights. The approach was a thorough study of the literature that assessed the legal and regulatory implications

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Htwe, Thandar, Aung Myint, and Muntasir Muntasir. "Artificial intelligence innovations in genetic technology: DNA-based diagnostics for the future of medicine." Journal of World Future Medicine, Health and Nursing 3, no. 2 (2025): 118–27. https://doi.org/10.70177/health.v3i2.1908.

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Advancements in artificial intelligence (AI) are revolutionizing the field of genetic technology, particularly in DNA-based diagnostics, offering promising applications for the future of medicine. The rapid growth of AI in the analysis of genetic data allows for faster, more accurate, and cost-effective diagnostic processes. This study explores the integration of AI innovations in DNA diagnostics and their potential to transform clinical practices. Using a systematic review methodology, this research evaluates the current AI-driven genetic diagnostic technologies, focusing on their impact on d

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Pokhrel, Ramesh Prasad. "Cryptocurrency Price Forecasting using LSTM: A Review." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 860–63. https://doi.org/10.22214/ijraset.2025.72250.

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The proliferation of cyber threats has necessitated the continuous evolution of defense mechanisms, particularly in the domain of malware detection. Static malware analysis, a technique that involves examining code without executing it, has traditionally been a cornerstone in cybersecurity research and practice. Recent advances in Artificial Intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, have catalyzed significant improvements in diagnostic applications within static malware analysis. This paper reviews the integration of AI methodologies into static malw

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Krishnan, Vicknesh, and Gobind Naidu. "Chatbot AI: A Sustainable Pathway for Digital Transformation in Patient Engagement." International Journal of Scientific Research and Modern Technology 4, no. 2 (2025): 56–63. https://doi.org/10.5281/zenodo.14916664.

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The combination of artificial intelligence (AI) and robotics in healthcare is introducing a new period of medical advancement, as AI-powered nano-robots are being developed to significantly transform interior diagnostics and therapy administration. These technological breakthroughs have the potential to significantly improve the accuracy of medical diagnoses, simplify procedures, and enhance the quality of care provided to patients. The application of artificial intelligence (AI) and robotics in healthcare settings offers a multitude of possibilities and difficulties. AI powered robots are now

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Reshma R, Ms. "Artificial Intelligence in Medical Diagnostics and Healthcare." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–6. https://doi.org/10.55041/isjem02354.

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Artificial Intelligence (AI) is revolutionizing healthcare by transforming traditional diagnostic processes, enhancing treatment planning, and improving overall patient care. AI-driven technologies, including machine learning (ML), deep learning (DL), natural language processing (NLP), and fuzzy logic, are being increasingly integrated into clinical settings to assist healthcare professionals in making more accurate and timely decisions. AI has demonstrated its potential to surpass human expertise in medical imaging interpretation, predictive analytics, and personalized medicine by analyzing l

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Pesapane, Filippo, Emilia Giambersio, Benedetta Capetti, et al. "Patients’ Perceptions and Attitudes to the Use of Artificial Intelligence in Breast Cancer Diagnosis: A Narrative Review." Life 14, no. 4 (2024): 454. http://dx.doi.org/10.3390/life14040454.

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Breast cancer remains the most prevalent cancer among women worldwide, necessitating advancements in diagnostic methods. The integration of artificial intelligence (AI) into mammography has shown promise in enhancing diagnostic accuracy. However, understanding patient perspectives, particularly considering the psychological impact of breast cancer diagnoses, is crucial. This narrative review synthesizes literature from 2000 to 2023 to examine breast cancer patients’ attitudes towards AI in breast imaging, focusing on trust, acceptance, and demographic influences on these views. Methodologicall

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Alotaibi, Fahdah Mehsan, ‏Abdulrhman Ali Almazam, ‏Arwa Mohammad Emam, et al. "Exploring the impact and applications of artificial intelligence in advancing modern medical diagnostic practices-role of healthcare providers." International journal of health sciences 4, S1 (2020): 114–31. http://dx.doi.org/10.53730/ijhs.v4ns1.15087.

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Introduction: Since its inception in 1956, artificial intelligence (AI) has advanced significantly, especially in the past decade. AI's integration into healthcare has revolutionized medical diagnostic practices, enabling faster and more accurate analysis of medical records. By mimicking human intelligence, AI facilitates the processing of vast amounts of data, thus improving diagnosis, treatment, and patient care. Aim: This review article aims to explore the impact and applications of AI in modern medical diagnostics and evaluate its role across various healthcare providers, including physici

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Alsulimani, Ahmad, Naseem Akhter, Fatima Jameela, et al. "The Impact of Artificial Intelligence on Microbial Diagnosis." Microorganisms 12, no. 6 (2024): 1051. http://dx.doi.org/10.3390/microorganisms12061051.

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Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI’s signi

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Yılmazer, Irmak. "AI in Biomedical Imaging and Diagnostics." Next Frontier For Life Sciences and AI 8, no. 1 (2024): 97. http://dx.doi.org/10.62802/fene2356.

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Advances in artificial intelligence (AI) and synthetic biology have profoundly influenced biomedical research, creating transformative opportunities in imaging, diagnostics, and therapeutic engineering. In biomedical imaging, AI-driven algorithms enhance precision and accuracy, enabling automated analysis of complex datasets, real-time imaging insights, and identification of disease biomarkers. Meanwhile, synthetic biology redefines cellular engineering, particularly in T-cell research, by enabling customized functionalities, such as precision-targeted antigen recognition and tunable immune re

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Alshehri, Sarah, Khalid A. Alahmari, and Areej Alasiry. "A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals." Journal of Personalized Medicine 14, no. 4 (2024): 354. http://dx.doi.org/10.3390/jpm14040354.

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The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in

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Butova, Xenia, Sergey Shayakhmetov, Maxim Fedin, Igor Zolotukhin, and Sergio Gianesini. "Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management." Journal of Personalized Medicine 11, no. 12 (2021): 1280. http://dx.doi.org/10.3390/jpm11121280.

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Consultation prioritization is fundamental in optimal healthcare management and its performance can be helped by artificial intelligence (AI)-dedicated software and by digital medicine in general. The need for remote consultation has been demonstrated not only in the pandemic-induced lock-down but also in rurality conditions for which access to health centers is constantly limited. The term “AI” indicates the use of a computer to simulate human intellectual behavior with minimal human intervention. AI is based on a “machine learning” process or on an artificial neural network. AI provides accu

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Mahadev Mastud. "AI in HealthCare." International Journal of Scientific Research in Science and Technology 12, no. 1 (2025): 34–36. https://doi.org/10.32628/ijsrst25121152.

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Artificial Intelligence (AI) is revolutionizing the healthcare industry by enhancing diagnostics, streamlining workflows, and enabling more personalized patient care. By leveraging powerful algorithms and vast amounts of data, AI can identify patterns, predict outcomes, and assist healthcare professionals in making informed decisions. This introduction explores the transformative role of AI in healthcare, highlighting its applications, benefits, challenges, and potential for future advancements. From improving diagnostic accuracy to optimizing administrative processes, AI is reshaping the way

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Resna Ibrahim, Najma.S, Anupama Biju, Jeffin George, and Tintu varghese. "AI in Health Care: Revolutionizing Diagnostics and Cancer Treatment." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 12 (2024): 3793–98. https://doi.org/10.47392/irjaem.2024.0563.

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Artificial Intelligence (AI) is revolutionizing healthcare, mainly in diagnostics and most cancers treatment, via improving accuracy, performance, and personalized care. Advanced gadget learning algorithms examine medical records, such as imaging scans, pathology slides, and genetic facts, to come across illnesses at in advance stages and predict patient outcomes with extraordinary precision. AI-pushed gear in radiology and pathology aid in figuring out tumors, assessing their aggressiveness, and suggesting potential remedy options. In oncology, AI models are accelerating drug discovery and en

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Doykov, Mladen, Stanislav Valkanov, Usman Khalid, et al. "Artificial Intelligence-Augmented Advancements in the Diagnostic Challenges Within Renal Cell Carcinoma." Journal of Clinical Medicine 14, no. 7 (2025): 2272. https://doi.org/10.3390/jcm14072272.

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Background: Advancements in artificial intelligence (AI) diagnostics for renal cell carcinoma (RCC) provide valuable information for classification and subtyping, which improve treatment options and patient care. RCC diagnoses are most commonly incidental due to a lack of specific characterizations of subtypes, often leading to overtreatment. Accurate diagnosis also allows for personalized patient management. Different diagnostic methods, such as histopathology, multi-omics, imaging, and perioperative diagnostics, show a lot of promise for AI. Objective: This literature review focuses on devel

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Giansanti, Daniele, Andrea Lastrucci, Antonia Pirrera, Sandra Villani, Elisabetta Carico, and Enrico Giarnieri. "AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies." Bioengineering 12, no. 7 (2025): 769. https://doi.org/10.3390/bioengineering12070769.

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Background: The integration of artificial intelligence (AI) into cervical cancer diagnostics has shown promising advancements in recent years. AI technologies, particularly in the analysis of cytological images, offer potential improvements in diagnostic accuracy and screening efficiency. However, challenges regarding model generalizability, explainability, and operational integration into clinical workflows persist, impeding widespread adoption. Aim: This narrative review aims to critically evaluate the current state of AI in cervical cancer diagnostic cytology, identifying trends, key develo

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Rehan, Dominka, Sven Solisch, Anna Blazhkova, Anna Susłow, Adam Szwed, and Ewa Szczęsna. "Advancing Alzheimer’s Diagnosis: The Role of AI - A Review." Journal of Education, Health and Sport 77 (January 9, 2025): 57093. https://doi.org/10.12775/jehs.2025.77.57093.

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Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disease that accounts for more than half of all cases of dementia worldwide. An aging society therefore poses a huge challenge to medicine. The exact mechanism responsible for this disease is still not fully understood. However, theories of neurodegeneration related to the deposition of pathological proteins in the brain and the imbalance between individual neurotransmitters have allowed the development of effective diagnostic methods - laboratory determination of specific biomarkers (tau protein, β-amyloid) and their ma

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Sheliemina, Nataliia. "The Use of Artificial Intelligence in Medical Diagnostics: Opportunities, Prospects and Risks." Health Economics and Management Review 5, no. 2 (2024): 104–24. http://dx.doi.org/10.61093/hem.2024.2-07.

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Rapid advancements in AI (artificial intelligence) technologies, including machine learning, natural language processing, and computer vision, have developed sophisticated tools capable of performing complex medical tasks. The AI integration in healthcare can revolutionise the industry by improving patient outcomes, optimising resource allocation, and reducing operational costs. However, the AI use in medicine carries certain risks related to ethics and data privacy, shortcomings in the quality of data for training algorithms, and importance of protecting against cyberthreats. There is also a

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Mohammed, Muna Haider, and Aya Abd Alwahab Ahmed. "Artificial Intelligence Applications in Cancer Diagnostic Devices: A Focused Review." Middle Eastern Cancer and Oncology Journal 1, no. 2 (2025): 38–42. https://doi.org/10.61706/mecoj160160.

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The field of cancer diagnostics is undergoing a rapid transformation due to the advent of artificial intelligence (AI), which has the potential to enhance diagnostic accuracy, efficiency, and the ability to customize care to individual patients. This scoping review explores the landscape of AI technologies, with a particular focus on deep learning, machine learning, and generative models, as well as their integration into medical imaging, digital pathology, and genomic profiling. These technologies offer a multitude of benefits, including higher diagnostic precision, early-stage cancer detecti

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Matei, Sergiu-Ciprian, Sorin Olariu, Ana-Maria Ungureanu, Daniel Malita, and Flavia Medana Petrașcu. "Does Artificial Intelligence Bring New Insights in Diagnosing Phlebological Diseases?—A Systematic Review." Biomedicines 13, no. 4 (2025): 776. https://doi.org/10.3390/biomedicines13040776.

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Background/Objectives: Artificial intelligence (AI) is rapidly transforming the landscape of modern medicine, offering advanced tools for diagnosing complex conditions. In the realm of venous pathologies such as chronic venous disease (CVD), venous reflux, and deep venous thrombosis (DVT), AI has shown tremendous potential to improve diagnostic accuracy, streamline workflows, and enhance clinical decision-making. This study aims to evaluate the efficacy and feasibility of AI algorithms in diagnosing venous diseases and explore their potential impact on clinical practice. Methods: This paper pr

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Akomolafe, Opeoluwa Oluwanifemi, Augustine Onyeka Okoli, Irene Sagay, and Sandra Oparah. "AI in Diagnostics and the Law: Regulating Machine-Learning Tools in Clinical Decision-Making." Journal of Frontiers in Multidisciplinary Research 2, no. 2 (2025): 135–47. https://doi.org/10.54660/.ijfmr.2021.2.2.135-147.

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The rapid integration of artificial intelligence (AI) and machine learning (ML) tools into clinical diagnostics is transforming healthcare delivery, offering unprecedented potential for improving diagnostic accuracy, efficiency, and early disease detection. From radiology and pathology to genomics and predictive analytics, AI systems are increasingly used to assist clinicians in complex decision-making processes. However, the growing reliance on these technologies raises significant legal and regulatory challenges that must be addressed to ensure patient safety, fairness, and accountability. T

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Ali, Rizwan, and Haiyan Cui. "Unleashing the potential of AI in modern healthcare: Machine learning algorithms and intelligent medical robots." Research on Intelligent Manufacturing and Assembly 3, no. 1 (2024): 100–108. http://dx.doi.org/10.25082/rima.2024.01.002.

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Artificial intelligence (AI) is playing an increasingly vital role in transforming the medical field, particularly in areas like medical imaging, clinical decision-making, pathology, and minimally invasive surgery. The rapid growth of medical data and the continuous refinement of machine learning algorithms have propelled AI's integration into healthcare. This study explores the advancements and applications of AI, specifically machine learning algorithms and intelligent medical robots, in enhancing diagnostics, treatment, and healthcare delivery. A comprehensive review of current AI applicati

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Halder, Sakib Alam. "Next-Generation Microbe Detection: The Role of Artificial Intelligence in the Diagnostic Modern Microbiology." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51354.

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Artificial Intelligence (AI) is transforming clinical microbiology, especially microbe identification. With the advances in computational biology, machine learning (ML), and deep learning (DL), AI tools are emerging to speed up, improve the accuracy of, and make microbial diagnostics more efficient. These instruments process sophisticated biological information such as genomic sequences, microscopy images, spectrometry results, and patient metadata, thus allowing for accurate identification of pathogens. The numerous AI applications in the identification of microbes, such as genome-based predi

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McRae, Michael P., Kritika S. Rajsri, Timothy M. Alcorn, and John T. McDevitt. "Smart Diagnostics: Combining Artificial Intelligence and In Vitro Diagnostics." Sensors 22, no. 17 (2022): 6355. http://dx.doi.org/10.3390/s22176355.

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We are beginning a new era of Smart Diagnostics—integrated biosensors powered by recent innovations in embedded electronics, cloud computing, and artificial intelligence (AI). Universal and AI-based in vitro diagnostics (IVDs) have the potential to exponentially improve healthcare decision making in the coming years. This perspective covers current trends and challenges in translating Smart Diagnostics. We identify essential elements of Smart Diagnostics platforms through the lens of a clinically validated platform for digitizing biology and its ability to learn disease signatures. This platfo

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Maturi, Bhanu, Subash Dulal, Suresh Babu Sayana, et al. "Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography." Journal of Clinical Medicine 14, no. 2 (2025): 625. https://doi.org/10.3390/jcm14020625.

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Background: Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise. Methods: A comprehensive review of existing literature was conducted to

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Savage, Neil. "How AI is improving cancer diagnostics." Nature 579, no. 7800 (2020): S14—S16. http://dx.doi.org/10.1038/d41586-020-00847-2.

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Dziatkovskii, A., and D. Kulagina. "AI AND BLOCKCHAIN FOR NEUROPSYCHOLOGY." Danish scientific journal, no. 64 (September 27, 2022): 46–47. https://doi.org/10.5281/zenodo.7140075.

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<strong>Abstract</strong> Advances in neuropsychology, along with other neuroscience, serve as a basis for the development of artificial intelligence systems. Meanwhile, there is the opposite direction of research on the use of artificial intelligence in neuropsychology. This article examines the possibilities of artificial intelligence in alliance with blockchain in neuropsychological diagnostics and the analysis of its results.

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Shaikh, Mujeeb Ahmed, Hazim Saleh Al-Rawashdeh, and Abdul Rahaman Wahab Sait. "A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques." Life 15, no. 3 (2025): 390. https://doi.org/10.3390/life15030390.

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Background: Down syndrome (DS) is one of the most prevalent chromosomal abnormalities affecting global healthcare. Recent advances in artificial intelligence (AI) and machine learning (ML) have enhanced DS diagnostic accuracy. However, there is a lack of thorough evaluations analyzing the overall impact and effectiveness of AI-based DS diagnostic approaches. Objectives: This review intends to identify methodologies and technologies used in AI-driven DS diagnostics. It evaluates the performance of AI models in terms of standard evaluation metrics, highlighting their strengths and limitations. M

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Cibaca, Khandelwal. "Ethical Challenges in Deploying AI in Medical Diagnostics: A Case Study Approach." International Journal on Science and Technology 15, no. 4 (2024): 1–7. https://doi.org/10.5281/zenodo.14752323.

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The rapid integration of artificial intelligence (AI) into medical diagnostics is transforming healthcare at an unprecedented pace. By November 2024, over 70% of hospitals in high-income countries are projected to be using AI in at least one diagnostic function. This swift advancement, while promising accurate, efficient, and early disease detection, also brings forth critical ethical challenges related to bias, accountability, transparency, and patient autonomy. This paper explores these issues through illustrative examples, proposes solutions grounded in ethical frameworks, and discusses the

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Al-Hassani, Raghad Tariq. "Artificial Intelligence for Medical Diagnostics in IoT-Based Healthcare Networks: Foundations and Future Trends." Babylonian Journal of Networking 2025 (July 4, 2025): 70–79. https://doi.org/10.58496/bjn/2025/006.

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AI is quickly transforming the landscape of medical diagnostics, leading to remarkable gains in accuracy, speed, and availability. We perform a systematic review on the fundamental strategies, tools, applications, and challenges of AI enabled diagnostic in medicine with focus on medical diagnostics in IoT-based healthcare networks. The paper presents the use of machine learning algorithms, deep learning models, including CNN, and NLP to interpret clinical documentation. It also investigates the usage of smart computing infrastructures such as edge systems and the Internet of Medical Things (Io

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Mohd Baqir, Kulbir Singh, Shubam Singh, and Tanya Sharma. "Current trends in tuberculosis diagnosis: Advancements and challenges." International Journal of Allied Medical Sciences and Clinical Research 13, no. 2 (2025): 137–41. https://doi.org/10.61096/ijamscr.v13.iss2.2025.137-141.

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Tuberculosis [TB] is still a worldwide health issue, and early and accurate diagnosis of TB is essential for the management of the disease. Although conventional diagnostic methods, including sputum smear microscopy and culture methods, provide some diagnostic information, these methods have challenges in terms of sensitivity, specificity, and turnaround time. Recent TB diagnostics include molecular-based diagnostics, such as GeneXpert MTB/RIF and line-probe assays, which provide rapid and highly sensitive TB detection, including in drug-resistant cases. Next-generation sequencing and whole ge

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Pawan, Joshi, Tiwari Shubhangi, Bhatt Sanjay, and Sati Bindu. "Artificial Intelligence in Healthcare: From Diagnostics to Personalized Medicine And Beyond." International Journal of Pharmaceutical and Clinical Research 16, no. 9 (2024): 68–72. https://doi.org/10.5281/zenodo.13885454.

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Artificial intelligence (AI) is being increasingly integrated into healthcare systems to enhance treatment, diagnosis, and patient care. This review evaluates the significant applications of AI in healthcare, particularly its impact on medical imaging, genomics, and early diagnosis, as well as its role in reducing errors and increasing efficiency. Deep learning, Machine learning, and natural language processing (NLP) are among the most impactful AI technologies being deployed. While AI holds immense promise, challenges, such as ethical concerns, bias, and regulatory hurdles, must be addressed

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Vegesna, Dr Vinod. "AI-Assisted Diagnosis of Rare Genetic Disorders : A Case Study." International Journal of Innovative Research in Advanced Engineering 11, no. 07 (2024): 753–59. http://dx.doi.org/10.26562/ijirae.2024.v1107.04.

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Artificial intelligence (AI) has completely changed the diagnostics of medical conditions, especially when it comes to finding uncommon genetic abnormalities. In order to improve the precision and speed of diagnosing uncommon genetic disorders, this case study investigates the effectiveness and promise of AI-assisted diagnostic tools. We examine patient genomic data to find patterns and abnormalities suggestive of particular genetic illnesses by utilising cutting-edge machine learning methods, especially deep learning and neural networks. To develop and validate our AI models, our research com

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Elkefi, Safa, Hongwei Wang, and Onur Asan. "Organizational considerations from HFE to speed up the adoption of AI-related technology in medical diagnostics." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 9, no. 1 (2020): 230–34. http://dx.doi.org/10.1177/2327857920091060.

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Diagnostic errors contribute to hospital complications that can lead to death. It is essential to create a favorable environment for implementing AI-related technologies to improve medical diagnostics. This study aims to present the different categories of A.I. diagnostic applications, as well as the organizational factors and policies, inﬂuencing the best adoption and implementation of A.I. applications. We conducted an online database search to identify peer-reviewed papers published between Jan 2009 and May 2019 that were related to A.I. applications in medical diagnostics. Papers were incl

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