Jul 2026· NTU Journal of Pure Sciences· Vol 5, pp. 138-145· 0 citations· 23 references
TL;DR
An all-encompassing review of current advances in AI requests across miscellaneous healing concentration, containing endemic disease discovery, main central nervous system, cardiology, tumor, and ophthalmology, and oncology is determined.
Abstract
Artificial intelligence (AI) has enhance critical to implant healing interpreter, accompanying the allure of the potential to improve the speed, veracity, and ability of ailment disease and classification This study determines an all-encompassing review of current advances in AI requests across miscellaneous healing concentration, containing endemic disease discovery, main central nervous system, cardiology, tumor, and ophthalmology. The review examines standard AI systems to a degree, deep learning, machine intelligence, and mixture models, emphasizing their effectiveness in demonstrative tasks including biosignals, clinical dataset, and medical depictions. In addition, the study debate challenges to executing AI in healthcare, including limited data, model interpretability, moral concerns, and legal limits. The study decides by investigating future directions, including allied education, explainable AI, and unification accompanying the Internet of Medical Things (IoMT). In order to close the scientific and practical gaps between AI research and clinical use, this study integrates insights from infectious illnesses, cardiology, neurology, ophthalmology, and oncology. For researchers, physicians, and legislators seeking to appreciate the potential and disadvantages of artificial intelligence in reconstructing medical diagnoses, this study serves as a priceless resource.
In the healthcare industry, artificial intelligence (AI) has significantly enhanced treatment plans, pharmaceutical advancements, hospital administration, and diagnostic precision. This review examines the integration of AI across domains, including robotic surgery, drug development, medical imaging, epidemiology, and clinical decision-making. Techniques like deep learning and natural language processing (NLP) have proven remarkably effective in the domains of medical image interpretation, illness trajectory prediction, and healthcare infrastructure optimisation. However, increasing the fairness and transparency of AI is crucial to gaining the trust of patients and medical professionals. Looking forward, future advancements in medical AI are anticipated to be primarily driven by generative AI, federated learning, and multimodal AI. Instead of taking the place of human knowledge, artificial intelligence (AI) will be used as a supplementary tool to help healthcare professionals make better clinical decisions by giving them data-driven insights. It will be crucial to guarantee sustainable AI deployment and foster global cooperation to make AI-driven medical solutions inclusive and accessible. The success of AI in imaging will be measured by worth creation, which includes better patient outcomes, faster turnaround, higher diagnostic certainty, and a higher quality of work life for radiologists. AI offers a novel and fascinating collection of methods for analysing image data. Radiologists will likely be at the forefront of AI's medical applications as they explore these new possibilities.
Saba Waleed, Syed Zulfiqar Ali Shah, Rubia Anjum Tariq et al.· Veredas do Direito· 2 citations
The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care, but challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal access continue to restrict large-scale implementation.
Unknown authors· Journal of Cognitive Human-C...· 0 citations
This narrative review examines the evolution of artificial intelligence (AI) in healthcare, with a focus on the transition from early rule-based systems to modern deep learning architectures and their integration into clinical practice. We examine foundational technologies, including convolutional neural networks for image interpretation, vision transformers for modeling long-range dependencies, and generative adversarial networks for image reconstruction and synthesis. The review further discusses the emergence of multimodal foundation models that integrate imaging with textual and genomic data to enhance diagnostic robustness. The application of these technologies is analyzed across three primary domains: Radiology (image enhancement and automated interpretation), cardiology (electrocardiographic and echocardiography analysis), and oncology (tumor classification and treatment planning). Specific attention is given to the national context in Türkiye, highlighting local initiatives such as TEKNOFEST and TÜBİTAK-supported projects that foster domestic AI development. While AI offers significant benefits in terms of diagnostic accuracy and treatment workflow optimization, challenges regarding data privacy, algorithmic bias, and interpretability (“black box” issues) persist. Future progress depends on the development of explainable AI, rigorous prospective validation, and the establishment of ethical regulatory frameworks.
Abdulkadir Yıldırım, Ö. Özdemi̇r· Artificial Intelligence in M...· 0 citations
This study aims to illustrate the significance of Abstract Science Intelligence (XAI) in the medical field, outlining its role in enhancing the interpretability of traditional "black-box" AI models.The goal of this research is to shed light on the significance of Abstract Science Intelligence (XAI) in the medical sector, elucidating its function in bolstering the interpretability of conventional "black-box" Artificial Intelligence models. In medical imaging, disease diagnosis, clinical decision support, and precision medicine, deep learning techniques have proven to be extremely successful; however, the inability to explain and understand how deep learning models work raises a variety of challenges for clinician trust, patient safety, ethical accountability, and regulatory compliance. This review provides a comprehensive overview of recent developments in XAI for healthcare, focusing on key methods for achieving interpretability, such as intrinsically interpretable models, and post-hoc explanation methods such as SHAP, LIME, Grad-CAM, attention mechanisms, surrogate models, and counterfactual explanations. A detailed review of the use of these methods in a variety of clinical areas such as radiology, oncology, cardiology, genomics, electronic health records and drug discovery is also given. Furthermore, the paper examines technical issues concerning explanation fidelity, computational complexity, robustness, scalability, and model validation, as well as ethical issues such as fairness, transparency, privacy, bias, and governance. Other research trends are also discussed, such as causal explainability, humancentered XAI, models that are uncertain, human-in-the-loop systems, and evaluation frameworks. In summary, the review highlights that explainability is not just a technical aspect but a key component in creating AI systems that are both trustworthy and clinically sound and can assist in safe and effective health care decision-making.
S. R. Ali, .a. Meher Nisha, K. A. Sathik et al.· International Journal of Int...· 0 citations
Artificial intelligence (AI) has emerged as a transformative force in clinical medicine, reshaping how diseases are diagnosed, treatments are selected, and patient care is delivered. This comprehensive review examines the current state and future trajectory of AI applications across the clinical spectrum, from diagnostic algorithms that match or exceed human expert performance in medical imaging and pathology, to therapeutic decision support systems that optimize treatment selection, drug dosing, and surgical planning. We synthesize evidence from recent studies demonstrating that deep learning models have achieved diagnostic accuracy comparable to or exceeding that of board-certified specialists across multiple specialties, including radiology (AUC 0.92–0.98), pathology (AUC 0.90–0.97), dermatology (AUC 0.91–0.96), and ophthalmology (AUC 0.94–0.99). In therapeutics, AI-Driven dosing algorithms have reduced adverse drug events by 30–45% in prospective studies, while machine learning-based treatment response prediction has demonstrated AUC values of 0.78–0.88 across oncology, cardiology, and psychiatry. However, significant challenges persist, including the gap between algorithmic performance and clinical outcome improvement, the “black box” problem of model interpretability, algorithmic bias, and the paucity of prospective validation studies. We critically examine these barriers and outline future frontiers, including multimodal AI integration, foundation models, causal inference, and Human-AI collaboration. We conclude that while AI has demonstrated remarkable capabilities in controlled research settings, successful clinical translation requires rigorous prospective validation, workflow integration, and a commitment to addressing ethical and practical challenges.
Verena Lengston· Japan Journal of Clinical &a...· 0 citations
The present review analyzes the existing context of AI pathology systems, particularly diagnostic precision, clinical validation, and technical systems such as convolutional neural networks and transformers and discusses the integration challenge in clinical workflows for these systems.
Abdul-Mohsen G. Alhejaily, D. Alghamdi· Biomedical Reports· 0 citations
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