Aug 2026· Biomedical Reports· Vol 25, pp. 1-10· 0 citations· 26 references
Medicine
TL;DR
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.
Abstract
The integration of artificial intelligence (AI) into digital pathology is perhaps the most revolutionary leap forward in modern diagnostic medicine. 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. AI systems have achieved pathologist-level performance in controlled settings, including diagnostic accuracy >99% and area under the receiver operating characteristic curve values exceeding 0.97. However, translating research into clinical adoption is riddled with several challenges attributable to computational requirements, data standardization issues, regulatory hurdles and limitations in generalizability. Moreover, Vision Transformers are widely popular as powerful alternatives to conventional convolutional models, delivering high performance in certain domains while also imposing a novel computational burden. Overcoming these challenges is a prerequisite for the successful integration of AI into pathology practice and the realization of its full diagnostic potential.
The progressive integration of Whole Slide Imaging (WSI) technology and Artificial Intelligence (AI) architectures is driving a structural transformation in pathology and precision oncology. This structured critical review analyzes and systematizes the impact of this technological transition along two fundamental operational dimensions of the modern biopharmaceutical industry: pre-registration Clinical Research and post-launch strategies governed by Medical Affairs. The first section explores how computational pathology is improving efficiency and reducing risk in drug development. Replacing analog visual assessment—intrinsically subject to inter-observer and intra-observer variability—with quantitative algorithms for cellular classification and segmentation enables optimization of patient recruitment in clinical trials, reducing screening failure rates. This review also examines the emerging role of Spatial Biology in extracting complex topological metrics from the Tumor Microenvironment (TME) and the use of AI for the objective and auditable quantification of critical surrogate endpoints, such as Pathological Complete Response (pCR), while acknowledging that algorithmic precision remains sensitive to pre-analytical variables and dataset biases. In the second section, the study investigates the strategic evolution of Medical Affairs, acting as a vital scientific communication and translational bridge between the complexity of Data Science and clinical hospital practice. Challenges related to AI adoption by clinicians are examined, emphasizing the importance of educational programs based on Explainable AI (XAI) to overcome the cognitive limitations of the black-box paradigm and the complex regulatory validation pathway for Software as a Medical Device (SaMD) under the stringent European IVDR framework—supported by an analysis of historical regulatory benchmarks such as the Paige Prostate case. The paper also explores the potential of AI in the large-scale generation of Real-World Evidence (RWE), applied to the creation of synthetic control arms in pharmacoeconomic settings. In conclusion, the study highlights that the diagnostic algorithm has ceased to be merely a laboratory support tool and has become a strategic asset and an integral adjunct to therapeutic decision-making. Overcoming current challenges related to data privacy through Federated Learning architectures, together with the imminent transition toward Foundation Models, foreshadows a fully data-driven healthcare ecosystem, making continuous skills development (digital upskilling) an essential requirement for professionals in the biopharmaceutical sector.
Carmela Baviello, D. Capuano, R. Verna· Life· 0 citations
Artificial intelligence (AI) has emerged as a promising adjunct in surgical pathology, particularly in the diagnosis of prostate cancer, where variability in interpretation or missed cancer foci can significantly affect patient management. This review provides a concise, practice-oriented overview of the two Food and Drug Administration (FDA)-cleared AI tools for prostate biopsy interpretation: Paige Prostate Detect and Ibex Prostate Detect (formerly Galen Second Read). We examine their regulatory indications, diagnostic performance and integration requirements within digital pathology workflows. Emphasis is placed on real-world implementation considerations, including variation in technical inputs and the level at which data are analysed. We highlight less obvious risks, such as domain shift and the potential for inequitable performance in under-represented patient populations. Trade-offs between sensitivity and specificity, particularly in the context of AI-assisted pathologist assessments, are discussed using data from clinical validation studies. We also consider the variable impact of AI tools depending on the user’s expertise, noting enhanced diagnostic consistency for general pathologists. By highlighting both the opportunities and limitations of integrating AI into routine practice, we aim to provide pathologists with a pragmatic understanding of how these systems may influence diagnostic workflows and to emphasise that FDA clearance must be complemented by local validation as well as ongoing performance monitoring to ensure safe and equitable deployment.
Quinn Rainer, Yue Sun, Monika Vyas et al.· Journal of Clinical Patholog...· 0 citations
The continued integration of AI has the potential to improve diagnostic accuracy, streamline radiological workflows, and support more personalized patient care in the next generation of diagnostic imaging.
Mr. Vishal Walia, Mr. Honey Thakur, Ms. Ashwarya Sharma et al.· PAIN, JOINTS, SPINE· 0 citations
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.
Taha Y. Abdulqader, Shatha A. Baker, Marwah Najm Abed et al.· NTU Journal of Pure Sciences· 0 citations
Background Whole‐slide imaging (WSI) has shifted pathology toward digital workflows, creating the foundation for applying artificial intelligence (AI) to diagnostic tasks. This review summarises validated AI applications in diagnostic pathology, with an emphasis on clinical performance, regulatory developments and the practical barriers that affect implementation. Methods A structured search of PubMed, Scopus and Google Scholar identified English‐language, peer‐reviewed studies from January 2020 to May 2025. Eligible studies applied AI to diagnostic, grading or prognostic tasks in human tissue, used a pathologist‐confirmed reference standard and included external or multi‐centre validation. Results More than 150,000 digital slides were represented across the included studies. Reported performance metrics demonstrated strong diagnostic accuracy across several validated applications. Large meta‐analyses and externally validated studies reported sensitivity values exceeding 96% and specificity above 93% for selected cancer‐detection tasks, while other studies demonstrated high agreement for Gleason grading (QWK up to 0.862) and biomarker quantification (Ki‐67 ICC 0.98). Conclusion AI has the capacity to strengthen diagnostic pathology by improving consistency, measurement and efficiency. Moving from experimental use to routine reporting will require broad validation across centres, enhanced model transparency, strong quality‐assurance systems and close cooperation between developers and pathologists.
J. Shazia Fathima, Mugundan Raghavelu Narendran, Mohammed Sharique Ahmed Quadri et al.· Analytical Cellular Patholog...· 0 citations
ABSTRACT
The adoption of whole-slide imaging is establishing a new paradigm in digital pathology. However, the translation of artificial intelligence (AI) from research to clinical practice faces significant hurdles, largely due to a misalignment between algorithmic advances and the practical demands of pathological diagnosis and prognosis. In this review, we propose a dual-perspective framework to systematically bridge this gap by linking core clinical tasks with cutting-edge deep learning methodologies. We present a comprehensive overview of the field from 2020 to 2025, analyzing how architectures such as convolutional neural networks, vision transformers, and graph neural networks are being adapted for diagnostic classification, tissue segmentation, and prognostic prediction. A key contribution is our novel algorithm-clinical task mapping framework, which offers practical guidance for selecting and designing AI solutions tailored to specific clinical goals. We also highlight emerging trends that minimize reliance on costly annotations-including weakly supervised and self-supervised learning-as well as advances in predicting immunohistochemistry results directly from hematoxylin and eosin-stained slides. Finally, we address critical challenges related to model interpretability, regulatory approval, and multicenter generalization, and outline a future pathway focused on developing integrated, trustworthy, and equitable AI systems that enhance, rather than replace, the expertise of pathologists.
Yun-qiu Gao, Teng Ma, Lisha Li et al.· Chinese Medical Journal· 0 citations