As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine.

Background: Network-connected medical devices have rapidly proliferated in the wake of recent global catalysts, leaving clinical laboratories and healthcare organizations vulnerable to malicious actors seeking to ransom sensitive healthcare information. As organizations become increasingly dependent on integrated systems and data-driven patient care operations, a sudden cyberattack and the associated downtime can have a devastating impact on patient care and the institution as a whole. Cybersecurity, information security, and information assurance principles are, therefore, vital for clinical laboratories to fully prepare for what has now become inevitable, future cyberattacks. Content: This review aims to provide a basic understanding of cybersecurity, information security, and information assurance principles as they relate to healthcare and the clinical laboratories. Common cybersecurity risks and threats are defined in addition to current proactive and reactive cybersecurity controls. Information assurance strategies are reviewed, including traditional castle-and-moat and zero-trust security models. Finally, ways in which clinical laboratories can prepare for an eventual cyberattack with extended downtime are discussed. Summary: The future of healthcare is intimately tied to technology, interoperability, and data to deliver the highest quality of patient care. Understanding cybersecurity and information assurance is just the first preparative step for clinical laboratories as they ensure the protection of patient data and the continuity of their operations.

Digital workflow transformation continues to sweep throughout a diversity of pathology departments spanning the globe following catalyzation of whole slide imaging (WSI) adoption by the SARS-CoV-2 (COVID-19) pandemic. The utility of WSI for a litany of use cases including primary diagnosis has been emphasized during this period, with WSI scanning devices gaining the approval of healthcare regulatory bodies and practitioners alike for clinical applications following extensive validatory efforts. As successful validation for WSI is predicated upon pathologist diagnostic interpretability of digital images with high glass slide concordance, departmental adoption of WSI is tantamount to the reliability of such images often predicated upon quality assessment notwithstanding image interpretability but extending to quality of practice following WSI adoption. Metrics of importance within this context include failure rates inclusive of different scanning errors that result in poor image quality and the potential such errors may incur upon departmental turnaround time (TAT). We sought to evaluate the impact of WSI implementation through retrospective evaluation of scan failure frequency in archival versus newly prepared slides, types of scanning error, and impact upon TAT following commencement of live WSI operation in May 2017 until the present period within a fully digitized high-volume academic institution. A 1.19% scan failure incidence rate was recorded during this period, with re-scanning requested and successfully executed for 1.19% of cases during the reported period of January 2019 until present. No significant impact upon TAT was deduced, suggesting an outcome which may be encouraging for departments considering digital workflow adoption.

Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.

Artificial intelligence (AI) is now a practical, value‐generating tool in genitourinary (GU) pathology. Real‐world deployments report up to 65% time‐savings and multi‐million‐dollar returns on investment within 3 years at high‐volume centres. Across prostate, bladder, renal and testicular systems, contemporary algorithms equal or exceed expert accuracy for cancer detection, grading and prognostication. Foundation models trained on millions of whole‐slide images now match specialized organ‐specific tools without bespoke tuning. High AI–pathologist concordance is widely regarded as a surrogate marker of safety and clinical acceptability, yet no universally codified regulatory threshold for sensitivity, specificity or concordance has been issued. Because internationally recognized guidelines still omit detailed instructions for safe roll‐out and sustained performance, we distilled insights from real‐world deployments and pioneering pilot studies into two complementary roadmaps: the nine‐step VALIDATED framework, which focuses on governance and safety oversight, and the 11‐principle ORCHESTRATE blueprint, which guides day‐to‐day implementation. By 2030, we anticipate AI will automate ~80% of routine quantification, allowing pathologists to assume the role of diagnostic orchestrators who integrate multimodal data streams, helping offset a ~40% workforce shortfall and reducing inter‐observer variability across practice settings. This review distils the evidence, economics and practical guidance required for successful AI adoption in GU pathology. Institutions following the VALIDATED–ORCHESTRATE pathway can harness efficiency gains while maintaining diagnostic excellence and achieving positive ROI within 5 years.

Bladder cancer diagnosis is challenged by invasive monitoring and workflow inefficiencies impacting diagnostic reliability. This prospective study enrolled 150 patients (January 2020-December 2022) and evaluated a novel liquid-based immunocytochemistry platform, coupled with integrated machine learning, for detecting urothelial carcinoma in voided urine. All 150 cytology slides met the adequacy threshold of ≥2,644 urothelial cells and showed preserved cytomorphology. Eight cases of papillary urothelial neoplasm of low malignant potential (PUNLMP) were set aside a-priori, yielding an analytic cohort of 142 patients (115 urothelial-carcinoma, 27 benign) for performance analysis. hTERT (sensitivity 92.2%, specificity 66.7%), GATA-3 (67.0%, 88.9%), and CK17 (89.6%, 66.7%). In multi-marker analysis, sensitivity reached 100% (95% CI 96.8-100) when any marker was positive, whereas specificity reached 100% (95% CI 87.3-100) when all three markers were positive. The workflow-optimized platform standardizes specimen preparation and multi-marker interpretation, offering a robust foundation for urine-based bladder-cancer diagnostics. Larger, multi-center validation studies are warranted to refine specificity estimates and facilitate laboratory integration. This study demonstrates that addressing fundamental workflow challenges in bladder cancer diagnostics before implementing artificial intelligence creates more effective diagnostic tools. By prioritizing specimen integrity and standardization through a novel liquid immunocytochemistry platform, exceptional diagnostic performance was achieved with 100% sensitivity and specificity under defined marker parameters across various cancer stages. This workflow-first approach to integrating machine learning with advanced biomarker analysis offers a model for developing clinically practical diagnostic innovations that can reduce reliance on invasive monitoring procedures while improving detection accuracy.