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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.

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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.

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People regularly share retellings of their personal events through social media websites to elicit feedback about the reasonability of their actions in the event's context. In this paper, we explore how learning approaches can be used toward the goal of classifying reasonability in personal retellings of events shared on social media. We collect 13,748 community-labeled posts from /r/AmITheAsshole, a subreddit in which Reddit users share retellings of personal events which are voted upon by community members. We build and evaluate a total of 21 machine learning models across seven types of models and three distinct feature sets. We find that our best-performing model can predict the reasonability of a post with an F1 score of .76. Our findings suggest that features derived from the post and author metadata were more predictive than simple linguistic features like the post sentiment and types of words used. We conclude with a discussion on the implications of our findings as they relate to sharing retellings of personal events on social media and beyond.

O ptic neuropathy because of infiltration by leukemia or lymphoma is rare, and confirmed through optic nerve MRI, demonstrating enlargement, increased T2 signal, and enhancement of the optic nerve and leukemia or lymphoma cells in the cerebrospinal fluid (CSF) or on biopsy (1). We present 2 cases of posterior ischemic optic neuropathy (PION) because of meningeal leukemia or lymphoma infiltrating the optic nerve sheath, describe characteristic MRI findings, and propose a pathophysiologic cause (see Supplemental Digital Content, Text, http://links.lww.com/ WNO/A653, for more background information).The first patient was a 29-year-old woman with relapsed acute B-cell lymphoblastic leukemia. She experienced complete vision loss in the right eye with right-sided headache, and difficulty focusing out of her left eye, without other neurologic or visual symptoms. Vision was no light perception (NLP) in the right eye and 20/400 in the left eye, with a right-sided afferent pupillary defect (APD). The right optic disc was pale. There were no other findings on ophthalmic examination. Orbital MRI revealed an enlarged right optic nerve with increased T2 signal. After contrast administration (Fig. 1A), there was enhancement in the perioptic subarachnoid space, but no enhancement of the optic nerve itself. Diffusion-weighted imaging (DWI) (Fig. 1B) revealed restricted diffusion in the optic nerve, confirmed on apparent diffusion coefficient (ADC) map (Fig. 1C). Lumbar puncture revealed B lymphoblasts in the CSF. The patient refused further treatment, was transferred to hospice care, and died.The second patient was a 66-year-old woman with relapsed large B-cell lymphoma. She developed complete loss of vision in the right eye over 3 days, with right eye pain, and no other neurologic or visual symptoms. Vision