Jackups, Ronald

Context.— Generative artificial intelligence (GAI) technologies are likely to dramatically impact health care workflows in clinical pathology (CP). Applications in CP include education, data mining, decision support, result summaries, and patient trend assessments. Objective.— To review use cases of GAI in CP, with a particular focus on large language models. Specific examples are provided for the applications of GAI in the subspecialties of clinical chemistry, microbiology, hematopathology, and molecular diagnostics. Additionally, the review addresses potential pitfalls of GAI paradigms. Data Sources.— Current literature on GAI in health care was reviewed broadly. The use case scenarios for each CP subspecialty review common data sources generated in each subspecialty. The potential for utilization of CP data in the GAI context was subsequently assessed, focusing on issues such as future reporting paradigms, impact on quality metrics, and potential for translational research activities. Conclusions.— GAI is a powerful tool with the potential to revolutionize health care for patients and practitioners alike. However, GAI must be implemented with much caution considering various shortcomings of the technology such as biases, hallucinations, practical challenges of implementing GAI in existing CP workflows, and end-user acceptance. Human-in-the-loop models of GAI implementation have the potential to revolutionize CP by delivering deeper, meaningful insights into patient outcomes both at an individual and a population level.

Context.— Generative artificial intelligence (AI) technologies are rapidly transforming numerous fields, including pathology, and hold significant potential to revolutionize educational approaches. Objective.— To explore the application of generative AI, particularly large language models and multimodal tools, for enhancing pathology education. We describe their potential to create personalized learning experiences, streamline content development, expand access to educational resources, and support both learners and educators throughout the training and practice continuum. Data Sources.— We draw on insights from existing literature on AI in education and the collective expertise of the coauthors within this rapidly evolving field. Case studies highlight practical applications of large language models, demonstrating both the potential benefits and unique challenges associated with implementing these technologies in pathology education. Conclusions.— Generative AI presents a powerful tool kit for enriching pathology education, offering opportunities for greater engagement, accessibility, and personalization. Careful consideration of ethical implications, potential risks, and appropriate mitigation strategies is essential for the responsible and effective integration of these technologies. Future success lies in fostering collaborative development between AI experts and medical educators, prioritizing ongoing human oversight and transparency to ensure that generative AI augments, rather than supplants, the vital role of educators in pathology training and practice.

Abstract Patients treated with antineoplastic therapy often develop thrombocytopenia requiring platelet transfusion, which has potential to exacerbate pulmonary injury. This study tested the hypothesis that amotosalen-UVA pathogen–reduced platelet components (PRPCs) do not potentiate pulmonary dysfunction compared with conventional platelet components (CPCs). A prospective, multicenter, open-label, sequential cohort study evaluated the incidence of treatment-emergent assisted mechanical ventilation initiated for pulmonary dysfunction (TEAMV-PD). The first cohort received CPC. After the CPC cohort, each site enrolled a second cohort transfused with PRPC. Other outcomes included clinically significant pulmonary adverse events (CSPAE) and the incidence of treatment-emergent acute respiratory distress syndrome (TEARDS) diagnosed by blinded expert adjudication. The incidence of TEAMV-PD in all patients (1068 PRPC and 1223 CPC) was less for PRPC (1.7 %) than CPC (3.1%) with a treatment difference of –1.5% (95% confidence interval [CI], –2.7 to –0.2). In patients requiring ≥2 PCs, the incidence of TEAMV-PD was reduced for PRPC recipients compared with CPC recipients (treatment difference, –2.4%; 95% CI, –4.2 to –0.6). CSPAE increased with increasing PC exposure but were not significantly different between the cohorts. For patients receiving ≥2 platelet transfusions, TEARDS occurred in 1.3% PRPC and 2.6% CPC recipients (P = .086). Bayesian analysis demonstrated PRPC may be superior in reducing TEAMV-PD and TEARDS for platelet transfusion recipients compared with CPC recipients, with 99.2% and 88.8% probability, respectively. In this study, PRPC compared with CPC demonstrated high probability of reduced severe pulmonary injury requiring assisted mechanical ventilation in patients with hematology disorders dependent on platelet transfusion. This trial was registered at www.ClinicalTrials.gov as #NCT02549222.

AbstractBackgroundPlatelet transfusion carries risk of transfusion‐transmitted infection (TTI). Pathogen reduction of platelet components (PRPC) is designed to reduce TTI. Pulmonary adverse events (AEs), including transfusion‐related acute lung injury and acute respiratory distress syndrome (ARDS) occur with platelet transfusion.Study designAn open label, sequential cohort study of transfusion‐dependent hematology‐oncology patients was conducted to compare pulmonary safety of PRPC with conventional PC (CPC). The primary outcome was the incidence of treatment‐emergent assisted mechanical ventilation (TEAMV) by non‐inferiority. Secondary outcomes included: time to TEAMV, ARDS, pulmonary AEs, peri‐transfusion AE, hemorrhagic AE, transfusion reactions (TRs), PC and red blood cell (RBC) use, and mortality.ResultsBy modified intent‐to‐treat (mITT), 1068 patients received 5277 PRPC and 1223 patients received 5487 CPC. The cohorts had similar demographics, primary disease, and primary therapy. PRPC were non‐inferior to CPC for TEAMV (treatment difference −1.7%, 95% CI: (−3.3% to −0.1%); odds ratio = 0.53, 95% CI: (0.30, 0.94). The cumulative incidence of TEAMV for PRPC (2.9%) was significantly less than CPC (4.6%, p = .039). The incidence of ARDS was less, but not significantly different, for PRPC (1.0% vs. 1.8%, p = .151; odds ratio = 0.57, 95% CI: (0.27, 1.18). AE, pulmonary AE, and mortality were not different between cohorts. TRs were similar for PRPC and CPC (8.3% vs. 9.7%, p = .256); and allergic TR were significantly less with PRPC (p = .006). PC and RBC use were not increased with PRPC.DiscussionPRPC demonstrated reduced TEAMV with no excess treatment‐related pulmonary morbidity.