Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field’s inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI’s application in healthcare.

The objective of this study was to evaluate the use of social media among healthcare workers in an attempt to identify how it affects the quality of patient care. An anonymous survey of 35 questions was conducted in South Texas, on 366 healthcare workers. Of the 97% of people who reported owning electronic devices, 87.9% indicated that they used social media. These healthcare workers indicated that they spent approximately 1 h on social media every day. The healthcare workers below the age of 40 were more involved in social media compared to those above 40 (p < 0.05). The use of social media among physicians and nurses was noted to be identical (88% for each group), and both groups encouraged their patients to research their clinical conditions on social media (p < 0.05). A higher number of physicians reported awareness of a social media policy in their hospital compared to nurses (p < 0.05). However, a large proportion of healthcare workers (40%) were unaware of their workplace policy, which could potentially cause a privacy breach of confidential medical information. Further studies are required to evaluate specific effects of these findings on the quality of patient care.

Wound healing is an intricate process involving coordinated interactions among inflammatory cells, skin fibroblasts, keratinocytes, and endothelial cells. Successful tissue repair hinges on controlled inflammation, angiogenesis, and remodeling facilitated by the exchange of cytokines and growth factors. Comorbid conditions can disrupt this process, leading to significant morbidity and mortality. Stem cell therapy has emerged as a promising strategy for enhancing wound healing, utilizing cells from diverse sources such as endothelial progenitor cells, bone marrow, adipose tissue, dermal, and inducible pluripotent stem cells. In this systematic review, we comprehensively investigated stem cell therapies in chronic wounds, summarizing the clinical, translational, and primary literature. A systematic search across PubMed, Embase, Web of Science, Google Scholar, and Cochrane Library yielded 22,454 articles, reduced to 44 studies after rigorous screening. Notably, adipose tissue-derived mesenchymal stem cells (AD-MSCs) emerged as an optimal choice due to their abundant supply, easy isolation, ex vivo proliferative capacities, and pro-angiogenic factor secretion. AD-MSCs have shown efficacy in various conditions, including peripheral arterial disease, diabetic wounds, hypertensive ulcers, bullous diabeticorum, venous ulcers, and post-Mohs micrographic surgery wounds. Delivery methods varied, encompassing topical application, scaffold incorporation, combination with plasma-rich proteins, and atelocollagen administration. Integration with local wound care practices resulted in reduced pain, shorter healing times, and improved cosmesis. Stem cell transplantation represents a potential therapeutic avenue, as transplanted stem cells not only differentiate into diverse skin cell types but also release essential cytokines and growth factors, fostering increased angiogenesis. This approach holds promise for intractable wounds, particularly chronic lower-leg wounds, and as a post-Mohs micrographic surgery intervention for healing defects through secondary intention. The potential reduction in healthcare costs and enhancement of patient quality of life further underscore the attractiveness of stem cell applications in wound care. This systematic review explores the clinical utilization of stem cells and stem cell products, providing valuable insights into their role as ancillary methods in treating chronic wounds.

Purpose: Previous sequencing studies revealed that alterations of genes associated with DNA damage response (DDR) are enriched in men with metastatic castration-resistant prostate cancer (mCRPC). BRCA2, a DDR and cancer susceptibility gene, is frequently deleted (homozygous and heterozygous) in men with aggressive prostate cancer. Here we show that patients with prostate cancer who have lost a copy of BRCA2 frequently lose a copy of tumor suppressor gene RB1; importantly, for the first time, we demonstrate that co-loss of both genes in early prostate cancer is sufficient to induce a distinct biology that is likely associated with worse prognosis. Experimental Design: We prospectively investigated underlying molecular mechanisms and genomic consequences of co-loss of BRCA2 and RB1 in prostate cancer. We used CRISPR-Cas9 and RNAi-based methods to eliminate these two genes in prostate cancer cell lines and subjected them to in vitro studies and transcriptomic analyses. We developed a 3-color FISH assay to detect genomic deletions of BRCA2 and RB1 in prostate cancer cells and patient-derived mCRPC organoids. Results: In human prostate cancer cell lines (LNCaP and LAPC4), loss of BRCA2 leads to the castration-resistant phenotype. Co-loss of BRCA2-RB1 in human prostate cancer cells induces an epithelial-to-mesenchymal transition, which is associated with invasiveness and a more aggressive disease phenotype. Importantly, PARP inhibitors attenuate cell growth in human mCRPC-derived organoids and human CRPC cells harboring single-copy loss of both genes. Conclusions: Our findings suggest that early identification of this aggressive form of prostate cancer offers potential for improved outcomes with early introduction of PARP inhibitor–based therapy. See related commentary by Mandigo and Knudsen, p. 1784

Cancer remains one of the leading causes of mortality worldwide, driving the need for innovative approaches in research and treatment. Artificial intelligence (AI) has emerged as a powerful tool in oncology, with the potential to revolutionize cancer diagnosis, treatment, and management. This paper reviews recent advancements in AI applications within cancer research, focusing on early detection through computer-aided diagnosis, personalized treatment strategies, and drug discovery. We survey AI-enhanced diagnostic applications and explore AI techniques such as deep learning, as well as the integration of AI with nanomedicine and immunotherapy for cancer care. Comparative analyses of AI-based models versus traditional diagnostic methods are presented, highlighting AI’s superior potential. Additionally, we discuss the importance of integrating social determinants of health to optimize cancer care. Despite these advancements, challenges such as data quality, algorithmic biases, and clinical validation remain, limiting widespread adoption. The review concludes with a discussion of the future directions of AI in oncology, emphasizing its potential to reshape cancer care by enhancing diagnosis, personalizing treatments and targeted therapies, and ultimately improving patient outcomes.

Hospital length of stay (HLOS) is a critical healthcare metric influencing patient outcomes, resource utilization, and healthcare costs. While reducing HLOS can improve hospital efficiency and patient throughput, it also poses risks such as premature discharge, increased readmission rates, and potential compromise of patient safety. This literature review synthesizes current evidence on the determinants of HLOS, including patient-specific factors such as demographics, comorbidities, and socioeconomic status, as well as hospital-related factors like admission route, resource allocation, and institutional policies. We also examine the relationship between HLOS and key clinical outcomes, including mortality, readmission rates, and healthcare-associated infections. Additionally, we evaluate predictive modeling approaches, including artificial intelligence and machine learning, for forecasting HLOS and guiding early intervention strategies. While interventions such as enhanced recovery after surgery (ERAS) protocols, multidisciplinary care teams, and structured discharge planning have demonstrated efficacy in reducing HLOS, their success varies based on healthcare setting, patient complexity, and resource availability. Predictive analytics, incorporating clinical and non-clinical variables, offer promising avenues for improving hospital efficiency, yet may carry risks related to data quality and model bias. Given the impact of HLOS on clinical and economic outcomes, targeted interventions and predictive models should be applied cautiously, with future research focusing on refining personalized discharge strategies and addressing disparities across diverse patient populations.