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.

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.

Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges. Given its overlap with leading comorbidities such as heart disease, innovative solutions are necessary to improve risk prediction and management strategies. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools in healthcare, offering novel approaches to chronic disease prevention. This narrative review explores the role of AI/ML in obesity risk prediction and management, with a special focus on childhood obesity. We begin by examining the multifactorial nature of obesity, including genetic, behavioral, and environmental factors, and the limitations of traditional approaches to predict and treat morbidity associated obesity. Next, we analyze AI/ML techniques commonly used to predict obesity risk, particularly in minimizing childhood obesity risk. We shift to the application of AI/ML in obesity management, comparing perspectives from healthcare providers versus patients. From the provider’s perspective, AI/ML tools offer real-time data from electronic medical records, wearables, and health apps to stratify patient risk, customize treatment plans, and enhance clinical decision making. From the patient’s perspective, AI/ML-driven interventions offer personalized coaching and improve long-term engagement in health management. Finally, we address key limitations and challenges, such as the role of social determinants of health, in embracing the role of AI/ML in obesity management, while offering our recommendations based on our literature review.

A medical student narrates transformative experiences in psychiatry and oncology rotations, emphasizing the importance of compassion and non-medical support in patient care. The encounters reshaped their perspective on effective, empathetic clinical practice.This story is based on real patient experiences; however, all identifying information has been removed or altered to ensure privacy and confidentiality.

Background/Objectives: Despite advances, large vessel occlusion strokes (LVO) remain associated with significant morbidity. Recent studies have suggested that blood transfusions may help manage critically ill LVO patients. We sought to evaluate the patient characteristics, complications, and clinical outcomes associated with blood transfusions in acute ischemic stroke (AIS) patients undergoing endovascular thrombectomy. Methods: A query of the 2016–2019 National Inpatient Sample was conducted to identify AIS patients who underwent endovascular thrombectomy, using International Classification of Disease 10th Revision diagnostic codes. Demographic, clinical characteristics, severity of presentation, complications, and outcomes were analyzed. Multivariate binary logistic regression was used to assess complications, length of stay (LOS), discharge disposition, and inpatient mortality. Results: A total of 47,835 AIS patients undergoing endovascular thrombectomy were identified. Of these patients, 1215 (2.5%) received blood transfusions. After controlling for age, gender, National Institutes of Health Stroke Scale scores, Elixhauser Comorbidity Index, and location of stroke, blood transfusions were significant positive predictors for higher rates of inpatient death (OR: 1.96; 95% CI: 1.681, 2.286; p < 0.001), lower rates of routine discharge (OR: 0.425; 95% CI: 0.342, 0.527; p < 0.001), and prolonged LOS (OR: 2.928; 95% CI: 2.572, 3.333; p < 0.001). Conclusions: Blood transfusions in AIS patients receiving endovascular thrombectomy are associated with elevated complication rates, extended hospital stays, and increased mortality, even after for controlling for predictors of poor outcome. Understanding the broader effects of blood transfusions in AIS patients is essential to ensure that the balance between potential benefits and risks upholds best care practice for all patients.