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.

BACKGROUND AND OBJECTIVES: Previous studies have demonstrated that current and/or past nicotine use disorder may worsen mortality and complications in patients experiencing subarachnoid hemorrhage (SAH). This retrospective study examines clinical outcomes in nontraumatic SAH patients with nicotine dependence. METHODS: The TriNetX Research Network was used to analyze data from adult patients (≥18 years old) with a history of nontraumatic SAH. Patients were further stratified by nicotine dependence (International Classification of Disease Tenth Revision Code F17) vs no nicotine dependence. Along with patient demographics and comorbidities, the primary outcomes that were examined included mortality, vasospasm, hydrocephalus, and delayed cerebral ischemia (DCI). RESULTS: A total of 209 970 patients diagnosed with nontraumatic SAH were included in the analysis. Of these, 43 315 patients had nicotine dependence (International Classification of Disease Tenth Revision Code F17). Patients with nicotine dependence were younger ( P < .0001) and more likely to be male ( P < .0001), White ( P < .0001), and non-Hispanic or Latino ( P < .0001). After SAH, patients with nicotine dependence had increased hazards of mortality (HR = 1.24; 95% CI: 1.20, 1.27), compared with patients with no nicotine dependence. Patients with nicotine dependence also had increased hazards of vasospasm (HR: 1.47; 95% CI: 1.40, 1.55) and DCI (HR: 1.35; 95% CI: 1.31, 1.39). CONCLUSION: SAH patients with nicotine dependence have increased risks of mortality, vasospasm, and DCI. Understanding the risk that nicotine dependence poses to patients who experience SAH underscores the importance of counseling for smoking cessation, which is vital for improving the long-term management of these patients.

Background/Objectives: Recent studies reveal an “obesity paradox”, suggesting better clinical outcomes after intracranial hemorrhage for obese patients compared to patients with a healthy BMI. While this paradox indicates improved survival rates for obese individuals in stroke cases, it is unknown whether this trend remains true across all forms of intracranial hemorrhage. Therefore, the objective of our study was to investigate the incidence, characteristics, and outcomes of hospitalized obese patients with intracranial hemorrhage. Methods: The National Inpatient Sample (NIS) database was queried for data from 2015 to 2019 to identify adult patients aged 18 years and older with a primary diagnosis of non-traumatic intracranial hemorrhage. Using International Classification of Disease 10th Edition codes, patients were stratified by BMI categories: healthy weight, overweight, class I–II obesity, and class III obesity. The cohorts were examined for demographic characteristics, comorbidities, stroke severity, inpatient complications, interventions, and clinical outcomes, including length of stay (LOS), discharge disposition, and inpatient mortality. Results: Of 41,960 intracranial hemorrhage patients identified, 13,380 (33.0%) also had an obese BMI. Class I–II obese intracranial hemorrhage patients were more likely to be of white race (OR: 1.101, 95% CI: 1.052, 1.152, p < 0.001), less likely to be female (OR: 0.773, 95% CI: 0.740, 0.808, p < 0.001), and more likely to have diabetes mellitus (OR: 1.545, 95% CI: 1.477, 1.616, p < 0.001) and hypertension (OR: 1.828, 95% CI: 1.721, 1.943, p < 0.001) in comparison to healthy-weight patients. In a matched cohort analysis adjusting for demographics and severity, intracranial hemorrhage patients with class I–II obesity had a shorter length of stay (LOS) (OR 0.402, 95% CI: 0.118, 0.705, p < 0.001), reduced inpatient mortality (OR 0.847, 95% CI: 0.798, 0.898, p < 0.001), and more favorable discharge disposition (OR 1.395, 95% CI: 1.321, 1.474, p < 0.001) compared to their healthy-weight counterparts. Furthermore, these patients were less likely to require decompressive hemicraniectomy (OR 0.697, 95% CI: 0.593, 0.820, p < 0.001). Following an analysis of individual ICH subtypes, obese subarachnoid hemorrhage (SAH) patients demonstrated reduced mortality (OR: 0.692, 95% CI: 0.577–0.831, p < 0.001) and LOS (OR: 0.070, 95% CI: 0.466–0.660, p = 0.039), with no differences in discharge disposition. Similarly, intracerebral hemorrhage patients demonstrated reduced mortality (OR: 0.891, 95% CI: 0.827–0.959, p = 0.002) and LOS (OR: 0.480, 95% CI: 0.216–0.743, p < 0.001). Other ICH subtypes showed improved discharge outcomes (OR: 1.504, 95% CI: 1.368–1.654, p < 0.001), along with decreased mortality (OR: 0.805, 95% CI: 0.715–0.907, p < 0.001) and LOS (OR: −10.313, 95% CI: −3.599 to −2.449, p < 0.001). Conclusions: Intracranial hemorrhage patients with class I–II obesity exhibited more favorable clinical outcomes than those who were of a healthy weight or overweight. Despite its association with risk factors contributing to intracranial hemorrhage, class I–II obesity was associated with improved outcomes, lending support to the existence of the obesity paradox in stroke.

Orexin (hypocretin) neuropeptides regulate numerous essential functions including sleep/wake state stability and reward processing. Orexin synthesizing neurons respond to drug cues and undergo structural changes following persistent drug exposure. Postmortem brains from opioid users and opioid-treated rodents have orexin somata that become ∼20% smaller and ∼50% more numerous and are postulated to promote hypermotivation for drug seeking though increased orexin release. Biophysical considerations suggest that the decreased soma size should increase cellular excitability; however, the impact of chronic opioids on firing ability, which drives peptide release, has not been explored. To test this, we assessed the intrinsic electrophysiological properties of orexin neurons by whole-cell recordings in slices from male orexin-EGFP mice treated for 2 weeks by daily morphine or saline injections. Paradoxically, we found that while daily morphine decreased the average soma size, it impaired excitability in a subpopulation of orexin neurons identified by electrophysiological criteria as “H-type” while entirely sparing “D-type” neurons. This impairment was manifest by smaller, broader action potentials, variable firing, and a downscaling of firing gain. These adaptations required more than a single morphine dose and recovered, along with soma size, after 4 weeks of passive withdrawal. Taken together, these observations indicate that daily opioid exposure differentially impacts H-type orexin neurons and predicts that the ability of these neurons to encode synaptic inputs into spike trains and to release neuropeptides becomes impaired in conjunction with opioid dependence.

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