Severe obesity (body mass index ≥ 40 kg/m2 or ≥ 35 kg/m2 with obesity-related comorbidities) is increasingly prevalent and independently associated with elevated perioperative morbidity and inferior oncologic outcomes in patients with colorectal cancer (CRC). Despite these risks, intentional preoperative weight optimization is not routinely incorporated into CRC management, owing to concerns regarding treatment delay, absence of guideline endorsement, and limited supporting evidence. A literature review was conducted using PubMed and Embase to evaluate the impact of severe obesity on morbidity, mortality, and oncologic outcomes in CRC. Peer-reviewed English-language studies involving adult human subjects were included, while conference abstracts, non-English publications, and studies unrelated to obesity and CRC were excluded. In the absence of published reports describing synchronized weight loss and CRC management in patients with severe obesity, three novel retrospective case examples were included to demonstrate feasibility during neoadjuvant treatment, with institutional review board approval obtained for all cases. Severe obesity complicates CRC staging due to limitations in cross-sectional imaging and anatomic delineation. Furthermore, severe and particularly visceral obesity is associated with increased rates of anastomotic leak, surgical site infection, and conversion to open surgery. Current CRC guidelines do not incorporate structured weight-loss strategies into standard treatment algorithms. Metabolic bariatric procedures, such as sleeve gastrectomy, achieve rapid and clinically meaningful weight reduction, often resulting in improved operative exposure and technical conditions for subsequent resection. Pharmacologic therapies, while more broadly accessible and less invasive, typically yield more modest reductions in visceral adiposity. Task force members report early experience across three distinct cases of locally advanced CRC in patients with severe obesity, demonstrating successful preoperative visceral fat reduction through multidisciplinary coordination incorporating metabolic bariatric surgery or pharmacologic therapy during neoadjuvant windows, followed by definitive oncologic resection. Severe obesity adversely influences CRC staging, operative complexity, and perioperative outcomes. Intentional metabolic optimization—through bariatric surgery or pharmacologic therapy—may represent a viable adjunct within multidisciplinary, patient-centered CRC care pathways. However, the absence of prospective short- and long-term outcome data underscores the need for systematic investigation to define optimal timing, safety parameters, and oncologic efficacy of weight-loss interventions in this high-risk population.
Generative AI (GenAI) has shifted AI capabilities from discriminative prediction to creative interaction, offering opportunities to augment productivity and innovation. However, realizing these benefits requires navigating risks where development outpaces governance. This article revisits the Six Human-Centered AI (HCAI) Grand Challenges to analyze their relevance in the generative era. Critical new requirements are identified: preserving human autonomy, ensuring operational safety against non-deterministic outputs, and navigating complex intellectual property landscapes. These findings are synthesized into an updated, actionable research agenda for each challenge, serving as a call to action to operationalize these principles. By shifting focus from risk mitigation to human empowerment, this agenda establishes human-centeredness as the organizing principle for a future where GenAI enhances human agency, dignity, and collective flourishing.
Sexual abuse and assault are a major public health problem with high prevalence rates and potentially negative mental health consequences. Men and masculine-identifying individuals who are members of the sexual and gender minority (SGM) community are at high risk of being sexually assaulted and face with unique barriers to seeking and engaging in mental health treatment such as structural stigma, minority stress, and mistrust of services. At the conclusion of a randomized trial of a peer-led online mental health treatment, qualitative interviews were conducted with 101 SGM male survivors about their past mental health treatment experiences and preferences for psychotherapy and pharmacotherapy. The vast majority reported that they had previously engaged briefly in formal mental health treatment, though most explained that they had never discussed trauma or related issues. Concerns regarding side effects of medication were prevalent. Barriers to psychotherapy engagement included perceived experiences of discrimination, difficulty accessing care (i.e., unsure how to find an SGM-affirmative provider, insurance, and financial cost), or perceived poor fit with the therapy or therapist. Most expressed willingness to seek treatment in the future, particularly individual psychotherapy with a licensed mental health professional. Understanding past mental health treatment experiences and preferences of this marginalized population can inform outreach as well as clinical services.
Patients with obstructive sleep apnea (OSA) frequently seek information online, yet the comparative quality of content delivered by web search engines versus generative AI systems is unclear. This study evaluated how different digital information sources perform in answering common patient questions about OSA. Thirty high-volume, patient-facing OSA questions were identified using Google Trends. Each question was submitted verbatim to four general-purpose large language models (GPT-4, GPT-5, DeepSeek, Mistral), a medically specialized retrieval-augmented model (OpenEvidence), and Google Search. Seven otolaryngologists with clinical experience in OSA independently rated each response for accuracy, clarity, completeness, relevance, and usefulness using a five-point rubric. Composite and domain scores were analyzed using one-way analysis of variance with multiple-comparison correction; inter-rater reliability was assessed with two-way random-effects intraclass correlation coefficients. A total of 180 question–system pairs received 6295 domain-level ratings. OpenEvidence achieved the highest mean composite score (4.33), followed by a tightly clustered group of LLMs (means 4.00–4.04). Google Search scored significantly lower (3.15). Differences among systems were statistically significant across all domains (p < 0.001), with large effect sizes for comparisons of OpenEvidence and general LLMs versus Google. Composite average-rater reliability was good (ICC = 0.70). For common OSA questions, generative AI systems—particularly a retrieval-augmented medical model—produced higher-quality patient-facing information than standard web search. These findings support cautious consideration of GenAI tools to supplement patient education in OSA, while underscoring the need for ongoing evaluation across diseases, disciplines, and patient populations. Patients with obstructive sleep apnea (OSA) frequently rely on online sources such as Google Search to understand symptoms, testing, and treatment, yet the quality of patient-facing information varies widely. As generative artificial intelligence tools are increasingly used for health questions, their comparative performance for OSA education has not been systematically evaluated using blinded expert review. In this blinded comparative study, generative AI systems, particularly a retrieval-augmented medical model, provided more accurate, clear, complete, and useful answers to common OSA questions than standard web search. These findings highlight that the choice of digital information source can meaningfully influence the quality of patient education in sleep medicine and support further evaluation of AI tools within clinical practice.
Laser-Induced Thermotherapy (LITT) has emerged as a promising minimally invasive treatment for localized breast tumors, offering targeted thermal ablation with minimal impact on surrounding tissues. This research uses an extended bioheat transfer model for simulation on clinically motivated LITT scenarios in MATLAB. We created a 3D breast tissue model with an embedded tumor. This model simulates realistic laser fiber placement and irradiation. Time-dependent laser power, time-dependent optical absorption, timedependent adaptive blood perfusion and spatial distribution of nanoparticles representing any of the key physiological and treatment-dependent parameters are systematically varied to simulate potential clinical conditions. We added these non-linear thermal effects to the Pennes bioheat equation and the resulting equation of the Pennes bioheat is then numerically solved utilizing a finite difference scheme. The tissue viability is determined using the Arrhenius damage model which makes it possible to dynamically follow the evolution of necrotic volume. They are simulated under various conditions of treatment such as different tumor sizes, different durations of laser and orientation of fibers to see their effect on thermal damage profile and effectiveness of ablation. Results show that temperature-dependent optical properties and perfusion feedback greatly impact on the heat penetration and localization of damage. The enhancement of absorption using nanoparticles leads to the significant increase in the area of necrosis with the simultaneous decrease in energy demand. This parametric study helps to improve LITT treatment planning. It provides a virtual platform for testing when clinical data is scarce and is a kind of a virtual platform to test it in case of lacking experimental or clinical information. The study helps in the preparation of more personalized and safer lasers to be used in the treatment of breast cancer.
