Search Results (363)

Artificial intelligence (AI) is now embedded across many aspects of healthcare, yet most implementations remain fragmented, task-specific, and layered onto legacy workflows. This paper does not review AI applications in healthcare per se; instead, it examines what an AI-first healthcare system would look like, one in which AI functions as a foundational organizing principle of care delivery rather than an adjunct technology. We synthesize evidence across ambulatory, inpatient, diagnostic, post-acute, and population health settings to assess where AI capabilities are sufficiently mature to support system-level integration and where critical gaps remain. Across domains, the literature demonstrates strong performance for narrowly defined tasks such as imaging interpretation, documentation support, predictive surveillance, and remote monitoring. However, evidence for longitudinal orchestration, cross-setting integration, and sustained impact on outcomes, costs, and equity remains limited. Key barriers include data fragmentation, workflow misalignment, algorithmic bias, insufficient governance, and lack of prospective, multi-site evaluations. We argue that advancing toward AI-first healthcare requires shifting evaluation from accuracy-centric metrics to system-level outcomes, emphasizing human-enabled AI, interoperability, continuous learning, and equity-aware design. Using hypertension management and patient journey exemplars, we illustrate how AI-first systems can enable proactive risk stratification, coordinated intervention, and continuous support across the care continuum. We further outline architectural and governance requirements, including cloud-enabled infrastructure, interoperability, operational machine learning practices, and accountability frameworks—necessary to operationalize AI-first care safely and at scale, subject to prospective validation, regulatory oversight, and post-deployment surveillance. This review contributes a system-level framework for understanding AI-first healthcare, identifies priority research and implementation gaps, and offers practical considerations for clinicians, health systems, researchers, and policymakers. By reframing AI as infrastructure rather than isolated tools, the AI-first approach provides a pathway toward more proactive, coordinated, and equitable healthcare delivery while preserving the central role of human judgment and trust. Full article

Prostate cancer remains the most frequently diagnosed malignancy in men and a leading cause of cancer-related mortality. Multiparametric MRI (mpMRI) has become the gold standard for non-invasive diagnosis, staging, and follow-up. Yet, its widespread adoption is hampered by long acquisition times, inter-reader variability, and interpretative complexity. Though most papers focus on specific applications without offering a cohesive therapeutic perspective, artificial intelligence (AI) has recently attracted attention as a potential solution to these shortcomings. For instance, deep learning models can help optimize imaging protocols for biparametric and multiparametric MRI, and AI-based reconstruction techniques have shown promise for reducing acquisition times without sacrificing diagnostic performance. Several systems have produced outcomes in the diagnostic phase that are comparable to those of skilled radiologists, as demonstrated in multicenter settings such as PI-CAI. Radiomics and radiogenomics provide more detailed insights into the biology of the disease by extracting quantitative features associated with tumor aggressiveness, extracapsular expansion, and treatment response, in addition to detection. Despite these developments, methodological variability, a lack of multicenter validation, proprietary algorithms, and unresolved standardization and governance difficulties continue to restrict clinical translation. Our work emphasizes the maturity of existing technologies, ongoing gaps, and the progressive integration necessary for successful clinical adoption by presenting AI applications aligned with the patient pathway. In this context, this review aims to outline how AI can support the entire patient journey—from acquisition and protocol selection to detection, quantitative analysis, treatment assessment, and follow-up—while maintaining a clinically centered perspective that emphasizes practical relevance over theoretical discussion, potentially enabling more reliable, effective, and customized patient care in the field of prostate cancer. Full article

Background and Clinical Significance: Anaplastic thyroid cancer (ATC) is a rare and aggressive malignancy with a poor prognosis, where median survival typically ranges from 4 to 10 months. Advances in genetic profiling, particularly the identification of BRAF mutations, offer new opportunities for targeted therapy. Case Presentation: This case report details the journey of a woman in her late 50s diagnosed with symptomatic ATC. Initial immunohistochemistry (IHC) testing for BRAF mutations returned negative results, leaving the patient with limited treatment options and prompting her to pursue medical assistance in dying (MAiD). However, next-generation sequencing (NGS) confirmed a ^V600EBRAF mutation, and a basis for targeted therapy. The patient began treatment with dabrafenib-trametinib, followed by pembrolizumab as second-line therapy, ultimately extending her life by nearly seven months. Conclusions: This case underscores the importance of rapid and comprehensive diagnostic approaches, particularly the higher sensitivity of NGS over IHC for detecting BRAF mutations. The complexities of accessing newer therapies in Canada’s single-payer healthcare system are also emphasized. The utilization of newer rapid diagnostic technologies can have a direct impact on directing treatment for ATC and other aggressive malignancies. Full article

Background: Mucopolysaccharidosis type IIIC (MPS IIIC) is a rare lysosomal storage disorder caused by pathogenic variants in the HGSNAT gene. Data from large patient cohorts remain scarce, particularly in Latin America. Methods: We retrospectively analyzed clinical, biochemical, and genetic data from patients diagnosed with MPS IIIC through the MPS Brazil Network. Diagnosis was based on reduced activity of acetyl-CoA:α-glucosaminide N-acetyltransferase (HGSNAT), elevated urinary glycosaminoglycans (uGAGs), and/or molecular genetics tests. Results: A total of 101 patients were confirmed with MPS IIIC, representing one of the largest cohorts worldwide. Females accounted for 60% of cases. The mean age at symptom onset was 5.4 ± 3.9 years, while the mean age at diagnosis was 11.7 ± 6.9 years, reflecting a 6-year diagnostic delay. Most patients initially presented with developmental delay (82%) and facial dysmorphism (80%), whereas behavioral manifestations were less frequently identified (25%), suggesting a milder phenotype than previously reported. Genetic information was available for 28% of patients, showing recurrent alleles (c.372-2A>G, c.252dupT) and several novel mutations, which expand the mutational spectrum of the disease. Genotype–phenotype similarities with Portuguese, Italian, and Chinese cases suggest shared ancestry contributions. Regional differences included earlier diagnoses in the North of Brazil and high consanguinity rates in the Northeast region. Conclusions: This study describes the largest Brazilian cohort of MPS IIIC, documenting novel variants and regional heterogeneity. Findings highlight diagnostic delays, ancestry influences, and the urgent need for disease-modifying therapies. Full article

Background: In response to the growing emotional support needs of patients in oncology, peer support was introduced into clinical teams in Quebec, Canada, in 2018. These peers, called accompanying patients (APs), are former cancer patients who use their experiential knowledge to provide support to patients during their oncology journey. This paper aims to identify APs’ perceptions of the program by including a diversity of perspectives. We include inexperienced and experienced APs, APs in different facilities, and APs in different cancer programs. Methods: We conducted a qualitative study based on 12 semi-structured interviews of APs between June and August 2024 in Quebec, Canada. We explored four themes, building on the Practice Change Model for qualitative analysis: APs’ sources of motivation, influences and environmental factors, resources available for AP integration, and the program’s effects. Results: 12 APs from 5 facilities participated in an interview. All the APs, both experienced and inexperienced, were highly motivated to participate in the program. Their motivations included a desire to give back to society, to help people and to give meaning to their illness. Both experienced and inexperienced APs were confident in their ability to accompany others. They were aware of their responsibilities and its limits regarding their role as an AP. They pointed out the program’s positive impact on their own emotional well-being and that of the patients. The program also benefited the clinical team, by limiting unhelpful demands from patients and saving time for clinicians. However, experienced APs did not feel well integrated into the healthcare team. Conclusions: We concluded that APs are highly motivated to be in the program. They perceived a need for the program in the current health system. They noted its beneficial effects on patients, on themselves, and on the clinical team. However, more resources need to be directed toward integrating APs into healthcare teams. Full article

The management of age-related macular degeneration (AMD) presents a significant challenge attributable to high disease heterogeneity. Patient realization of symptoms is poor and it is urgent to treat before permanent anatomic damage results in vision loss. This is true for the initial conversion from non-exudative intermediate AMD (iAMD) to exudative AMD (nAMD), and for the recurrence of nAMD undergoing treatment. Starting from the essential requirements that any practical solution needs to fulfill, we will reflect on how persistent navigation towards innovative solutions during a 25-year journey yielded significant advances towards improvements in personalized care. An early insight was that the acute nature of AMD progression requires frequent monitoring and therefore diagnostic testing should be performed at the patient’s home. Four key requirements were identified: (1) A tele-connected home device with acceptable diagnostic performance and a supportive patient user interface, both hardware and software. (2) Automated analytics capabilities that can process large volumes of data. (3) Efficient remote patient engagement and support through a digital healthcare provider. (4) A low-cost medical system that enables digital healthcare delivery through appropriate compensation for both the monitoring provider and the prescribing physician services. We reviewed the published literature accompanying first the development of Preferential Hyperacuity Perimetry (PHP) for monitoring iAMD, followed by Spectral Domain Optical Coherence Tomography (SD-OCT) for monitoring nAMD. Emphasis was given to the review of the validation of the core technologies, the regulatory process, and real-world studies, and how they led to the release of commercial services that are covered by Medicare in the USA. We concluded that while during the first quarter of the 21st century, the two main pillars of management of AMD were anti-VEGF intravitreal injections and in-office OCT, the addition of home-monitoring-based digital health services can become the third pillar. Full article

Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, with profound implications for congenital heart disease (CHD). Tetralogy of Fallot (ToF), the most common cyanotic disease, requires lifelong surveillance and complex management because of late complications such as pulmonary regurgitation, arrhythmias, and right ventricular dysfunction. This review synthesizes current evidence on AI applications across the continuum of ToF care—from prenatal diagnosis to adulthood follow-up. We examine advances in imaging, perioperative planning, intraoperative monitoring, intensive care, and long-term surveillance, including wearable and implantable technologies. Machine learning (ML), deep learning (DL), and natural language processing (NLP) are revolutionizing diagnostic accuracy, risk stratification, surgical decision-making, and personalized long-term care. The future lies in the integration of multimodal data, including imaging, electronic health records (EHRs), genomic information, and continuous monitoring, to support precision medicine. Challenges remain regarding dataset limitations, interpretability, regulatory standards, and ethical concerns. Nevertheless, ongoing innovation and collaboration between clinicians, engineers, and regulators promise a new era in congenital cardiology. By embedding AI throughout the patient journey, healthcare systems may improve outcomes and quality of life for individuals with ToF. Full article

Background: Interstitial lung diseases (ILDs) profoundly affect daily life, limiting mobility, independence, and emotional stability. While antifibrotic therapies may slow physiological decline, the living experience—characterized by breathlessness, cough, frailty, and psychological distress—remains insufficiently understood; this study therefore aimed to capture real-world patient perspectives on functional capacity, self-management, and mental health to identify treatable traits beyond conventional physiological measures. Materials and Methods: A cross-sectional quantitative online survey was conducted between September 2024 and January 2025 by Lungenfibrose e.V. in collaboration with the Center for Interstitial and Rare Lung Diseases (ZISL), Universities of Giessen and Marburg Lung Center (Giessen site). Patients with physician-confirmed ILD completed standardized instruments assessing dyspnea (MRC), cough intensity (VAS-Cough), frailty (CFS), and health-related quality of life (EQ-5D-5L). Data were analyzed descriptively across physical, functional, and psychosocial domains. Results: The majority of 69 respondents had idiopathic pulmonary fibrosis (64.7%) with a mean diagnostic delay of 1.4 ± 2.2 years; 69% were diagnosed within two years of symptom onset, and 77% were receiving antifibrotic therapy (nintedanib 57%, pirfenidone 19%). Functional limitations were substantial—55% were mobile for fewer than two hours per day, 73% reported mobility impairment, and oxygen use was common (51% during exertion, 26% at rest). Frailty increased over time (mean CFS 3.2 → 3.8), with 46% classified as fit, 36% vulnerable, and 18% frail. Dyspnea and cough remained burdensome (mean VAS-cough 40 ± 26; 58% moderate–severe), and health-related quality of life was reduced (mean EQ-VAS 56.5 ± 23.7), with high rates of anxiety/depression (78%), limitations in daily activities (76%), and pain/discomfort (74%). Despite overall satisfaction with care (mean 7.1 ± 2.5), respondents frequently reported unmet needs for psychological support and clearer communication about treatment and disease management. Conclusions: Despite antifibrotic therapy and structured specialist care, individuals living with ILD continue to face substantial physical and emotional challenges. Treatable traits—including frailty, dyspnea, inactivity, anxiety, and social isolation—emerge as key determinants of well-being. Multidisciplinary strategies integrating rehabilitation, psychosocial support, and patient education alongside pharmacological therapy are essential to preserve autonomy and improve quality of life in pulmonary fibrosis. Full article

The study aimed to understand how patients with liver cancer experience and adapt to the fear of cancer recurrence, providing insights into psychological processes and strategies that can inform psycho-oncology research and interventions. In-depth interviews were conducted with 13 patients with liver cancer from December 2019 to February 2020 and analyzed using Colaizzi’s phenomenological method. Four theme clusters emerged: (1) “Inevitable reality of recurrence,” which highlighted the acceptance of recurrence; (2) “Amplified fears,” which reflected heightened emotional distress; (3) “Changes in daily life driven by fear,” which illustrated lifestyle changes driven by uncertainty; and (4) “Living with fear,” which described adaptive strategies and resilience. The findings highlight the need for targeted psycho-oncological approaches to address the fear of cancer recurrence in patients with liver cancer, supporting the development of resilience and enhancing their overall quality of life. Further research is essential to design tailored strategies that reduce psychological distress and promote long-term survivorship. Full article

Background/Objectives: Patau syndrome (trisomy 13) is a rare genetic disorder with high mortality, and poor prognosis. Patients surviving beyond infancy usually present with severe psychomotor delays, failure to thrive, intellectual disabilities and seizures. Female sex and mosaic trisomy 13 are considered positive prognostic factors. Methods: Here we report an 8-year-old female patient with Patau syndrome, diagnosed prenatally, born prematurely at 35 + 4 weeks of gestation via vaginal delivery as a third child of 33 years old healthy and unrelated parents. The birth weight was 2087 g, Apgar scored 9 at 1 min and 10 at 5 min, also self-ventilating in room air since birth. The patient has several associated congenital abnormalities; however, medical adjustments such as multiple surgeries, PEG, hearing aids, glasses, anti-epileptic medications, and suction support the girl’s daily life. The patient attends a primary school with specialist support that fosters her physical and sensory development and promotes progress in communication. Despite the numerous obstacles she faces, the girl’s journey demonstrates remarkable growth and development with the support of an interdisciplinary care team. It highlights the critical role of personalized care and early intervention. Conclusions: Due to the increasing survival rates of patients with Patau syndrome, complex and multidisciplinary care is required for both the patients and their families to achieve the best possible outcomes and ensure proper care, growth, and development of the child. All medical procedures must be thoroughly assessed for potential complications and viable improvement in quality of life. Full article

Background/Objectives: Indeterminate colitis (IC) is an erroneous diagnosis for predominantly colonic inflammatory bowel disease (IBD) when there is a non-definitive foundation of the benchmark for ulcerative colitis (UC) and Crohn’s colitis (CC) after a combined state-of-the-art classification system of clinical, endoscopic, radiologic, and histologic tools are used. This confounds an effective surgical regimen; specifically pouch surgery, “the restorative proctocolectomy with ileal pouch–anal anastomosis (PRC-IPAA)”. Transforming the distinction between UC and CC in otherwise IC into authentic UC and CC requires priority attention when considering a patient’s candidacy for RPC-IPAA. RPC-IPAA is the accepted standard curative surgical procedure in the treatment for UC (and Familial Adenomatous Polyposis (FAP)). Further, inapproximate/incorrect diagnosis and treatment can sustain potential long-term morbidity from inaccurate and unnecessary surgery and cost. Methods: In trying to resolve these diagnostic ambiguities, the current study advances our understanding by showing the expression of human alpha defensin 5 (DEFA5 alias HD5) restricted in the colon crypt mucosal lining areas, and by identifying the cells of the small intestine (ileum) “colonic ileal metaplasia” in CC that may serve as a biomarker to portray/ascertain authentic CC and UC among IC cohorts, with a positive predictive value (PPV) of 96 percent. Results: Hence, the imprecise diagnosis of IC largely would be circumvented. This new diagnostic tool offers instant tangible benefits over existing diagnostic pathways. The journey toward its widespread clinical use is now subject to logistical and regulatory defiance, which all emerging molecular diagnostic technologies inevitably encounter. Conclusions: The aim of this communication is to provide a summary of the currently available diagnostic advances relating to surgical management for IC in clinical settings, and the related challenges. Further, I briefly discuss aspects of its pathophysiology, surveillance, and diagnostic assay development. Full article

Objectives: Alexander disease (AxD) is a rare neurodegenerative disorder that represents a group of leukodystrophies with severe disability and premature death, mostly with an infancy/childhood onset. In rare cases of late-onset phenotypes, symptoms are often milder and difficult to diagnose. We present a diagnostic journey of a teenage male patient with a progressive gait disorder starting at the age of 13 years, with a final diagnosis of Alexander disease. Early in the course of the disease, the boy exhibited distinctive cognitive involvement and neuropsychological deterioration characterized by selective impairment of visual and long-term auditory memory, along with a decline in IQ but preserved reasoning abilities. Methods: The patient underwent an extensive neurological diagnostic workup, which included magnetic resonance imaging (MRI) of the brain, spine, and abdomen, as well as electrophysiological, metabolic, and biochemical tests. Numerous specialist consultations were conducted, including genetic, cardiology, ophthalmology, pulmonology, oncohematology, psychological, and speech–language pathology consultations. In addition, a focused literature review was performed using PubMed, Scopus, Web of Science, and Google Scholar with the search terms “Alexander disease,” “GFAP gene,” “late-onset,” “spastic paraplegia” and “GFAP variant p/Gly18Val”. Results: Whole exome sequencing revealed an extremely rare missense GFAP heterozygous variant NM_002055.5: c.54G>T (p/Gly18Val), confirming the diagnosis of AxD. Conclusions: The presented case highlights the importance of whole-exome sequencing in the diagnosis of unexplained otherwise neurological symptoms, such as progressive spastic paraplegia. Full article

Protein engineering is a rapidly evolving field that plays a critical role in transforming drug discovery and development. This innovative field harnesses the unique structural and functional properties of engineered proteins, such as monoclonal antibodies, nanobodies, therapeutic enzymes, and cytokines, to address complex diseases more effectively than traditional small-molecule drugs. These biologics not only enhance therapeutic specificity but also minimize adverse effects, marking a significant advancement in patient care. However, the journey of protein engineering is not without challenges. Issues related to protein folding, stability, and potential immunogenicity pose significant complications. Additionally, navigating the complex regulatory landscape can delay the transition from laboratory to clinical application. Addressing these hurdles requires the integration of cutting-edge technologies, including phage and yeast display technology, CRISPR, and advanced computational modeling, which enhance the predictability and efficiency of protein design. In this review, we explore the multifaceted impact of protein engineering on modern medicine, highlighting its potential to transform treatment paradigms, methodologies, challenges, and the successful development and approval of recombinant protein-based therapies. By navigating the complexities and leveraging technological advancements, the field is poised to unlock new therapeutic possibilities, ultimately improving patient outcomes and transforming healthcare. Full article

Background/Objectives: Type 2 diabetes and obesity present escalating global health and economic challenges, highlighting the need for therapies that can effectively manage glycemic levels and reduce excess adiposity. Semaglutide, a glucagon-like peptide-1 receptor (GLP-1R) agonist available in subcutaneous or oral formulation, has quickly evolved from a theoretical concept to a crucial component of modern metabolic care. This review explores the comprehensive development journey of semaglutide, drawing on evidence from medicinal chemistry, animal studies, initial human trials, the pivotal SUSTAIN and STEP programs, and real-world post-marketing surveillance. Methods: We conducted a detailed analysis of preclinical data sets, Phase I–III clinical trials, regulatory documents, and pharmaco-epidemiological studies published between 2008 and 2025. Results: Through strategic molecular modifications, such as specific amino-acid substitutions and the addition of a C18 fatty-diacid side chain to enhance albumin binding, the half-life of the peptide was extended to approximately 160 h, allowing for weekly dosing. Studies in rodents and non-human primates showed that semaglutide effectively lowered blood glucose levels, reduced body weight, and preserved β-cells while maintaining a favorable safety profile. Phase I trials confirmed consistent pharmacokinetics and tolerability, while Phase II trials identified 0.5 mg and 1.0 mg once weekly as the most effective doses. The extensive SUSTAIN program validated significant reductions in HbA1c levels and weight loss compared to other treatments, as well as a 26% decrease in the relative risk of major adverse cardiovascular events (SUSTAIN-6). Subsequent STEP trials expanded the use of semaglutide to chronic weight management, revealing that nearly two-thirds of patients experienced a body weight reduction of at least 15%. Regulatory approvals from the FDA, EMA, and other regulatory agencies were obtained between 2017 and 2021, with ongoing research focusing on metabolic dysfunction-associated steatohepatitis, cardiovascular events, and chronic kidney disease. Conclusions: The trajectory of semaglutide exemplifies how intentional peptide design, iterative translational research, and outcome-driven clinical trial design can lead to groundbreaking therapies for complex metabolic disorders. Full article

Transesophageal echocardiography (TEE) has emerged as the pivotal imaging modality in transcatheter mitral valve replacement (TMVR), bridging the gap between anatomical complexity and procedural precision. Unlike any other tool, TEE accompanies the patient journey across all stages of TMVR, from patient assessment and device selection to intraprocedural guidance and post-implant surveillance, providing real-time insights into valve anatomy, hemodynamics, and complications. This review consolidates the most recent consensus statements, quantitative thresholds, and device-specific considerations, while also highlighting evolving technologies. By outlining best practices for integrating TEE within multimodality workflows and the Heart Team paradigm, this article offers clinicians a comprehensive and practice-oriented roadmap for optimizing TMVR outcomes. Full article