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Eating disorders (EDs) are multisystemic, debilitating, and complex illnesses that affect many young Canadians. These disorders are associated with high rates of medical complications, psychiatric and physical comorbidities, functional impairment, family distress, and financial burden. Despite the severity and increasing prevalence of EDs in youth, advancements in understandings of the pathophysiology and treatment of EDs have remained limited over the past three decades. This trend may be shaped by the chronic underfunding of the field, reliance on small sampled cross-sectional studies, and the notable lack of research focused on youth with EDs from historically underrepresented communities. Current treatment practices demonstrate modest efficacy and often omit the complex, heterogeneous presentations, development, and maintenance of pediatric EDs. Large-scale, multiaxial datasets are necessary to elucidate ED etiology and enable phenotyping. This is a critical step towards implementing future precision psychiatry and personalized treatment advances. In this commentary, we share our experience of conceptualizing a precision ED data and bio-registry, EDBioMAP: Eating Disorder Bio-Registry and Multiaxial Precision Health Platform, and suggest necessary pillars to inform, implement, and drive the successful use of precision psychiatry in pediatric ED care. Effective data utilization requires actionable steps and includes: (1) establishing strategic partnerships; (2) incorporating measurement-based care into clinical practice; (3) collecting novel biological markers; (4) developing minimum datasets; and (5) leveraging predictive modelling techniques. Strategic and standardized data integration is imperative to informing the future use of precision psychiatry for EDs. It can lend well to igniting multi-site collaboration to enhance large datasets necessary for this type of work and offers avenues for future development of personalized treatment interventions and clinical decision-making tools for youth with EDs.

For over a decade, the mental health field has been interested in precision treatment using psychopharmacological interventions. More recently, this interest has expanded to include psychotherapy, which is the primary treatment modality for eating disorders. Personalised medicine and precision treatment are also seen as priorities for the eating disorder field by those with lived experience and carers, clinicians and researchers. However, precision treatment necessitates the collection of large amounts of clinical data. Three frameworks exist or have been proposed for the purpose of gathering large-scale routine clinical outcomes in eating disorder services: The International Consortium for Health Outcomes Measurement (ICHOM) eating disorder set, the Australia national minimum dataset, and the Eating Disorders Clinical Research Network. Despite the emergence of these frameworks, challenges exist with implementation. This paper outlines the rationale for the collection of routine outcome data in eating disorder treatment settings, the three existing frameworks proposed, and considerations for implementation and scaling. These include clinical and practice applications, technical aspects, statistics, and contextual factors. We invite attention to our recommendations and collaborative approaches to facilitate progress towards precision treatment in eating disorders. Plain English summary Precision treatment, also known as precision medicine, involves tailoring treatment to the individual characteristics of each patient. Precision treatment for eating disorders is seen as a priority by individuals with lived experience, their carers, clinicians and researchers. However, precision treatment depends on large amounts of clinical data being collected. Currently, eating disorder services do not collect the same information from or about patients. There is no large clinical database to inform precision medicine decisions. Three main frameworks have been proposed to support largescale and consistent data collection in eating disorder services: The International Consortium for Health Outcomes Measurement (ICHOM) eating disorder set, the Australian national minimum dataset, and the UK Eating Disorders Clinical Research Network. These frameworks hold promise but there are challenges with applying them. This paper summarises why collecting routine outcome data is important, the three main frameworks proposed, and the factors which may help to progress data collection and precision treatment for eating disorders. We consider clinical, practical, technical, statistical and contextual factors. It is important that progress in this area is collaborative and involves individuals with lived experience, carers, clinicians and researchers.

Cancer is one of the most common families of diseases today with millions of new patients every year around the world. Bladder cancer (BC) is one of the most prevalent types of cancer affecting both genders, and it is not known to be associated with a specific group in the population. The current treatment standard for BC follows a standard weekly Bacillus Calmette–Guérin (BCG) immunotherapy-based therapy protocol which includes BCG and IL-2 injections. Unfortunately, due to the biological and clinical complexity of the interactions between the immune system, treatment, and cancer cells, clinical outcomes vary significantly among patients. Unfortunately, existing models are commonly developed for a non-existing average patient or pose strict, unrealistic, expectations on the treatment process. In this work, we propose the most extensive ordinary differential equation-based biological model of BCG treatment to date and a deep learning-based scheduling approach to obtain a personalized treatment schedule. Our results show that resulting treatment schedules favorably compare with the current standard practices and the current state-of-the-art scheduling approach.

Tom M Hillary Department of Dermatology, University Hospital Leuven, Leuven, BelgiumCorrespondence: Tom M Hillary, Department of Dermatology, University Hospital Leuven, Herestraat 49, Leuven, Vlaams-Brabant, 3000, Belgium, Tel +3216337950, Email tom.hillary@uzleuven.beBackground: Biologic therapies targeting IL‑17 and IL‑23 have revolutionized psoriasis management, enabling rapid and durable disease control. Yet treatment selection still follows a trial‑and‑error approach, and clinically validated biomarkers for personalization remain absent.Objective: To outline current challenges in biomarker development for psoriasis and describe the design and aims of the PICASSO prospective cohort as a platform for future personalized medicine.Current Challenges: Despite evidence that IL‑17/IL‑23 inhibitors may induce disease modification through effects on effector, memory, and regulatory immune cells, reliable predictive or prognostic biomarkers have not emerged. Barriers include complex pathogenesis, universally high biologic efficacy reducing need for stratification, inconsistent findings from genetic or transcriptomic studies, and the multifactorial nature of comorbidities.Methods (Picasso (ProspectIve Cohort psoriASiS FOllow-Up)): PICASSO is a 10‑year prospective biobank/registry enrolling patients within three years of disease onset. Biological samples are longitudinally linked to clinical and epidemiological data, with follow‑ups every 2.5 years. The ultimate aim is to identify biomarkers predicting disease trajectory.Conclusion: Personalized psoriasis care requires biomarkers predicting progression and comorbidity risk. PICASSO represents a step toward disease‑modifying, preventive precision medicine.Keywords: personalized treatment, research, dermatology, disease modification

Plain english summary Traditional eating disorder (ED) treatment approaches often use a “one-size-fits-all” method, despite the fact EDs are complex and can vary greatly from person to person. This review discusses how personalised treatment can transform care for people with EDs. Personalised care tailors treatment to each person’s unique biology, mental health, and life circumstances, with the understanding that a more flexible and individualised approach could lead to better outcomes. We explore new discoveries in genetic research, machine learning, and advanced tracking methods to predict how someone might respond to specific treatments and identify what works best for them. We also emphasise the importance of addressing changes in the illness experience over time and including patients’ perspectives in their care. While these approaches show great promise, challenges remain, such as ensuring we have evidence to guide effective personalisation, and that treatments are ethical, widely available and easy for clinicians to use. The paper highlights a future where ED treatments are more precise, effective, and adapted to the individual, offering new hope for recovery. Eating disorders (EDs) are complex and heterogeneous conditions, which are often not resolved with conventional, manualised treatments. Arguments for the development of holistic, person-centred treatments accounting for individual variability have been mounting amongst researchers, clinicians and people with lived experience alike. This review explores the transformative potential of personalised medicine in ED care, emphasising the integration of precision diagnostics and tailored interventions based on individual genetic, biological, psychological and environmental profiles. Building on advancements in genomics, neurobiology, and computational technologies, it advocates for a shift from categorical diagnostic frameworks to symptom-based and dimensional approaches. The paper summarises emerging evidence supporting precision psychiatry, including the development of biomarkers, patient-reported outcomes, predictive modelling, and staging models, and discusses their application in ED research and clinical care. It highlights the utility of machine learning and idiographic statistical methods in optimising therapeutic outcomes and identifies key challenges, such as ethical considerations, scalability and implementation.

Aberrant metabolism is the root cause of several serious health issues, creating a huge burden to health and leading to diminished life expectancy. A dysregulated metabolism induces the secretion of several molecules which in turn trigger the inflammatory pathway. Inflammation is the natural reaction of the immune system to a variety of stimuli, such as pathogens, damaged cells, and harmful substances. Metabolically triggered inflammation, also called metaflammation or low-grade chronic inflammation, is the consequence of a synergic interaction between the host and the exposome—a combination of environmental drivers, including diet, lifestyle, pollutants and other factors throughout the life span of an individual. Various levels of chronic inflammation are associated with several lifestyle-related diseases such as diabetes, obesity, metabolic associated fatty liver disease (MAFLD), cancers, cardiovascular disorders (CVDs), autoimmune diseases, and chronic lung diseases. Chronic diseases are a growing concern worldwide, placing a heavy burden on individuals, families, governments, and health-care systems. New strategies are needed to empower communities worldwide to prevent and treat these diseases. Precision medicine provides a model for the next generation of lifestyle modification. This will capitalize on the dynamic interaction between an individual’s biology, lifestyle, behavior, and environment. The aim of precision medicine is to design and improve diagnosis, therapeutics and prognostication through the use of large complex datasets that incorporate individual gene, function, and environmental variations. The implementation of high-performance computing (HPC) and artificial intelligence (AI) can predict risks with greater accuracy based on available multidimensional clinical and biological datasets. AI-powered precision medicine provides clinicians with an opportunity to specifically tailor early interventions to each individual. In this article, we discuss the strengths and limitations of existing and evolving recent, data-driven technologies, such as AI, in preventing, treating and reversing lifestyle-related diseases.

Futurists have anticipated that novel autonomous technologies, embedded with machine learning (ML), will substantially influence healthcare. ML is focused on making predictions as accurate as possible, while traditional statistical models are aimed at inferring relationships between variables. The benefits of ML comprise flexibility and scalability compared with conventional statistical approaches, which makes it deployable for several tasks, such as diagnosis and classification, and survival predictions. However, much of ML-based analysis remains scattered, lacking a cohesive structure. There is a need to evaluate and compare the performance of well-developed conventional statistical methods and ML on patient outcomes, such as survival, response to treatment, and patient-reported outcomes (PROs). In this article, we compare the usefulness and limitations of traditional statistical methods and ML, when applied to the medical field. Traditional statistical methods seem to be more useful when the number of cases largely exceeds the number of variables under study and a priori knowledge on the topic under study is substantial such as in public health. ML could be more suited in highly innovative fields with a huge bulk of data, such as omics, radiodiagnostics, drug development, and personalized treatment. Integration of the two approaches should be preferred over a unidirectional choice of either approach.

Background Precision medicine, targeting treatments to individual genetic and clinical profiles, faces challenges in data collection, costs, and privacy. Generative AI offers a promising solution by creating realistic, privacy-preserving patient data, potentially revolutionizing patient-centric healthcare. Objective This review examines the role of deep generative models (DGMs) in clinical informatics, medical imaging, bioinformatics, and early diagnostics, showcasing their impact on precision medicine. Methods Adhering to PRISMA guidelines, the review analyzes studies from databases such as Scopus and PubMed, focusing on AI's impact in precision medicine and DGMs' applications in synthetic data generation. Results DGMs, particularly Generative Adversarial Networks (GANs), have improved synthetic data generation, enhancing accuracy and privacy. However, limitations exist, especially in the accuracy of foundation models like Large Language Models (LLMs) in digital diagnostics. Conclusion Overcoming data scarcity and ensuring realistic, privacy-safe synthetic data generation are crucial for advancing personalized medicine. Further development of LLMs is essential for improving diagnostic precision. The application of generative AI in personalized medicine is emerging, highlighting the need for more interdisciplinary research to advance this field.

Introduction Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI’s role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. Research Significance This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI’s significance in healthcare and supports healthcare organizations in effectively adopting AI technologies. Materials and Methods The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. Results Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. Conclusion AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

Background Tumor Boards (TBs) are Multidisciplinary Team (MDT) meetings in which different specialists work together closely sharing clinical decisions in cancer care. The composition is variable, depending on the type of tumor discussed. As an organizational tool, MDTs are thought to optimize patient outcomes and to improve care performance. The aim of the study was to perform an umbrella review summarizing the available evidence on the impact of TBs on healthcare outcomes and processes. Methods Pubmed and Web of Science databases were investigated along with a search through citations. The only study design included was systematic review. Only reviews published after 1997 concerning TBs and performed in hospital settings were considered. Two researchers synthetized the studies and assessed their quality through the AMSTAR2 tool. Results Five systematic reviews published between 2008 and 2017 were retrieved. One review was focused on gastrointestinal cancers and included 16 studies; another one was centered on lung cancer and included 16 studies; the remaining three studies considered a wide range of tumors and included 27, 37 and 51 studies each. The main characteristics about format and members and the definition of TBs were collected. The decisions taken during TBs led to changes in diagnosis (probability to receive a more accurate assessment and staging), treatment (usually more appropriate) and survival (not unanimous improvement shown). Other outcomes less highlighted were quality of life, satisfaction and waiting times. Conclusions The study showed that the multidisciplinary approach is the best way to deliver the complex care needed by cancer patients; however, it is a challenge that requires organizational and cultural changes and must be led by competent health managers who can improve teamwork within their organizations. Further studies are needed to reinforce existing literature concerning health outcomes. Evidence on the impact of TBs on clinical practices is still lacking for many aspects of cancer care. Further studies should aim to evaluate the impact on survival rates, quality of life and patient satisfaction. Regular studies should be carried out and new process indicators should be defined to assess the impact and the performance of TBs more consistently.