Patient experiences of interprofessional collaboration and intersectoral communication in rare disease healthcare in Germany - a mixed-methods study.

BACKGROUND: Rare diseases are often complex, chronic and many of them life-shortening. In Germany, healthcare for rare diseases is organized in expert centers for rare diseases. Most patients additionally have regional general practicioners and specialists for basic medical care. Thus, collaboration and information exchange between sectors is highly relevant. Our study focuses on the patient and caregiver perspective on intersectoral and interdisciplinary care between local healthcare professionals (HCPs) and centers for rare diseases in Germany. The aims were (1) to investigate patients' and caregivers' general experience of healthcare, (2) to analyse patients' and caregivers' perception of collaboration and cooperation between local healthcare professionals and expert centers for rare diseases and (3) to investigate patients' and caregivers' satisfaction with healthcare in the expert centers for rare diseases. RESULTS: In total 299 individuals of whom 176 were patients and 123 were caregivers to pediatric patients participated in a survey using a questionnaire comprising several instruments and constructs. Fifty participants were additionally interviewed using a semistructured guideline. Most patients reported to receive written information about their care, have a contact person for medical issues and experienced interdisciplinary exchange within the centers for rare diseases. Patients and caregivers in our sample were mainly satisfied with the healthcare in the centers for rare diseases. The qualitative interviews showed a rather mixed picture including experiences of uncoordinated care, low engagement and communication difficulties between professionals of different sectors. Patients reported several factors that influenced the organization and quality of healthcare e.g. engagement and health literacy in patients or engagement of HCPs. CONCLUSIONS: Our findings indicate the high relevance of transferring affected patients to specialized care as fast as possible to provide best medical treatment and increase patient satisfaction. Intersectoral collaboration should exceed written information exchange and should unburden patients of being and feeling responsible for communication between sectors and specialists. Results indicate a lack of inclusion of psychosocial aspects in routine care, which suggests opportunities for necessary improvements.

Scoping review of the recommendations and guidance for improving the quality of rare disease registries.

BACKGROUND: Rare disease registries (RDRs) are valuable tools for improving clinical care and advancing research. However, they often vary qualitatively, structurally, and operationally in ways that can determine their potential utility as a source of evidence to support decision-making regarding the approval and funding of new treatments for rare diseases. OBJECTIVES: The goal of this research project was to review the literature on rare disease registries and identify best practices to improve the quality of RDRs. METHODS: In this scoping review, we searched MEDLINE and EMBASE as well as the websites of regulatory bodies and health technology assessment agencies from 2010 to April 2023 for literature offering guidance or recommendations to ensure, improve, or maintain quality RDRs. RESULTS: The search yielded 1,175 unique references, of which 64 met the inclusion criteria. The characteristics of RDRs deemed to be relevant to their quality align with three main domains and several sub-domains considered to be best practices for quality RDRs: (1) governance (registry purpose and description; governance structure; stakeholder engagement; sustainability; ethics/legal/privacy; data governance; documentation; and training and support); (2) data (standardized disease classification; common data elements; data dictionary; data collection; data quality and assurance; and data analysis and reporting); and (3) information technology (IT) infrastructure (physical and virtual infrastructure; and software infrastructure guided by FAIR principles (Findability; Accessibility; Interoperability; and Reusability). CONCLUSIONS: Although RDRs face numerous challenges due to their small and dispersed populations, RDRs can generate quality data to support healthcare decision-making through the use of standards and principles on strong governance, quality data practices, and IT infrastructure.

'High hopes for treatment': Australian stakeholder perspectives of the clinical translation of advanced neurotherapeutics for rare neurological diseases.

INTRODUCTION: Advanced therapies offer unprecedented opportunities for treating rare neurological disorders (RNDs) in children. However, health literacy, perceptions and understanding of novel therapies need elucidation across the RND community. This study explored healthcare professionals' and carers' perspectives of advanced therapies in childhood-onset RNDs. METHODS: In this mixed-methodology cross-sectional study, 20 healthcare professionals (clinicians, genetic counsellors and scientists) and 20 carers completed qualitative semistructured interviews and custom-designed surveys. Carers undertook validated psychosocial questionnaires. Thematic and quantitative data analysis followed. RESULTS: Participants described high positive interest in advanced therapies, but low knowledge of, and access to, reliable information. The substantial 'therapeutic gap' and 'therapeutic odyssey' common to RNDs were recognised in five key themes: (i) unmet need and urgency for access; (ii) seeking information; (iii) access, equity and sustainability; (iv) a multidisciplinary and integrated approach to care and support and (v) difficult decision-making. Participants were motivated to intensify RND clinical trial activity and access to advanced therapies; however, concerns around informed consent, first-in-human trials and clinical trial procedures were evident. There was high-risk tolerance despite substantial uncertainties and knowledge gaps. RNDs with high mortality, increased functional burdens and no alternative therapies were consistently prioritised for the development of advanced therapies. However, little consensus existed on prioritisation to treatment access. CONCLUSIONS: This study highlights the need to increase clinician and health system readiness for the clinical translation of advanced therapeutics for RNDs. Co-development and use of educational and psychosocial resources to support clinical decision-making, set therapeutic expectations and promotion of equitable, effective and safe delivery of advanced therapies are essential. PATIENT OR PUBLIC CONTRIBUTION: Participant insights into the psychosocial burden and information need to enhance the delivery of care in this formative study are informing ongoing partnerships with families, including co-production and dissemination of psychoeducational resources featuring their voices hosted on the Sydney Children's Hospitals Network website SCHN Brain-Aid Resources.

A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data.

To provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based strain data and differentiate between rare diseases like constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Patient population (retrospectively registered) included those presenting with heart failure due to CP (n = 51), RCM (n = 47), and patients without heart failure symptoms (n = 53). Longitudinal, radial, and circumferential strains/strain rates for left ventricular segments were processed into topological feature vectors using Machine learning PH workflow. In differentiating CP and RCM, the PH workflow model had a ROC AUC of 0.94 (Sensitivity = 92%, Specificity = 81%), compared with the GLS model AUC of 0.69 (Sensitivity = 65%, Specificity = 66%). In differentiating between all three conditions, the PH workflow model had an AUC of 0.83 (Sensitivity = 68%, Specificity = 84%), compared with the GLS model AUC of 0.68 (Sensitivity = 52% and Specificity = 76%). By employing persistent homology to differentiate the "pattern" of cardiac deformations, our machine-learning approach provides reasonable accuracy when evaluating small datasets and aids in understanding and visualizing patterns of cardiac imaging data in clinically challenging disease states.

Project SATURN- a real-world evidence data collaboration with existing European datasets in Osteogenesis Imperfecta to support future therapies.

Regulatory marketing authorisation is not enough to ensure patient access to new medicinal products. Health Technology Assessment bodies may require data on effectiveness, relative effectiveness, and cost-effectiveness. Healthcare systems may require data on clinical utility, savings, and budget impact. Furthermore, the exact requirements of these bodies vary country by country and sometimes even region to region, resulting in a patchwork of different data requirements to achieve effective, reimbursed patient access to new therapies. In addition, clinicians require data to make informed clinical management decisions. This requirement is of key importance in rare diseases where there is often limited data and clinical experience at the time of regulatory approval.This paper describes an innovative initiative that is called Project SATURN: Systematic Accumulation of Treatment practices and Utilization, Real world evidence, and Natural history data for the rare disease Osteogenesis Imperfecta. The objective of this project is to generate a common core dataset by utilising existing data sources to meet the needs of the various stakeholders and avoiding fragmentation through multiple approaches (e.g., a series of individual national requests/approaches, and unconnected with the regulators' potential requirements). It is expected that such an approach will reduce the time for patient access to life-changing medications. Whilst Project SATURN applies to Osteogenesis Imperfecta, it is anticipated that the principles could also be applied to other rare diseases and reduce the time for patient access to new medications.

An algorithm to identify patients aged 0-3 with rare genetic disorders.

BACKGROUND: With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. We sought to develop a digital phenotyping algorithm (PheIndex) using electronic medical records to identify children aged 0-3 diagnosed with genetic disorders or who present with illness with an increased risk for genetic disorders. RESULTS: Through expert opinion, we established 13 criteria for the algorithm and derived a score and a classification. The performance of each criterion and the classification were validated by chart review. PheIndex identified 1,088 children out of 93,154 live births who may be at an increased risk for genetic disorders. Chart review demonstrated that the algorithm achieved 90% sensitivity, 97% specificity, and 94% accuracy. CONCLUSIONS: The PheIndex algorithm can help identify when a rare genetic disorder may be present, alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist.

Identifying key health system components associated with improved outcomes to inform the re-configuration of services for adults with rare autoimmune rheumatic diseases: a mixed-methods study.

BACKGROUND: Adults with rare autoimmune rheumatic diseases face unique challenges and struggles to navigate health-care systems designed to manage common conditions. Evidence to inform an optimal service framework for their care is scarce. Using systemic vasculitis as an exemplar, we aimed to identify and explain the key service components underpinning effective care for rare diseases. METHODS: In this mixed-methods study, data were collected as part of a survey of vasculitis service providers across the UK and Ireland, interviews with patients, and from organisational case studies to identify key service components that enable good care. The association between these components and patient outcomes (eg, serious infections, mortality) and provider outcomes (eg, emergency hospital admissions) were examined in a population-based data linkage study using routine health-care data obtained from patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis from national health datasets in Scotland. We did univariable and multivariable analyses using Bayesian poisson and negative binomial regression to estimate incident rate ratios (IRRs), and Cox proportional hazards models to estimate hazard ratios (HRs). People with lived experiences were involved in the research and writing process. FINDINGS: Good care was characterised by service components that supported timely access to services, integrated care, and expertise. In 1420 patients with ANCA-associated vasculitis identified from national health datasets, service-reported average waiting times for new patients of less than 1 week were associated with fewer serious infections (IRR 0·70 [95% credibility interval 0·55-0·88]) and fewer emergency hospital admissions (0·78 [0·68-0·92]). Nurse-led advice lines were associated with fewer serious infections (0·76 [0·58-0·93]) and fewer emergency hospital admissions (0·85 [0·74-0·96]). Average waiting times for new patients of less than 1 week were also associated with reduced mortality (HR 0·59 [95% credibility interval 0·37-0·93]). Cohorted clinics, nurse-led clinics, and specialist vasculitis multi-disciplinary team meetings were associated with fewer serious infections (IRR 0·75 [0·59-0·96] for cohorted clinics; 0·65 [0·39-0·84] for nurse-led clinics; 0·72 [0·57-0·90] for specialist vasculitis multi-disciplinary team meetings) and emergency hospital admissions (0·81 [0·71-0·91]; 0·75 [0·65-0·94]; 0·86 [0·75-0·96]). Key components were characterised by their ability to overcome professional tensions between specialties. INTERPRETATION: Key service components associated with important health outcomes and underpinning factors were identified to inform initiatives to improve the design, delivery, and effectiveness of health-care models for rare autoimmune rheumatic diseases. FUNDING: Versus Arthritis.

Improving the care of children with GENetic Rare disease: Observational Cohort study (GenROC)-a study protocol.

INTRODUCTION: Around 2000 children are born in the UK per year with a neurodevelopmental genetic syndrome with significantly increased morbidity and mortality. Often little is known about expected growth and phenotypes in these children. Parents have responded by setting up social media groups to generate data themselves. Given the significant clinical evidence gaps, this research will attempt to identify growth patterns, developmental profiles and phenotypes, providing data on long-term medical and educational outcomes. This will guide clinicians when to investigate, monitor or treat symptoms and when to search for additional or alternative diagnoses. METHODS AND ANALYSIS: This is an observational, multicentre cohort study recruiting between March 2023 and February 2026. Children aged 6 months up to 16 years with a pathogenic or likely pathogenic variant in a specified gene will be eligible. Children will be identified through the National Health Service and via self-recruitment. Parents or carers will complete a questionnaire at baseline and again 1 year after recruitment. The named clinician (in most cases a clinical geneticist) will complete a clinical proforma which will provide data from their most recent clinical assessment. Qualitative interviews will be undertaken with a subset of parents partway through the study. Growth and developmental milestone curves will be generated through the DECIPHER website (https://deciphergenomics.org) where 5 or more children have the same genetic syndrome (at least 10 groups expected). ETHICS AND DISSEMINATION: The results will be presented at national and international conferences concerning the care of children with genetic syndromes. Results will also be submitted for peer review and publication.

Digenic Inheritance in Rare Disorders and Mitochondrial Disease-Crossing the Frontier to a More Comprehensive Understanding of Etiology.

Our understanding of rare disease genetics has been shaped by a monogenic disease model. While the traditional monogenic disease model has been successful in identifying numerous disease-associated genes and significantly enlarged our knowledge in the field of human genetics, it has limitations in explaining phenomena like phenotypic variability and reduced penetrance. Widening the perspective beyond Mendelian inheritance has the potential to enable a better understanding of disease complexity in rare disorders. Digenic inheritance is the simplest instance of a non-Mendelian disorder, characterized by the functional interplay of variants in two disease-contributing genes. Known digenic disease causes show a range of pathomechanisms underlying digenic interplay, including direct and indirect gene product interactions as well as epigenetic modifications. This review aims to systematically explore the background of digenic inheritance in rare disorders, the approaches and challenges when investigating digenic inheritance, and the current evidence for digenic inheritance in mitochondrial disorders.

IgG4-related disease-rare but you should not forget it.

Immunoglobulin G4-related disease is a systemic immune-mediated disease with insidious evolution characterized by fibroinflammatory lesions over virtually any organ system. Despite the remarkable progression of knowledge, its etiology remains undefined. Due to its relapse-remitting pattern, it could accumulate irreversible damage, increasing comorbidities and mortality. This paper emphasizes key concepts for diagnosing and treating patients with this condition.

Dementia in Rare Genetic Neurodevelopmental Disorders: A Systematic Literature Review.

BACKGROUND AND OBJECTIVES: Knowledge of young-onset Alzheimer disease in adults with Down syndrome has greatly improved clinical care. However, little is known about dementia in rare genetic neurodevelopmental disorders (RGNDs). In this review, a comprehensive overview is provided of reports on dementia and cognitive/adaptive trajectories in adults with RGNDs. METHODS: A systematic literature review was conducted in Embase, Medline ALL, and PsycINFO on December 6, 2022. The protocol was registered in PROSPERO (CRD42021223041). Search terms for dementia, cognitive and adaptive functioning, and RGNDs were combined using generic terms and the Orphanet database. Study characteristics and descriptive data on genetic diagnosis, clinical and neuropathologic features, comorbidities, and diagnostic methods were extracted using a modified version of the Cochrane Data Extraction Template. RESULTS: The literature search yielded 40 publications (17 cohorts, 23 case studies) describing dementia and/or cognitive or adaptive trajectories in adults with 14 different RGNDs. Dementia was reported in 49 individuals (5 cohorts, 20 cases) with a mean age at onset of 44.4 years. Diagnostics were not disclosed for half of the reported individuals (n = 25/49, 51.0%). A total of 44 different psychodiagnostic instruments were used. MRI was the most reported additional investigation (n = 12/49, 24.5%). Comorbid disorders most frequently associated with cognitive/adaptive decline were epilepsy, psychotic disorders, and movement disorders. DISCUSSION: Currently available literature shows limited information on aging in RGNDs, with relatively many reports of young-onset dementia. Longitudinal data may provide insights into converging neurodevelopmental degenerative pathways. We provide recommendations to optimize dementia screening, diagnosis, and research.

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

BACKGROUND: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. METHODS: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. RESULTS: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. CONCLUSIONS: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Between hope and reality: treatment of genetic diseases through nucleic acid-based drugs.

Rare diseases (RD) affect a small number of people compared to the general population and are mostly genetic in origin. The first clinical signs often appear at birth or in childhood, and patients endure high levels of pain and progressive loss of autonomy frequently associated with short life expectancy. Until recently, the low prevalence of RD and the gatekeeping delay in their diagnosis have long hampered research. The era of nucleic acid (NA)-based therapies has revolutionized the landscape of RD treatment and new hopes arise with the perspectives of disease-modifying drugs development as some NA-based therapies are now entering the clinical stage. Herein, we review NA-based drugs that were approved and are currently under investigation for the treatment of RD. We also discuss the recent structural improvements of NA-based therapeutics and delivery system, which overcome the main limitations in their market expansion and the current approaches that are developed to address the endosomal escape issue. We finally open the discussion on the ethical and societal issues that raise this new technology in terms of regulatory approval and sustainability of production.

Prediction of Individual Disease Progression Including Parameter Uncertainty in Rare Neurodegenerative Diseases: The Example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS).

The aim of this study was to develop a model to predict individual subject disease trajectories including parameter uncertainty and accounting for missing data in rare neurological diseases, showcased by the ultra-rare disease Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS). We modelled the change in SARA (Scale for Assessment and Rating of Ataxia) score versus Time Since Onset of symptoms using non-linear mixed effect models for a population of 173 patients with ARSACS included in the prospective real-world multicenter Autosomal Recessive Cerebellar Ataxia (ARCA) registry. We used the Multivariate Imputation Chained Equation (MICE) algorithm to impute missing covariates, and a covariate selection procedure with a pooled p-value to account for the multiply imputed data sets. We then investigated the impact of covariates and population parameter uncertainty on the prediction of the individual trajectories up to 5 years after their last visit. A four-parameter logistic function was selected. Men were estimated to have a 25% lower SARA score at disease onset and a moderately higher maximum SARA score, and time to progression (T50) was estimated to be 35% lower in patients with age of onset over 15 years. The population disease progression rate started slowly at 0.1 points per year peaking to a maximum of 0.8 points per year (at 36.8 years since onset of symptoms). The prediction intervals for SARA scores 5 years after the last visit were large (median 7.4 points, Q1-Q3: 6.4-8.5); their size was mostly driven by individual parameter uncertainty and individual disease progression rate at that time.

mRNA therapies: Pioneering a new era in rare genetic disease treatment.

Rare genetic diseases, often referred to as orphan diseases due to their low prevalence and limited treatment options, have long posed significant challenges to our medical system. In recent years, Messenger RNA (mRNA) therapy has emerged as a highly promising treatment approach for various diseases caused by genetic mutations. Chemically modified mRNA is introduced into cells using carriers like lipid-based nanoparticles (LNPs), producing functional proteins that compensate for genetic deficiencies. Given the advantages of precise dosing, biocompatibility, transient expression, and minimal risk of genomic integration, mRNA therapies can safely and effectively correct genetic defects in rare diseases and improve symptoms. Currently, dozens of mRNA drugs targeting rare diseases are undergoing clinical trials. This comprehensive review summarizes the progress of mRNA therapy in treating rare genetic diseases. It introduces the development, molecular design, and delivery systems of mRNA therapy, highlighting their research progress in rare genetic diseases based on protein replacement and gene editing. The review also summarizes research progress in various rare disease models and clinical trials. Additionally, it discusses the challenges and future prospects of mRNA therapy. Researchers are encouraged to join this field and collaborate to advance the clinical translation of mRNA therapy, bringing hope to patients with rare genetic diseases.

Issues, Challenges and Opportunities for Economic Evaluations of Orphan Drugs in Rare Diseases: An Umbrella Review.

BACKGROUND AND OBJECTIVES: There are significant challenges when obtaining clinical and economic evidence for health technology assessments of rare diseases. Many of them have been highlighted in previous systematic reviews but they have not been summarised in a comprehensive manner. For all stakeholders working with rare diseases, it is important to be aware and understand these issues. The objective of this review is to identify the main challenges for the economic evaluation of orphan drugs in rare diseases. METHODS: An umbrella review of systematic reviews of economic studies concerned with orphan and ultra-orphan drugs was conducted. Studies that were not systematic reviews, or on advanced therapeutic medicinal products, personalised medicines or other interventions that were not considered orphan drugs were excluded. The database searches included publications from 2010 to 2023, and were conducted in MEDLINE, EMBASE and the Cochrane library using filters for systematic reviews, and economic evaluations and models. These filters were combined with search terms for rare diseases and orphan drugs. A hand search supplemented the literature searches. The findings were reported by a compliant Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram. RESULTS: Two hundred and eighty-two records were identified from the literature searches, of which 64 were duplicates, whereas five reviews were identified from the hand search. A total of 36 reviews were included after screening against inclusion/exclusion criteria, 35 from literature searches and one from hand searching. Of those studies 1, 27 and 8 were low, moderate and high quality, respectively. The reviews highlight the scarcity of evidence for health economic parameters, for example, clinical effectiveness, costs, quality of life and the natural history of disease. Health economic evaluations such as cost-effectiveness and budget-impact analyses were scarce, and generally low-to-moderate quality. The causes were limited health economic parameters, together with publications bias, especially for cost-effectiveness analyses. CONCLUSIONS: The results highlighted issues around a considerable paucity of evidence for economic evaluations and few cost-effectiveness analyses, supporting the notion that a paucity of evidence makes economic evaluations of rare diseases more challenging compared with more prevalent diseases. Furthermore, we provide recommendations for more sustainable approaches in economic evaluations of rare diseases.

Innovations in Medicine: Exploring ChatGPT's Impact on Rare Disorder Management.

Artificial intelligence (AI) is rapidly transforming the field of medicine, announcing a new era of innovation and efficiency. Among AI programs designed for general use, ChatGPT holds a prominent position, using an innovative language model developed by OpenAI. Thanks to the use of deep learning techniques, ChatGPT stands out as an exceptionally viable tool, renowned for generating human-like responses to queries. Various medical specialties, including rheumatology, oncology, psychiatry, internal medicine, and ophthalmology, have been explored for ChatGPT integration, with pilot studies and trials revealing each field's potential benefits and challenges. However, the field of genetics and genetic counseling, as well as that of rare disorders, represents an area suitable for exploration, with its complex datasets and the need for personalized patient care. In this review, we synthesize the wide range of potential applications for ChatGPT in the medical field, highlighting its benefits and limitations. We pay special attention to rare and genetic disorders, aiming to shed light on the future roles of AI-driven chatbots in healthcare. Our goal is to pave the way for a healthcare system that is more knowledgeable, efficient, and centered around patient needs.