Artificial intelligence in rare disease diagnosis and treatment

Applying artificial intelligence to rare diseases: a literature review highlighting lessons from Fabry disease

BACKGROUND

Use of artificial intelligence (AI) in rare diseases has grown rapidly in recent years. In this review we have outlined the most common machine-learning and deep-learning methods currently being used to classify and analyse large amounts of data, such as standardized images or specific text in electronic health records. To illustrate how these methods have been adapted or developed for use with rare diseases, we have focused on Fabry disease, an X-linked genetic disorder caused by lysosomal α-galactosidase. A deficiency that can result in multiple organ damage.

METHODS

We searched PubMed for articles focusing on AI, rare diseases, and Fabry disease published anytime up to 08 January 2025. Further searches, limited to articles published between 01 January 2021 and 31 December 2023, were also performed using double combinations of keywords related to AI and each organ affected in Fabry disease, and AI and rare diseases.

RESULTS

In total, 20 articles on AI and Fabry disease were included. In the rare disease field, AI methods may be applied prospectively to large populations to identify specific patients, or retrospectively to large data sets to diagnose a previously overlooked rare disease. Different AI methods may facilitate Fabry disease diagnosis, help monitor progression in affected organs, and potentially contribute to personalized therapy development. The implementation of AI methods in general healthcare and medical imaging centres may help raise awareness of rare diseases and prompt general practitioners to consider these conditions earlier in the diagnostic pathway, while chatbots and telemedicine may accelerate patient referral to rare disease experts. The use of AI technologies in healthcare may generate specific ethical risks, prompting new AI regulatory frameworks aimed at addressing these issues to be established in Europe and the United States.

CONCLUSION

AI-based methods will lead to substantial improvements in the diagnosis and management of rare diseases. The need for a human guarantee of AI is a key issue in pursuing innovation while ensuring that human involvement remains at the centre of patient care during this technological revolution.

Fetal origins of adult disease: transforming prenatal care by integrating Barker's Hypothesis with AI-driven 4D ultrasound

INTRODUCTION

The fetal origins of adult disease, widely known as Barker's Hypothesis, suggest that adverse fetal environments significantly impact the risk of developing chronic diseases, such as diabetes and cardiovascular conditions, in adulthood. Recent advancements in 4D ultrasound (4D US) and artificial intelligence (AI) technologies offer a promising avenue for improving prenatal diagnostics and validating this hypothesis. These innovations provide detailed insights into fetal behavior and neurodevelopment, linking early developmental markers to long-term health outcomes.

CONTENT

This study synthesizes contemporary developments in AI-enhanced 4D US, focusing on their roles in detecting fetal anomalies, assessing neurodevelopmental markers, and evaluating congenital heart defects. The integration of AI with 4D US allows for real-time, high-resolution visualization of fetal anatomy and behavior, surpassing the diagnostic precision of traditional methods. Despite these advancements, challenges such as algorithmic bias, data diversity, and real-world validation persist and require further exploration.

SUMMARY

Findings demonstrate that AI-driven 4D US improves diagnostic sensitivity and accuracy, enabling earlier detection of fetal abnormalities and optimization of clinical workflows. By providing a more comprehensive understanding of fetal programming, these technologies substantiate the links between early-life conditions and adult health outcomes, as proposed by Barker's Hypothesis.

OUTLOOK

The integration of AI and 4D US has the potential to revolutionize prenatal care, paving the way for personalized maternal-fetal healthcare. Future research should focus on addressing current limitations, including ethical concerns and accessibility challenges, to promote equitable implementation. Such advancements could significantly reduce the global burden of chronic diseases and foster healthier generations.

Pharmacogenomics and rare diseases: optimizing drug development and personalized therapeutics

Pharmacogenomics (PGx) is an evolving field that integrates genetic information into clinical decision-making to optimize drug therapy and minimize adverse drug reactions (ADRs). Its application in rare disease (RD) drug development is promising, given the genetic basis of many RDs and the need for precision medicine approaches. Despite significant advancements, challenges persist in developing effective therapies for RDs due to small patient populations, genetic heterogeneity, and limited surrogate biomarkers. The Orphan Drug Act in the U.S. has incentivized RD drug development. However, the traditional drug approval process is constrained by logistical and economic challenges, necessitating innovative PGx-driven strategies. Identifying genetic biomarkers in the early drug development stages can optimize dose selection, enhance therapeutic efficacy, and reduce ADRs. Case studies such as eliglustat for Gaucher disease and ivacaftor for cystic fibrosis demonstrate the efficacy of PGx-guided treatment strategies. Integrating PGx into global drug development requires the harmonization of regulatory policies and increased diversity in genetic research. Artificial intelligence (AI) tools further enhance genetic analysis, disease prediction, and clinical decision-making. Modernizing drug labeling with PGx information is critical to ensuring safe and effective drug use. Collectively, PGx offers transformative potential in RD therapeutics by facilitating personalized medicine approaches and addressing unmet medical needs.

Comparative analysis of large language models on rare disease identification

Diagnosing rare diseases is challenging due to their low prevalence, diverse presentations, and limited recognition, often leading to diagnostic delays and errors. This study evaluates the effectiveness of multiple large language models (LLMs) in identifying rare diseases, comparing their performance with that of human physicians using real clinical cases. We analyzed 152 rare disease cases from the Chinese Medical Case Repository using four LLMs: ChatGPT-4o, Claude 3.5 Sonnet, Gemini Advanced, and Llama 3.1 405B. Overall, the LLMs performed better than human physicians, and Claude 3.5 Sonnet achieved the highest accuracy at 78.9%, significantly surpassing the accuracy of human physicians, which was 26.3%. These findings suggest that LLMs can improve rare disease diagnosis and serve as valuable tools in clinical settings, particularly in regions with limited resources. However, further validation and careful consideration of ethical and privacy issues are necessary for their effective integration into medical practice.

The impact of rare diseases on the quality of life in paediatric patients: current status

Rare diseases, also known as orphan diseases, are a group of disorders that affect a small percentage of the population. Despite individually affecting a small number of people, collectively, they impact millions worldwide. This is particularly significant in paediatric patients, highlighting the global scale of the issue. This review delves into the exact prevalence of rare diseases among children and adolescents and their diverse impact on the quality of life of patients and their families. The review sheds light on the complex interplay of genetic and environmental factors contributing to these conditions and the diagnostic challenges and delays often encountered in identifying and categorising these diseases. It is noted that although there have been significant strides in the field of genomic medicine and the development of orphan drugs, effective treatments remain limited. This necessitates a comprehensive, multidisciplinary approach to management involving various specialities working closely together to provide holistic care. Furthermore, the review addresses the psychosocial and economic burdens faced by families with paediatric patients suffering from rare diseases, highlighting the urgent need for enhanced support mechanisms. Recent technological and therapeutic advancements, including genomic sequencing and personalized medicine, offer promising avenues for improving patient outcomes. Additionally, the review underscores the role of policy and advocacy in advancing research, ensuring healthcare access, and supporting affected families. It emphasises the importance of increased awareness, education, and collaboration among healthcare providers, researchers, policymakers, and patient advocacy groups. It stresses the pivotal role each group plays in improving the diagnosis, treatment, and overall quality of life for paediatric patients with rare diseases.

Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers

Artificial intelligence (AI) has become a powerful tool in biochemistry, greatly enhancing research capabilities by enabling the analysis of complex datasets, predicting molecular interactions, and accelerating drug discovery. As AI continues to evolve, its applications in biochemistry are poised to expand, revolutionizing both theoretical and applied research. This review explores current and potential AI applications in biochemistry, with a focus on data analysis, molecular modeling, enzyme engineering, and metabolic pathway studies. Key AI techniques-such as machine learning algorithms, natural language processing, and AI-based molecular modeling-are discussed. The review also highlights emerging research areas benefiting from AI, including personalized medicine and synthetic biology. The methodology involves an extensive analysis of existing literature, particularly peer-reviewed studies on AI applications in biochemistry. AI-driven tools like AlphaFold, which have significantly advanced protein structure prediction, are evaluated alongside AI's role in expediting drug discovery. The review also addresses challenges such as data quality, model interpretability, and ethical considerations. Results indicate that AI has expanded the scope of biochemical research by facilitating large-scale data analysis, enhancing molecular simulations, and opening new avenues of inquiry. However, challenges remain, particularly in data handling and ethical concerns. In conclusion, AI is transforming biochemistry by driving innovation and expanding research possibilities. Future advancements in AI algorithms, interdisciplinary collaboration, and integration with automated techniques will be crucial to fully unlocking AI's potential in advancing biochemical research.

Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions

Diagnostic errors in health care pose significant risks to patient safety and are disturbingly common. In the emergency department (ED), the chaotic and high-pressure environment increases the likelihood of these errors, as emergency clinicians must make rapid decisions with limited information, often under cognitive overload. Artificial intelligence (AI) offers promising solutions to improve diagnostic errors in three key areas: information gathering, clinical decision support (CDS), and feedback through quality improvement. AI can streamline the information-gathering process by automating data retrieval, reducing cognitive load, and providing clinicians with essential patient details quickly. AI-driven CDS systems enhance diagnostic decision making by offering real-time insights, reducing cognitive biases, and prioritizing differential diagnoses. Furthermore, AI-powered feedback loops can facilitate continuous learning and refinement of diagnostic processes by providing targeted education and outcome feedback to clinicians. By integrating AI into these areas, the potential for reducing diagnostic errors and improving patient safety in the ED is substantial. However, successfully implementing AI in the ED is challenging and complex. Developing, validating, and implementing AI as a safe, human-centered ED tool requires thoughtful design and meticulous attention to ethical and practical considerations. Clinicians and patients must be integrated as key stakeholders across these processes. Ultimately, AI should be seen as a tool that assists clinicians by supporting better, faster decisions and thus enhances patient outcomes.

Quantifying hope: an EU perspective of rare disease therapeutic space and market dynamics

Rare diseases, affecting millions globally, pose a significant healthcare burden despite impacting a small population. While approximately 70% of all rare diseases are genetic and often begin in childhood, diagnosis remains slow and only 5% have approved treatments. The UN emphasizes improved access to primary care (diagnostic and potentially therapeutic) for these patients and their families. Next-generation sequencing (NGS) offers hope for earlier and more accurate diagnoses, potentially leading to preventative measures and targeted therapies. In here, we explore the therapeutic landscape for rare diseases, analyzing drugs in development and those already approved by the European Medicines Agency (EMA). We differentiate between orphan drugs with market exclusivity and repurposed existing drugs, both crucial for patients. By analyzing market size, segmentation, and publicly available data, this comprehensive study aims to pave the way for improved understanding of the treatment landscape and a wider knowledge accessibility for rare disease patients.

Artificial Intelligence: A New Frontier in Rare Disease Early Diagnosis

Rare diseases present significant challenges, including delays in diagnosis, inadequate treatment responses, and difficulties in monitoring. These challenges arise from the complexity of symptoms, limited medical expertise, and insufficient diagnostic tools. Artificial Intelligence (AI) has gained attention for its potential to improve healthcare, particularly in diagnosing complex conditions. By analyzing large datasets, recognizing patterns, and integrating clinical information, AI can refine diagnostic accuracy, enhance treatment strategies, and improve patient outcomes. This literature review examines AI applications in three key areas of rare disease diagnosis: genetic analysis, imaging-based phenotyping, and natural language processing (NLP) for clinical data extraction. AI-driven advancements in these domains have been explored to improve disease detection and management. However, several challenges persist, including limited data availability, algorithm transparency, privacy considerations, and ethical concerns. Efforts such as data augmentation and transfer learning are being explored to address these issues and expand AI's role in clinical practice. By refining diagnostic processes and optimizing treatment strategies, AI has the potential to improve the management of rare diseases. This review critically examines AI's role in rare disease diagnosis, with a particular emphasis on its applications in genetic analysis, imaging-based phenotyping, and NLP, while also addressing key challenges and future directions for clinical integration.

Telemedicine for Personalized Nutritional Intervention of Rare Diseases: A Narrative Review on Approaches, Impact, and Future Perspectives

Background: Telemedicine represents a growing opportunity to improve access to personalized care for patients with rare diseases, addressing the challenges of specialized healthcare that is often limited by geographical barriers. The aim of this narrative review is to explore how telemedicine can facilitate tailored nutritional interventions for rare diseases, focusing on inherited metabolic diseases, rare neurological disorders, such as leukodystrophies, and neuromuscular disorders, including spinal muscular atrophies. Methods: This narrative review is based on a systematic search of the published literature over the past 20 years, and includes systematic reviews, meta-analysis, retrospective studies, and original articles. References were selected through searches in databases such as PubMed and Scopus, applying predefined inclusion and exclusion criteria. Among the inclusion criteria, studies focusing on pediatric patients aged 0 to 18 years, diagnosed with rare neurological diseases or inherited metabolic disorders, and using telemedicine in addition to in-person visits at their reference center were considered. Among the exclusion criteria, studies involving patients with other pathologies or comorbidities and those involving patients older than 18 years were excluded. Results: A total of 66 documents were analyzed to examine the challenges and specific needs of patients with rare diseases, highlighting the advantages and limitations of telemedicine compared to traditional care. The use of telemedicine has revolutionized the medical approach, facilitating integrated care by multidisciplinary teams. Conclusions: Telemedicine still faces several technical, organizational, and security challenges, as well as disparities in access across different geographical areas. Emerging technologies such as artificial intelligence could positively transform the monitoring and management of patients with rare diseases. Telemedicine has great potential ahead of it in the development of increasingly personalized and effective care, in fact, emerging technologies are important to provide remote care, especially for patients with rare diseases.

Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications

The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments. Beyond applications, neuroscience itself has inspired AI innovations, with neural architectures and brain-like processes shaping advances in learning algorithms and explainable models. This bidirectional exchange has fueled breakthroughs such as dynamic connectivity mapping, real-time neural decoding, and closed-loop brain-computer systems that adaptively respond to neural states. However, challenges persist, including issues of data integration, ethical considerations, and the "black-box" nature of many AI systems, underscoring the need for transparent, equitable, and interdisciplinary approaches. By synthesizing the latest breakthroughs and identifying future opportunities, this review charts a path forward for the integration of AI and neuroscience. From harnessing multimodal data to enabling cognitive augmentation, the fusion of these fields is not just transforming brain science, it is reimagining human potential. This partnership promises a future where the mysteries of the brain are unlocked, offering unprecedented advancements in healthcare, technology, and beyond.

A Comparison of Transfer Learning Metaphyseal Sign Diagnostic Models for Kashin-Beck Disease Based on X-rays of Children's Hands

Background Kashin-Beck disease (KBD), prevalent in certain regions of the world, primarily affects children and is characterized by joint deformities. Timely screening and accurate diagnosis, heavily reliant on metaphyseal signs in X-rays, are crucial but challenging, especially in regions where specialist availability is scarce. Artificial intelligence (AI)-assisted diagnostic technology offers a valuable solution to streamline KBD screening, emphasizing its importance in enhancing diagnostic precision and efficiency. Methods This study developed and compared five deep learning models - KBV16, KBX, KBV19, KBIn, and KBM2 - to assist in diagnosing KBD by analyzing pediatric hand radiographs. We optimized these models with a dataset comprising 22,366 images, encompassing both metaphyseal positive and control groups. The models were trained and validated using Binary Cross-Entropy (BCE) and Accuracy (ACC) metrics. Results The KBV16 model outperformed the others, achieving an accuracy of 0.9563 on the validation set and 0.9535 on the test set. The implementation of data augmentation techniques, along with the meticulous selection of learning rates and batch sizes, significantly enhanced the models' performance. Conclusion This study presented a novel application of deep learning in KBD diagnosis, demonstrating the potential of AI models to enhance diagnostic precision. Notably, the KBV16 model emerged as a powerful tool for early detection of KBD. Future research should concentrate on refining these models for clinical use and integrating them into existing healthcare systems to improve medical services, particularly in medical resource-constrained regions.

[Artificial intelligence for the comprehensive approach to orphan/rare diseases: A scoping review]

INTRODUCTION

Orphan diseases (OD) are rare but collectively common, presenting challenges such as late diagnoses, disease progression, and limited therapeutic options. Recently, artificial intelligence (AI) has gained interest in the research of these diseases.

OBJECTIVE

To synthesize the available evidence on the use of AI in the comprehensive approach to orphan diseases.

METHODS

An exploratory systematic review of the Scoping Review type was conducted in PubMed, Bireme, and Scopus from 2019 to 2024.

RESULTS

fifty-six articles were identified, with 21.4% being experimental studies; 28 documents did not specify an OD, 8 documents focused primarily on genetic diseases; 53.57% focused on diagnosis, and 36 different algorithms were identified.

CONCLUSIONS

The information found shows the development of AI algorithms in different clinical settings, confirming the potential benefits in diagnosis times, therapeutic options, and greater awareness among health professionals.

Privacy-by-Design with Federated Learning will drive future Rare Disease Research

Up to 6% of the global population is estimated to be affected by one of about 10,000 distinct rare diseases (RDs). RDs are, to this day, often not understood, and thus, patients are heavily underserved. Most RD studies are chronically underfunded, and research faces inherent difficulties in analyzing scarce data. Furthermore, the creation and analysis of representative datasets are often constrained by stringent data protection regulations, such as the EU General Data Protection Regulation. This review examines the potential of federated learning (FL) as a privacy-by-design approach to training machine learning on distributed datasets while ensuring data privacy by maintaining the local patient data and only sharing model parameters, which is particularly beneficial in the context of sensitive data that cannot be collected in a centralized manner. FL enhances model accuracy by leveraging diverse datasets without compromising data privacy. This is particularly relevant in rare diseases, where heterogeneity and small sample sizes impede the development of robust models. FL further has the potential to enable the discovery of novel biomarkers, enhance patient stratification, and facilitate the development of personalized treatment plans. This review illustrates how FL can facilitate large-scale, cross-institutional collaboration, thereby enabling the development of more accurate and generalizable models for improved diagnosis and treatment of rare diseases. However, challenges such as non-independently distributed data and significant computational and bandwidth requirements still need to be addressed. Future research must focus on applying FL technology for rare disease datasets while exploring standardized protocols for cross-border collaborations that can ultimately pave the way for a new era of privacy-preserving and distributed data-driven rare disease research.

Rare disease genomics and precision medicine

Rare diseases, though individually uncommon, collectively affect millions worldwide. Genomic technologies and big data analytics have revolutionized diagnosing and understanding these conditions. This review explores the role of genomics in rare disease research, the impact of large consortium initiatives, advancements in extensive data analysis, the integration of artificial intelligence (AI) and machine learning (ML), and the therapeutic implications in precision medicine. We also discuss the challenges of data sharing and privacy concerns, emphasizing the need for collaborative efforts and secure data practices to advance rare disease research.

New Horizons of Model Informed Drug Development in Rare Diseases Drug Development

Model-informed approaches provide a quantitative framework to integrate all available nonclinical and clinical data, thus furnishing a totality of evidence approach to drug development and regulatory evaluation. Maximizing the use of all available data and information about the drug enables a more robust characterization of the risk-benefit profile and reduces uncertainty in both technical and regulatory success. This offers the potential to transform rare diseases drug development, where conducting large well-controlled clinical trials is impractical and/or unethical due to a small patient population, a significant portion of which could be children. Additionally, the totality of evidence generated by model-informed approaches can provide confirmatory evidence for regulatory approval without the need for additional clinical data. In the article, applications of novel quantitative approaches such as quantitative systems pharmacology, disease progression modeling, artificial intelligence, machine learning, modeling of real-world data using model-based meta-analysis and strategies such as external control and patient-reported outcomes as well as clinical trial simulations to optimize trials and sample collection are discussed. Specific case studies of these modeling approaches in rare diseases are provided to showcase applications in drug development and regulatory review. Finally, perspectives are shared on the future state of these modeling approaches in rare diseases drug development along with challenges and opportunities for incorporating such tools in the rational development of drug products.

Leveraging foundation and large language models in medical artificial intelligence

ABSTRACT

Recent advancements in the field of medical artificial intelligence (AI) have led to the widespread adoption of foundational and large language models. This review paper explores their applications within medical AI, introducing a novel classification framework that categorizes them as disease-specific, general-domain, and multi-modal models. The paper also addresses key challenges such as data acquisition and augmentation, including issues related to data volume, annotation, multi-modal fusion, and privacy concerns. Additionally, it discusses the evaluation, validation, limitations, and regulation of medical AI models, emphasizing their transformative potential in healthcare. The importance of continuous improvement, data security, standardized evaluations, and collaborative approaches is highlighted to ensure the responsible and effective integration of AI into clinical applications.

Mapping the diagnostic odyssey of congenital disorders of glycosylation (CDG): insights from the community

BACKGROUND

Congenital disorders of glycosylation (CDG) are a group of rare metabolic diseases with heterogeneous presentations, leading to substantial diagnostic challenges, which are poorly understood. Therefore, this study aims to elucidate this diagnostic journey by examining families' and professionals' experiences.

RESULTS AND DISCUSSION

A questionnaire was designed for CDG families and professionals, garnering 160 and 35 responses, respectively. Analysis revealed the lack of seizures as a distinctive feature between PMM2-CDG (11.2%) with Other CDG (57.7%) at symptom onset. Hypotonia and developmental disability were prevalent symptoms across all studied CDG. Feeding problems were identified as an early onset symptom in PMM2-CDG (Cramer's V (V) = 0.30, False Discovery Rate (FDR) = 3.8 × 10- 9), and hypotonia in all studied CDG (V = 0.34, FDR = 7.0 × 10- 3). The average time to diagnosis has decreased in recent years (now ~ 3.9 years), due to advancements namely the increased use of whole genome and exome sequencing. However, misdiagnoses remain prevalent (PMM2-CDG - 44.9%, non-PMM2-CDG - 64.8%). To address these challenges, we propose adapting medical training to increase awareness of CDG and other rare diseases, ongoing education for physicians, the development of educational resources for relevant medical units, and empowerment of families through patient organizations and support networks.

CONCLUSION

This study emphasizes the crucial role of community-centered research, and the insights families can offer to enhance CDG management. By pinpointing existing gaps and needs, our findings can inform targeted interventions and support systems to improve the lives of those impacted by CDG.

Development of an Expert-Based Scoring System for Early Identification of Patients with Inborn Errors of Immunity in Primary Care Settings - the PIDCAP Project

Early diagnosis of inborn errors of immunity (IEIs) has been shown to reduce mortality, morbidity, and healthcare costs. The need for early diagnosis has led to the development of computational tools that trigger earlier clinical suspicion by physicians. Primary care professionals serve as the first line for improving early diagnosis. To this end, a computer-based tool (based on extended Jeffrey Modell Foundation (JMF) Warning Signs) was developed to assist physicians with diagnosis decisions for IEIs in the primary care setting. Two expert-guided scoring systems (one pediatric, one adult) were developed. IEI warning signs were identified and a panel of 36 experts reached a consensus on which signs to include and how they should be weighted. The resulting scoring system was tested against a retrospective registry of patients with confirmed IEI using primary care EHRs. A pilot study to assess the feasibility of implementation in primary care was conducted. The scoring system includes 27 warning signs for pediatric patients and 24 for adults, adding additional clinically relevant criteria established by expert consensus to the JMF Warning Signs. Cytopenias, ≥ 2 systemic infections, recurrent fever and bronchiectasis were the leading warning signs in children, as bronchiectasis, autoimmune diseases, cytopenias, and > 3 pneumonias were in adults. The PIDCAP (Primary Immune Deficiency "Centre d'Atenció Primària" that stands for Primary Care Center in Catalan) tool was implemented in the primary care workstation in a pilot area. The expert-based approach has the potential to lessen under-reporting and minimize diagnostic delays of IEIs. It can be seamlessly integrated into clinical primary care workstations.

[Several suggestions for improving diagnosis and management of patients with neurofibromatosis type 1]

Neurofibromatosis type 1 (NF1) is an autosomal dominant genetic disease caused by the mutations in the NF1 gene, with an incidence of approximately 1/3 000. Affecting multiple organs and systems throughout the body, NF1 caused a wide variety of clinical symptoms. A comprehensive multidisciplinary diagnostic and treatment model is needed to meet the diverse needs of NF1 patients and improve their quality of life. In recent years, the emergence of targeted therapies has further benefited NF1 patients, and the number of clinical consultations has increased dramatically. However, due to the rarity of the disease itself and insufficient attention previously, the standardized, systematic, and precise diagnosis and treatment model of NF1 still needs to be further improved. In this paper, we reviewed the current status of comprehensive diagnosis and treatment of NF1 in China, combine with our long-term experiences in diagnosis and treatment of this disease. Meanwhile, we propose future directions and several suggestions for the comprehensive diagnosis and treatment model for Chinese NF1 patients.

Deciphering the Complexity of FSHD: A Multimodal Approach as a Model for Rare Disorders

Rare diseases are heterogeneous diseases characterized by various symptoms and signs. Due to the low prevalence of such conditions (less than 1 in 2000 people), medical expertise is limited, knowledge is poor and patients' care provided by medical centers is inadequate. An accurate diagnosis is frequently challenging and ongoing research is also insufficient, thus complicating the understanding of the natural progression of the rarest disorders. This review aims at presenting the multimodal approach supported by the integration of multiple analyses and disciplines as a valuable solution to clarify complex genotype-phenotype correlations and promote an in-depth examination of rare disorders. Taking into account the literature from large-scale population studies and ongoing technological advancement, this review described some examples to show how a multi-skilled team can improve the complex diagnosis of rare diseases. In this regard, Facio-Scapulo-Humeral muscular Dystrophy (FSHD) represents a valuable example where a multimodal approach is essential for a more accurate and precise diagnosis, as well as for enhancing the management of patients and their families. Given their heterogeneity and complexity, rare diseases call for a distinctive multidisciplinary approach to enable diagnosis and clinical follow-up.

Efficacy of avalglucosidase alfa on forced vital capacity percent predicted in treatment-naïve patients with late-onset Pompe disease: A pooled analysis of clinical trials

Background

The efficacy of avalglucosidase alfa (AVA) versus alglucosidase alfa (ALG) on forced vital capacity percent predicted (FVCpp) in patients with late-onset Pompe disease (LOPD) has been assessed in the Phase 3 COMET trial (NCT02782741). Due to the rarity of LOPD and thus small sample size in COMET, additional data were analyzed to gain further insights into the efficacy of AVA versus ALG.

Methods

Data from treatment-naive patients with LOPD were pooled from COMET and Phase 1/2 NEO1/NEO-EXT (NCT01898364/NCT02032524) trials for patients treated with AVA, and Phase 3 LOTS trial (NCT00158600) for patients treated with ALG. Regression analyses using mixed models with repeated measures consistent with those pre-specified in COMET were performed post-hoc. Analyses were adjusted for trials and differences in baseline characteristics. Four models were developed: Model 1 considered all trials; Model 2 included Phase 3 trials; Model 3 included Phase 3 trials and was adjusted for baseline ventilation use; Model 4 included COMET and NEO1/NEO-EXT (i.e., AVA trials only).

Results

Overall, 100 randomized patients from COMET (AVA, n = 51, ALG, n = 49), 60 from LOTS (ALG arm only), and three patients from NEO1/NEO-EXT (who received open-label AVA only) were considered for analysis. Mean age at enrollment was similar across trials (45.3-50.3 years); however, patients from LOTS had a longer mean duration of disease versus COMET and NEO1/NEO-EXT trials (9.0 years and 0.5-2.2 years, respectively) and younger mean age at diagnosis (36.2 years and 44.7-48.6 years, respectively). Least squares mean (95% confidence interval) improvement from baseline in FVCpp at Week 49-52 for AVA versus ALG was 2.43 (-0.13; 4.99) for COMET (n = 98); 2.31 (0.06; 4.57) for Model 1 (n = 160); 2.43 (0.21; 4.65) for Model 2 (n = 157); 2.80 (0.54; 5.05) for Model 3 (n = 154); and 2.27 (-0.30; 4.45) for Model 4 (n = 101).

Conclusions

Models 1 to 3, which had an increased sample size versus COMET, demonstrated a nominally significant effect on FVCpp favoring AVA versus ALG after 1 year of treatment, consistent with results from COMET.

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

A review: artificial intelligence in image-guided spinal surgery

INTRODUCTION

Due to the complex anatomy of the spine and the intricate surgical procedures involved, spinal surgery demands a high level of technical expertise from surgeons. The clinical application of image-guided spinal surgery has significantly enhanced lesion visualization, reduced operation time, and improved surgical outcomes.

AREAS COVERED

This article reviews the latest advancements in deep learning and artificial intelligence in image-guided spinal surgery, aiming to provide references and guidance for surgeons, engineers, and researchers involved in this field.

EXPERT OPINION

Our analysis indicates that image-guided spinal surgery, augmented by artificial intelligence, outperforms traditional spinal surgery techniques. Moving forward, it is imperative to collect a more expansive dataset to further ensure the procedural safety of such surgeries. These insights carry significant implications for the integration of artificial intelligence in the medical field, ultimately poised to enhance the proficiency of surgeons and improve surgical outcomes.

Charting a course for global progress in PIDs by 2030 - proceedings from the IPOPI global multi-stakeholders' summit (September 2023)

The International Patient Organisation for Primary Immunodeficiencies (IPOPI) held its second Global Multi-Stakeholders' Summit, an annual stimulating and forward-thinking meeting uniting experts to anticipate pivotal upcoming challenges and opportunities in the field of primary immunodeficiency (PID). The 2023 summit focused on three key identified discussion points: (i) How can immunoglobulin (Ig) therapy meet future personalized patient needs? (ii) Pandemic preparedness: what's next for public health and potential challenges for the PID community? (iii) Diagnosing PIDs in 2030: what needs to happen to diagnose better and to diagnose more? Clinician-Scientists, patient representatives and other stakeholders explored avenues to improve Ig therapy through mechanistic insights and tailored Ig preparations/products according to patient-specific needs and local exposure to infectious agents, amongst others. Urgency for pandemic preparedness was discussed, as was the threat of shortage of antibiotics and increasing antimicrobial resistance, emphasizing the need for representation of PID patients and other vulnerable populations throughout crisis and care management. Discussion also covered the complexities of PID diagnosis, addressing issues such as global diagnostic disparities, the integration of patient-reported outcome measures, and the potential of artificial intelligence to increase PID diagnosis rates and to enhance diagnostic precision. These proceedings outline the outcomes and recommendations arising from the 2023 IPOPI Global Multi-Stakeholders' Summit, offering valuable insights to inform future strategies in PID management and care. Integral to this initiative is its role in fostering collaborative efforts among stakeholders to prepare for the multiple challenges facing the global PID community.

Artificial intelligence in drug repurposing for rare diseases: a mini-review

Drug repurposing, the process of identifying new uses for existing drugs beyond their original indications, offers significant advantages in terms of reduced development time and costs, particularly in addressing unmet medical needs in rare diseases. Artificial intelligence (AI) has emerged as a transformative force in healthcare, and by leveraging AI technologies, researchers aim to overcome some of the challenges associated with rare diseases. This review presents concrete case studies, as well as pre-existing platforms, initiatives, and companies that demonstrate the application of AI for drug repurposing in rare diseases. Despite representing a modest part of the literature compared to other diseases such as COVID-19 or cancer, the growing interest, and investment in AI for drug repurposing in rare diseases underscore its potential to accelerate treatment availability for patients with unmet medical needs.

Advancements in Artificial Intelligence in Emergency Medicine in Taiwan: A Narrative Review

The rapid progression of artificial intelligence (AI) in healthcare has greatly influenced emergency medicine, particularly in Taiwan-a nation celebrated for its technological innovation and advanced public healthcare. This narrative review examines the current status of AI applications in Taiwan's emergency medicine and highlights notable achievements and potential areas for growth. AI has wide capabilities encompass a broad range, including disease prediction, diagnostic imaging interpretation, and workflow enhancement. While the integration of AI presents promising advancements, it is not devoid of challenges. Concerns about the interpretability of AI models, the importance of dataset accuracy, the necessity for external validation, and ethical quandaries emphasize the need for a balanced approach. Regulatory oversight also plays a crucial role in ensuring the safe and effective deployment of AI tools in clinical settings. As its footprint continues to expand in medical education and other areas, addressing these challenges is imperative to harness the full potential of AI for transforming emergency medicine in Taiwan.

Performance of Artificial Intelligence (AI)-Powered Chatbots in the Assessment of Medical Case Reports: Qualitative Insights From Simulated Scenarios

Introduction With the expanding awareness and use of AI-powered chatbots, it seems possible that an increasing number of people could use them to assess and evaluate their medical symptoms. If chatbots are used for this purpose, that have not previously undergone a thorough medical evaluation for this specific use, various risks might arise. The aim of this study is to analyze and compare the performance of popular chatbots in differentiating between severe and less critical medical symptoms described from a patient's perspective and to examine the variations in substantive medical assessment accuracy and empathetic communication style among the chatbots' responses. Materials and methods Our study compared three different AI-supported chatbots - OpenAI's ChatGPT 3.5, Microsoft's Bing Chat, and Inflection's Pi AI. Three exemplary case reports for medical emergencies as well as three cases without an urgent reason for an emergency medical admission were constructed and analyzed. Each case report was accompanied by identical questions concerning the most likely suspected diagnosis and the urgency of an immediate medical evaluation. The respective answers of the chatbots were qualitatively compared with each other regarding the medical accuracy of the differential diagnoses mentioned and the conclusions drawn, as well as regarding patient-oriented and empathetic language. Results All examined chatbots were capable of providing medically plausible and probable diagnoses and classifying situations as acute or less critical. However, their responses varied slightly in the level of their urgency assessment. Clear differences could be seen in the level of detail of the differential diagnoses, the overall length of the answers, and how the chatbot dealt with the challenge of being confronted with medical issues. All given answers were comparable in terms of empathy level and comprehensibility. Conclusion Even AI chatbots that are not designed for medical applications already offer substantial guidance in assessing typical medical emergency indications but should always be provided with a disclaimer. In responding to medical queries, characteristic differences emerge among chatbots in the extent and style of their respective answers. Given the lack of medical supervision of many established chatbots, subsequent studies, and experiences are essential to clarify whether a more extensive use of these chatbots for medical concerns will have a positive impact on healthcare or rather pose major medical risks.

The use of artificial intelligence in the treatment of rare diseases: A scoping review

With the increasing application of artificial intelligence (AI) in medicine and healthcare, AI technologies have the potential to improve the diagnosis, treatment, and prognosis of rare diseases. Presently, existing research predominantly focuses on the areas of diagnosis and prognosis, with relatively fewer studies dedicated to the domain of treatment. The purpose of this review is to systematically analyze the existing literature on the application of AI in the treatment of rare diseases. We searched three databases for related studies, and established criteria for the selection of retrieved articles. From the 407 unique articles identified across the three databases, 13 articles from 8 countries were selected, which investigated 10 different rare diseases. The most frequently studied rare disease group was rare neurologic diseases (n = 5/13, 38.46%). Among the four identified therapeutic domains, 7 articles (53.85%) focused on drug research, with 5 specifically focused on drug discovery (drug repurposing, the discovery of drug targets and small-molecule inhibitors), 1 on pre-clinical studies (drug interactions), and 1 on clinical studies (information strength assessment of clinical parameters). Across the selected 13 articles, we identified total 32 different algorithms, with random forest (RF) being the most commonly used (n = 4/32, 12.50%). The predominant purpose of AI in the treatment of rare diseases in these articles was to enhance the performance of analytical tasks (53.33%). The most common data source was database data (35.29%), with 5 of these studies being in the field of drug research, utilizing classic databases such as RCSB, PDB and NCBI. Additionally, 47.37% of the articles highlighted the existing challenge of data scarcity or small sample sizes.

The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders

Rare genetic disorders (RDs), characterized by their low prevalence and diagnostic complexities, present significant challenges to healthcare systems. This article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) in addressing these challenges. It emphasizes the need for accurate and early diagnosis of RDs, often hindered by genetic and clinical heterogeneity. This article discusses how AI and ML are reshaping healthcare, providing examples of their effectiveness in disease diagnosis, prognosis, image analysis, and drug repurposing. It highlights AI's ability to efficiently analyze extensive datasets and expedite diagnosis, showcasing case studies like Face2Gene. Furthermore, the article explores how AI tailors treatment plans for RDs, leveraging ML and deep learning (DL) to create personalized therapeutic regimens. It emphasizes AI's role in drug discovery, including the identification of potential candidates for rare disease treatments. Challenges and limitations related to AI in healthcare, including ethical, legal, technical, and human aspects, are addressed. This article underscores the importance of data ethics, privacy, and algorithmic fairness, as well as the need for standardized evaluation techniques and transparency in AI research. It highlights second-generation AI systems that prioritize patient-centric care, efficient patient recruitment for clinical trials, and the significance of high-quality data. The integration of AI with telemedicine, the growth of health databases, and the potential for personalized therapeutic recommendations are identified as promising directions for the field. In summary, this article provides a comprehensive exploration of how AI and ML are revolutionizing the diagnosis and treatment of RDs, addressing challenges while considering ethical implications in this rapidly evolving healthcare landscape.