โก Quick Summary
This narrative review explores the role of Artificial Intelligence (AI) in enhancing care for individuals with haemophilia, a rare bleeding disorder. The findings suggest that AI can significantly improve diagnostic accuracy, predictive modelling, and treatment optimisation, paving the way for more personalised healthcare strategies. ๐ค
๐ Key Details
- ๐ Review Scope: Analysis of 40 articles on AI applications in haemophilia care.
- ๐งฉ Focus Areas: Diagnostic tools, predictive modelling, digital health technologies, treatment optimisation.
- โ๏ธ Challenges Addressed: Algorithmic bias, cost, accessibility, and data scarcity.
- ๐ Key Technologies: Machine learning, AI-powered digital tools, generative AI.
๐ Key Takeaways
- ๐ AI enhances diagnostic accuracy and predicts disease severity in haemophilia patients.
- ๐ค Machine learning improves precision in robot-assisted surgeries.
- ๐ก AI-driven digital tools like chatbots and wearables support self-management and real-time monitoring.
- ๐ Generative AI aids in patient education and clinical decision-making.
- ๐ฌ Individualised prophylaxis strategies using factor mimetics and rebalancing agents are emerging.
- โ๏ธ Ethical concerns and data scarcity limit the full potential of AI in haemophilia care.
- ๐ Future research should focus on mitigating biases and improving data availability.
๐ Background
Haemophilia is a rare X-linked bleeding disorder characterized by deficiencies in coagulation factors, leading to recurrent bleeding episodes, particularly in joints and muscles. Despite advancements in treatment, challenges such as inhibitor development and disparities in technology access remain prevalent. The integration of AI into haemophilia care offers a promising avenue to enhance diagnostic and treatment strategies, ultimately improving patient outcomes.
๐๏ธ Study
This review synthesizes findings from 40 articles, focusing on the applications of AI in haemophilia care. The authors assessed the impact of AI on diagnosis, predictive modelling, and treatment optimisation while also addressing the limitations and ethical concerns associated with AI integration in healthcare.
๐ Results
The review highlights that AI significantly enhances diagnostic accuracy, predicts disease severity, and assesses inhibitor risks. Furthermore, AI-powered digital tools facilitate self-management and real-time monitoring, while machine learning improves precision in robot-assisted surgeries. The emergence of AI-driven individualised prophylaxis strategies marks a significant advancement in treatment optimisation.
๐ Impact and Implications
The integration of AI into haemophilia care represents a paradigm shift towards precision medicine. By leveraging AI technologies, healthcare providers can offer more tailored treatment strategies, ultimately leading to improved patient outcomes. However, ethical concerns and data limitations must be addressed to fully realise the potential of AI in this field.
๐ฎ Conclusion
This review underscores the transformative potential of AI in haemophilia care, highlighting its ability to enhance diagnostic accuracy and treatment strategies. As research continues to evolve, it is crucial to focus on mitigating biases and improving data availability to optimise patient outcomes. The future of haemophilia care looks promising with the integration of AI technologies! ๐
๐ฌ Your comments
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Artificial Intelligence Applications in Haemophilia Care: A Narrative Review of the Literature.
Abstract
INTRODUCTION: Haemophilia is a rare X-linked bleeding disorder caused by deficiencies in coagulation factors, leading to recurrent bleeding episodes, particularly in joints and muscles. Haemophilia A accounts for 80%-85% of cases, while Haemophilia B represents 15%-20%. Despite advances in treatment, challenges such as inhibitor development, treatment variability, data scarcity, algorithmic bias, and disparities in technology access persist. Artificial intelligence (AI) has the potential to improve diagnostic accuracy, prognostication, and management, advancing personalised treatment strategies.
AIM: This review examines AI applications in haemophilia care, assessing their impact on diagnosis, predictive modelling, digital health solutions, and treatment optimisation while addressing limitations and ethical concerns.
METHODS: A narrative review of 40 articles was conducted, focusing on AI-driven diagnostic tools, predictive modelling, digital health technologies, and treatment optimisation. Additionally, barriers to AI integration, including algorithmic bias, cost, and accessibility, were evaluated.
RESULTS: AI enhances diagnostic accuracy, predicts disease severity, assesses inhibitor risks, and optimises recombinant therapies. Machine learning improves precision in robot-assisted surgeries, while AI-powered digital tools, including chatbots and wearables, support self-management and real-time monitoring. Generative AI facilitates patient education and predictive modelling, aiding clinical decision-making. AI-driven individualised prophylaxis strategies using factor mimetics and rebalancing agents are emerging.
CONCLUSION: AI represents a paradigm shift toward precision medicine in haemophilia care. However, ethical concerns, data scarcity, and financial barriers limit its full potential. Future research should focus on mitigating biases, improving data availability, and refining AI-driven personalised treatment strategies to optimise patient outcomes.
Author: [‘Aramouni K’, ‘Jabbour K’, ‘Charbel N’, ‘Hammoud R’, ‘Klim J’, ‘Taher A’, ‘Noun P’, ‘Kreidieh F’]
Journal: Haemophilia
Citation: Aramouni K, et al. Artificial Intelligence Applications in Haemophilia Care: A Narrative Review of the Literature. Artificial Intelligence Applications in Haemophilia Care: A Narrative Review of the Literature. 2025; (unknown volume):(unknown pages). doi: 10.1111/hae.70135