Abstract：
OBJECTIVE: Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management.
RESULTS: We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.
RESULTS: We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.
摘要：
目标：人工智能(AI)，无论是神经网络，机器学习或深度学习，对医疗保健系统有许多有益的影响；然而，其潜在的应用和对免疫疾病的诊断能力还有待探索。了解人工智能系统可以帮助医护人员更好地将人工智能融入他们的实践中，并释放其在诊断方面的潜力。临床研究,和疾病管理。
结果：我们回顾了人工智能系统的最新进展及其在医疗保健系统中的集成，以及它们在疾病诊断和管理方面的潜在好处。我们探索了用于过敏诊断的机器学习及其从患者数据集中的学习模式，以及在与过敏反应甚至远程监控相关的研究领域使用AI的可能优势。考虑到临床医生和患者在将AI整合到医疗保健中方面提出的道德挑战和隐私问题，我们探讨了由监管机构调整的新准则。尽管面临这些挑战,人工智能似乎已成功融入各种医疗保健系统，并提供以患者为中心的解决方案，同时帮助医护人员。人工智能为免疫疾病诊断领域提供了新的希望,监测,和管理，因此有可能彻底改变医疗保健系统。
结果：我们回顾了人工智能系统的最新进展及其在医疗保健系统中的集成，以及它们在疾病诊断和管理方面的潜在好处。我们探索了用于过敏诊断的机器学习及其从患者数据集中的学习模式，以及在与过敏反应甚至远程监控相关的研究领域使用AI的可能优势。考虑到临床医生和患者在将AI整合到医疗保健中方面提出的道德挑战和隐私问题，我们探讨了由监管机构调整的新准则。尽管面临这些挑战,人工智能似乎已成功融入各种医疗保健系统，并提供以患者为中心的解决方案，同时帮助医护人员。人工智能为免疫疾病诊断领域提供了新的希望,监测,和管理，因此有可能彻底改变医疗保健系统。
