Nowadays, diseases are increasing in numbers and severity by the hour.
Immunity diseases, affecting 8\% of the world population in 2017 according to
the World Health Organization (WHO), is a field in medicine worth attention due
to the high rate of disease occurrence classified under this category. This
work presents an up-to-date review of state-of-the-art immune diseases
healthcare solutions. We focus on tackling the issue with modern solutions such
as Deep Learning to detect anomalies in the early stages hence providing health
practitioners with efficient tools. We rely on advanced deep learning
techniques such as Convolutional Neural Networks (CNN) to fulfill our objective
of providing an efficient tool while providing a proficient analysis of this
solution. The proposed solution was tested and evaluated by the immunology
department in the Principal Military Hospital of Instruction of Tunis, which
considered it a very helpful tool.

本研究通过使用深度学习技术，利用卷积神经网络（CNN）等高级技术，及早检测免疫疾病中的异常情况，提供有效的医疗解决方案。研究结果证明该方案被 Tunis 指挥的主要军事医院免疫学部门采纳并评价为非常有帮助的工具。

采用深度学习技术对免疫荧光幻灯片进行解读：抗核抗体案例研究

Interpretation of immunofluorescence slides by deep learning techniques:  anti-nuclear antibodies case study

Internet-of-Things (IoT) is profoundly redefining the way we create, consume,
and share information. Health aficionados and citizens are increasingly using
IoT technologies to track their sleep, food intake, activity, vital body
signals, and other physiological observations. This is complemented by IoT
systems that continuously collect health-related data from the environment and
inside the living quarters. Together, these have created an opportunity for a
new generation of healthcare solutions. However, interpreting data to
understand an individual's health is challenging. It is usually necessary to
look at that individual's clinical record and behavioral information, as well
as social and environmental information affecting that individual. Interpreting
how well a patient is doing also requires looking at his adherence to
respective health objectives, application of relevant clinical knowledge and
the desired outcomes.
We resort to the vision of Augmented Personalized Healthcare (APH) to exploit
the extensive variety of relevant data and medical knowledge using Artificial
Intelligence (AI) techniques to extend and enhance human health to presents
various stages of augmented health management strategies: self-monitoring,
self-appraisal, self-management, intervention, and disease progress tracking
and prediction. kHealth technology, a specific incarnation of APH, and its
application to Asthma and other diseases are used to provide illustrations and
discuss alternatives for technology-assisted health management. Several
prominent efforts involving IoT and patient-generated health data (PGHD) with
respect converting multimodal data into actionable information (big data to
smart data) are also identified. Roles of three components in an evidence-based
semantic perception approach- Contextualization, Abstraction, and
Personalization are discussed.

IoT has created opportunities for healthcare solutions but interpreting data requires clinical, behavioral, and environmental information. Augmented Personalized Healthcare (APH) uses AI techniques to enhance human health with self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. The kHealth technology and its application to Asthma are used to provide illustrations for technology-assisted health management.