  The connection between the design and delivery of health care services using
information technology is known as health informatics. It involves data usage,
validation, and transfer of an integrated medical analysis using neural
networks of multi-layer deep learning techniques to analyze complex data. For
instance, Google incorporated ''DeepMind'' health mobile tool that integrates
\& leverage medical data needed to enhance professional healthcare delivery to
patients. Moorfield Eye Hospital London introduced DeepMind Research Algorithms
with dozens of retinal scans attributes while DeepMind UCL handled the
identification of cancerous tissues using CT \& MRI Scan tools. Atomise
analyzed drugs and chemicals with Deep Learning Neural Networks to identify
accurate pre-clinical prescriptions. Health informatics makes medical care
intelligent, interactive, cost-effective, and accessible; especially with DL
application tools for detecting the actual cause of diseases. The extensive use
of neural network tools leads to the expansion of different medical disciplines
which mitigates data complexity and enhances 3-4D overlap images using target
point label data detectors that support data augmentation, un-semi-supervised
learning, multi-modality and transfer learning architecture. Health science
over the years focused on artificial intelligence tools for care delivery,
chronic care management, prevention/wellness, clinical supports, and diagnosis.
The outcome of their research leads to cardiac arrest diagnosis through Heart
Signal Computer-Aided Diagnostic tool (CADX) and other multifunctional deep
learning techniques that offer care, diagnosis \& treatment. Health informatics
provides monitored outcomes of human body organs through medical images that
classify interstitial lung disease, detects image nodules for reconstruction \&
tumor segmentation. The emergent medical research applications gave rise to
clinical-pathological human-level performing tools for handling Radiological,
Ophthalmological, and Dental diagnosis. This research will evaluate
methodologies, Deep learning architectures, approaches, bio-informatics,
specified function requirements, monitoring tools, ANN (artificial neural
network), data labeling \& annotation algorithms that control data validation,
modeling, and diagnosis of different diseases using smart monitoring health
informatics applications.
