•  Natural language processing in the medical domain is a promising research area that has been investigated in essential applications. The relationship between natural language processing and healthcare helps provide astonishing insight into understanding quality, enhancing methods, and better medical diagnoses for patients.
•  Integrating natural language processing in medical analysis brings novel and breathtaking opportunities for superior healthcare distribution and patient experience. Significant advantages of utilizing natural language processing in the healthcare sector are enhancing clinical documentation, supporting clinical trial matching, and accelerating clinical decision-making.
•  Natural language processing employs medical data by utilizing machine learning and deep learning algorithms to detect and discover many diseases. Interpretation of linguistic features, unleashing unused electronic health record information, and implementing alternative information sources are a few strengths of medical natural language processing.
•  The most magnificent and impressive application concepts of natural language processing in healthcare are
   •  Clinical Documentation
   •  Speech Recognition
   •  Computer-Assisted Coding (CAC)
   •  Automated Registry Reporting
   •  Clinical Decision Support
   •  Clinical Trial Matching, Prior Authorization
   •  AI Chatbots and Virtual Scribe
   •  Risk Adjustment and Hierarchical Condition Categories
   •  Computational Phenotyping, Review Management & Sentiment Analysis
   •  Dictation and EMR Implications
   •  Root Cause Analysis
   •  Clinical Coding
   •  Clinical Named Entity Recognition
   •  Computational Phenotyping
   •  Pathological Reports Annotation
   •  Medical Dictionary Entity Representation
   •  Disease Annotation
   •  Tumor Detection and Segmentation.
•  The main focus of healthcare natural language processing for enhanced function is electronic health record usability, predictive analytics, phenotyping, and quality improvement. Some of the challenges in medical natural language processing are inadequacy of de-identification of medical corpora, hard in producing ICD code from the medical record, insufficient key annotation of medical text, difficulty in medical text mining, and demand to extract relation in medical entities.
•  Even though natural language processing is highly applied for healthcare, it faces a few restrictions, such as biases and overfitting in machine learning training, training on outcomes, and discrepancies in the writing of medical notes. In recent times, natural language processing has been highly beneficial in the COVID-19 pandemic by facilitating an essential role in identifying the disease, balancing intensive care, finding the drugs, and controlling the proliferation of infections. Cognitive technologies, health chatbots, blockchain technology, and deep learning are the current trending techniques applied in healthcare natural language processing.
•  Futuristic opportunities of natural language processing in healthcare are clinical natural language processing applied to mental health records, using natural language processing for large-sample clinical research, natural language processing in clinical practice for extrinsic evaluation, and deep learning with word embedding for medical imaging.