Natural Language Processing and Machine Learning are the two buzzwords in the healthcare industry today. Across the globe, healthcare providers are relying on these technologies to deliver fast, safe and efficient healthcare services to patients. The demand for intelligent decision making and automation is increasing day by day and is sure to uplift the face of the healthcare sector in the near future.
Natural Language Processing (NLP) is a branch of Artificial Intelligence that allows computers to assess and comprehend both spoken and written human language. It is primarily used to develop software that generates and understands natural languages, allowing users to converse with computers in their natural language rather than using artificial or programming languages. NLP allows clinicians to streamline administrative operations, devote more time for patient care, and enhance the patient experience by leveraging real-time data.
Machine learning (ML) is a form of Artificial Intelligence (AI) that enables systems to use algorithms and data to improve prediction accuracy and replicate intelligent human behavior. It analyses historical data and delivers an accurate output. Healthcare providers can now use machine learning to make smart decisions about patient diagnosis and treatment plans, resulting in an overall improvement in healthcare services. Machine learning algorithms can also assist clinicians in obtaining real-time data and key statistics related to a patient’s ailment, blood pressure, lab test results, family history, etc.
Natural Language Processing and Machine Learning are undoubtedly the most widely used and rapidly expanding technologies in the healthcare industry. Let’s see how these technologies aid in the transformation of the healthcare sector:
Doctors are adopting speech recognition devices integrated with NLP algorithms for generating patient records. This not only enhances the quality of patient data, but also relieves medical practitioners from administrative duties, minimizes stress and enables them to spend maximum time on patient care.
NLP systems can assist in bridging the gap between individuals’ comprehension of their health and sophisticated medical words. NLP is indeed a powerful tool to combat Electronic Health Record ( EHR) distress. It is used by many medical service providers as a substitute for typing and written notes.
NLP can assist healthcare employees by autofilling all important data accurately and quickly. It can also detect flaws in technical documentation and give recommendations, thus lowering operational expenses for healthcare organizations.
NLP integrated medical chatbots are used in hospital websites to interact with patients virtually and understand their problems even before consulting a doctor. These bots will ask important questions to patients to understand their symptoms and treatment options and the chat history will be shared with doctors. This in a way will reduce hospital wait times, consultation hours, and manage patient flow efficiently.
Robotic surgery integrated with Machine Learning is quite popular nowadays. Surgeries require a lot of expertise and it’s a long procedure. Certain surgeries like neurosurgeries involve high risks and any slight mistake can even cause death. In such scenarios, robots seem to be a safe bet for carrying out successful surgeries.
When it comes to medical image analysis, Machine Learning will categorize lesions either into benign and malignant or normal and abnormal. Radiologists can make use of ML algorithms to easily distinguish between healthy and cancer cells, thereby providing patients with highly safe and effective radiotherapies.
Big Data can be used to forecast and manage disease outbreaks. The goal is to figure out how an epidemic spreads in specific places and study it. This is especially beneficial in villages and other locations where healthcare facilities are scarce. Machine learning models may be created to forecast the nature of an epidemic’s propagation in a given location, as well as where the next outbreak is most likely to occur. When training the machine learning model to discover other locations prone to outbreaks, factors like climate, demographics, geography and population distribution of an affected area must be taken into account.
Clinical trials are time-consuming and costly. Machine learning can be utilized to perform predictive analytics in order to identify possible clinical trial participants and enable scientists to pool people from various data points and sources. Machine Learning will carry out continuous trial monitoring with minimal human interaction and errors.
Natural Language Processing and Machine Learning find a lot of applications in the healthcare industry. Integrating these technologies into hospitals or clinics can help medical service providers to automate tasks, eliminate errors, enhance patient service, and many more. ThinkPalm blends Natural Language Processing and Machine Learning into software solutions to deliver outstanding results for clients in the healthcare industry. Get in touch with us for accurate, efficient and healthcare-specific web application development and services.