Role of ML & NLP in accelerating digital healthcare transformation

calendar_today 25 August, 2022 person_outline Growth Plus Reports

image

Worldwide, healthcare providers are depending on recent technologies like machine learning and natural language processing to deliver safe and efficient services to their patients. Undoubtedly, these are the most prevalent and quickly developing technologies in the healthcare sector.

What is NLP?

Natural Language Processing, a part of Artificial Intelligence, allows practitioners to access patient data in a more structured way. NLP enables computers to assess and comprehend spoken and written human language. To communicate with computers in their natural language rather than through programming or artificial languages, it is mostly used to create software that creates and comprehends natural languages. By utilizing real-time data, NLP enables physicians to automate routine tasks, free up more time for medical care, and improve patient experiences.

What is ML?

Machine Learning (ML) enables computers to use data and algorithms to increase prediction accuracy and imitate intelligent human behavior. It performs an analysis of old data and produces precise results. Now that healthcare providers may utilize machine learning to make wise decisions about patient diagnoses and treatment plans, healthcare services have generally improved.

How are NLP & ML applied for revolutionizing healthcare?

For creating patient records, doctors are utilizing speech recognition technology combined with NLP algorithms. In addition, improving the quality of patient data also frees up medical professionals from administrative tasks, reduces stress, and allows them to devote the most amount of time possible to patient care. In fact, NLP is a potent approach for addressing Electronic Health Record (EHR) discomfort. Many medical service providers use it in place of typing and handwritten notes. The potential for automation in healthcare has increased thanks to NLP. By precisely and swiftly auto filling the necessary data, it has aided healthcare workers. Additionally, it can offer advice and find errors in technical documentation, reducing operational costs for healthcare businesses.

Machine learning helps in classifying lesions into benign and malignant or normal and abnormal when it comes to medical picture analysis. Radiologists may utilize ML algorithms to quickly discern between healthy and cancerous cells, enabling them to give patients radiotherapies that are both extremely safe and powerful. Nowadays, robotic surgery that incorporates machine learning is prevalent. Surgery is a lengthy process that calls for a great deal of knowledge. A minor error during some procedures, such as neurosurgery, can even result in death. In such cases, using robots to do effective surgery seems to be a sure thing.

CONCLUSION

Though there are a lot of applications of both technologies, there is still relatively less adoption. This may be due to the challenges like lacking advanced capabilities by many healthcare organizations, errors in interpreting the actual clinical text, etc. But still, in the coming years the healthcare sector is going to observe many more possibilities of this technology with more positive health outcomes.