Top 5 Applications of Machine Learning In Healthcare Sector

Author: Umar Bajwa

In the healthcare sector, if we say that there are so many manual processes still, it would be no wrong. Similarly, as the population is growing, there seem to be rising challenges especially when it comes to recording and evaluating the sufficient amount of information regarding the patients. This is where machine learning ML in healthcare sector offers the way to discover and process the data without the need for any manual process, making the healthcare system further dynamic. The results from the machine learning cannot be doubted as the results have always been witnessed as effective ones.

Knowing that the healthcare sector is full of data coming in from the research and development, patients, clinics and physicians, etc. machine learning has become a major part of the sector to manage the issue of synchronizing the information and making use of it with an aim to bring improvement in the infrastructure and treatments.

"The main motive of machine learning is to ensure making machine efficient, prosperous as well as reliable than it has ever been"

Although machine learning is an equal tool to the doctor’s brain, as a patient one always requires a sensitive human touch, meaning that the technology cannot replace the need for doctors. But the automated machine with the following, 5 applications can offer the services to the patients in a a far better way. Below are the 5 machine learning applications in the healthcare sector;

  • Radiology and machine learning

The introduction of machine learning for radiology gives rise to an algorithm that serves to be capable enough for detecting the differences between both the healthy and cancerous tissues with an aim to bring improvement in the radiation treatments. Furthermore, this also brings improvement in increasing the accuracy when planning the therapy.

  • Clinical researches and trials

The machine learning for the clinical trials is equipped with an aim to pay greater attention towards the development of treatments. Hence, costing both the time and money. Having this applied results in the real-time monitoring as well as the robust services. When applied, this offers the ultimate benefit of conducting remote monitoring and a safer environment for the patients.

  • Personalized treatment for behavior modification

This has been referred to as one of the great advancements in the healthcare sector aimed to offer better services depending on the individual health data. The utilization of machine learning for personalized treatments ensure the development of the system to diagnose the disease via considering the symptoms as well as the genetic information. For the development of the system, healthcare providers make use of the machine learning algorithm, letting everybody diagnose what they are suffering from on their own. Thereby, reducing the cost of healthcare.

  • Drug discovery

Drug discovery is also among the growing technologies in machine learning as it helps in bringing the learnings into precision medicine. A majority of pharmaceutical companies in the healthcare sector are into adopting drug discovery with an ultimate objective to make use of artificial intelligence and speeding up of the process, thereby minimizing the failure rate.

  • Smart electronic health recorder

The two essential machine learning based technologies namely, document classification and optical character recognition, are helpful in advancing both the collection as well as digitization of the information in the healthcare sector. The development of this application is to produce a system as per which the queries of the patients are dealt with via email, thereby establishing a safer and easily accessible system. Furthermore, making use of the technologies is also a solution for preventing the errors that may occur, i.e. the duplication of data.

Machine learning has become a part of us due to the enhanced benefits it offers. Concerning the healthcare sector, it is not so wide-ranging due to the scarcity of data as well as the complexity in the medical field. How do you see MI in the healthcare sector? Feel free to share with us in the comment section as below!