5 ways in which machine learning can become a game-changer in the healthcare sector

Published: 2019-11-22 11:43:26

The healthcare sector is one of the biggest names in the list of investors for digital solutions over the past decade or so. But, a key thing to note here is that most of these investments have been on electronic medical records and making healthcare operations more sturdy and reliable. While these investments have borne the fruit, the impact it has on patients have quite turned out into a routine affair rather than something innovative. So what’s next for healthcare innovation? What technology platform will reinvent care delivery from traditional service models? The answer lies in machine learning and its larger umbrella – artificial intelligence. No, we are not talking about humanoids or artificial brains, but how digital systems being adopted across the healthcare spectrum can be tweaked with machine learning to drive more value for healthcare providers and patients.
 
Let’s explore 5 ways in which machine learning can empower the healthcare sector to transform service delivery:
 
**Intelligent Robotic Surgery** 
Globally, the demand for robotic surgery is on the rise primarily due to the precision it offers for incisions and hence ultimately lower hospital stays for patients after surgery for healing. It has already proven to reduce hospital stay time by [21% according to studies](https://hbr.org/2018/05/10-promising-ai-applications-in-health-care). If you are to introduce machine learning in the picture, then a whole new dimension of possibilities are opened up for surgical procedures. By learning heaps of robotic surgical data from past procedures, intelligent algorithms can enable more accurate incision patterns and surgical techniques that require minimal cuts and stitches. Machine learning-enabled tools can also empower doctors to have greater control of surgical procedures in handling complex cases.

**Clinical Analysis**
From running advanced analysis on blood sample observations to decoding patterns within tumorous cells, machine learning can be the best friend of doctors to gauge precisely the medical condition of a patient. Powerful AI-enabled tools can simultaneously compare specimen behavior with millions of recorded medical history archives swiftly and offer insights into matching patterns of illness diagnosis. It might take current laboratory research and observation specialist days to compare and diagnose conditions that [AI-enabled digital tools](https://www.fingent.com/blog/digital-tools-for-the-future-of-healthcare-providers) can find in seconds. This is a perfect example of how man and machine (machine learning) can collaborate to deliver value for all. These are the stories that need to be portrayed in contrast to the regular Hollywood themes of machines taking over mankind abruptly. The system has been recognized as such a worthy solution for medical diagnostics, that governments across the world are launching programs that involve regional health agencies incorporating AI-enabled research to boost clinical diagnostics performance and ultimately predict health status of citizens.
 
**Training and Information Discovery**
No matter how much ever technology evolves, a human doctor can never be replaced by machines or software bots. But on the contrary, this software can help make a doctor’s life much easier. Experience in medical procedures is a major criterion of how doctors build their skills and they constantly need to become knowledgeable of the latest happenings in their field of specialization in order to treat patients continuously. This is where machine learning can help doctors identify relevant research and knowledge materials from millions of medical records that may be available in different parts of the world. For a normal person, having to find relevant information from books and records that are so huge in number would be a Herculean task, but for a software bot, it takes only a couple of minutes maximum. This capability can help significantly in training and learning sessions organized for doctors, wherein detailed insights about medical practices being followed across the globe can be taught much better with relevant examples.
 
**Medical Imaging** 
We have progressed significantly in medical imaging since the days of X-rays and today there are different imaging techniques that provide sharp and crystal-clear details on how various body functions occur over different conditions. But the ability to detect suspicious behavior of tissues, cells, etc. is often a painstaking job performed manually by doctors and imaging professionals. Machine learning can make this process easier and more accurate. Modern digital tools equipped with machine learning can easily identify anomalies from imaging records such as an advanced MRI of the brain or kidney or the spinal cord. It can identify potential patterns of deadly conditions such as cancerous growth in the early stages itself. It is always possible for the human eye to lose sight of microscopic details of a medical image, but trained AI algorithms will not miss these patterns. This can prove to be a critical application of machine learning as it will help identify potentially fatal diseases in earlier stages and help patients acquire necessary treatment at the earliest to avoid any consequences.
 
**Smart Assistants** 
AI can be the best nurse for patients in an advisory role. Today there are thousands of smart wearable devices that people can purchase and wear on them. These devices can measure all vital body parameters from as simple as pulse rate to even blood sample evaluations. By tracking patterns in your body during activities such as breathing rate, pulse rate, and others, machine learning-enabled assistants can identify your optimal health condition. It can advise you to have a check on activities if anomalies are detected from usual patterns. For example, if the level of fatigue or tiredness increases exponentially while walking or jogging for a shorter distance than historically observed data, it could be an indication of declining health or stamina. Another aspect for intelligent assistants is when patients can chat with an AI assistant on their phones to know about potential ailments without paying a visit to the nearest clinic. They can upload results of blood tests and their physical activities that are tracked by wearables and the AI assistant would then run a powerful analysis of these results. By comparing them with millions of reference cases available across medical databases, the assistant can provide insights into how the person’s health parameters are doing now.
 
As you can see, the possibilities of machine learning and Artificial Intelligence in the healthcare sector are numerous. From becoming virtual nurses to patients to enabling doctors to diagnose fatal medical conditions faster, there is a wide scope of applications that AI and machine learning can find in this sector. While concerns about data security and patient privacy are always a question when it comes to research activities, machine learning can guarantee a safe and secure environment for analysis of patient data since it is fully automated and not controlled by human intervention. In the coming years, more consumer-facing healthcare initiatives will witness the proliferation of machine learning and AI and the results will be beneficial for patients as well as the industry as a whole.

**Author Bio:**

Tony believes in building technology around processes, rather than building processes around technology. He specializes in [custom software development in USA](https://www.fingent.com/), especially in analyzing processes, refining it and then building technology around it. He works with clients on a daily basis to understand and analyze their operational structure, discover (and not invent) key improvement areas and come up with technology solutions to deliver an efficient process.