Machine Learning as a service is all poised to shift healthcare towards a healthier future. It is the next big thing in the healthcare industry as providers start to adopt advanced data analytics capabilities.

The development of machine learning and predictive analytics is likely to reach $19.5 billion by 2025, as per Research and Markets. As the Internet of Things begin to bring huge data into the healthcare environment, providers will start to use this information to drive down costs and improve the patient experience.

Due to the high variety, variable, velocity and unstandardized nature of IoT data, advanced machine learning capabilities will be required to generate multiple data streams and actionable insights that can be used to directly improve results and streamline the process of delivering patient care.

Implementing Machine Learning in a Medical ecosystem.

Machine Learning provides methods, techniques, and tools that can help solve diagnostic and prognostic problems in a variety of medical domains. It is being used to analyze important clinical parameters, prediction of disease progression, data mining of medical research, and for overall patient management.

Successful implementation of ML methods provides opportunities to facilitate and enhance the work efficiency of medical experts and quality of medical care.

Below are some major ML application areas in medicine.

1. Personalized medical treatment

Predictive analytics and Machine learning is offering a wide range of applications in the future that will change the dynamics of healthcare.
Individual and personalized healthcare treatments are based on their medical history, genetic lineage, past conditions, diet, stress levels, and more.
This will impact in high stake treatments like cancer, chemotherapy etc. where every patient’s treatment is based on their age, gender, stage of advancement of the disease etc.

2. Autonomous treatment or recommendations

Still in the trial stages, autonomous treatment will be an opportunity for providers to monitor, adjust and disperse the medications by tracking the patient’s data about their blood, sleep, diet etc. It will automatically regulate and take corrective actions like calling the doctors in case of emergencies.

Take the example of insulin pumps that work autonomously, constantly monitor blood sugar levels and inject insulin as needed, without disturbing the user’s daily life.
The legal constraint of having so much power based on algorithms are not trivial, and like any other innovation in healthcare, autonomous treatments of any kind will be scrutinized to prove their viability, safety, and advantage over other treatment methods.

3. Improving performance (beyond amelioration)

While contemporary medicine is primarily focused on treatment and prevention of disease, the need is for proactive healthcare prevention and intervention. Driving this change is the IoT devices, notably the wearable technologies like Fitbit, smart watches etc. that push for this initiative.

Machine learning and artificial intelligence can be leveraged to not only monitor health related parameters like BP, blood sugar etc. but also to track work related aspect like job stress levels and seek positive improvement in high risk groups.

4. Autonomous robotic surgery

At present, robotic surgical system like the da Vinci Surgical System are still in the nascent stages of complex assisted surgery using minimally invasive approach and is controlled by a surgeon from a console. They have the dexterity and trained ability of a surgeon and are commonly used for cardiac valve repair and gynecologic surgical procedures.

Machine learning, in the future could be used to combine visual data and motor patterns within devices such as the da Vinci to allow machines to master surgeries. These machine learning algorithms could learn as many surgical procedures with enough training to better perform medical procedures.

5. Virtual Assistants and Machine Learning

Apple’s Siri, Google’s Assistant and Microsoft’s Cortana have successfully made voice-enabled devices popular with the smartphone users, including healthcare professionals and patients. Powered by speech and text recognition software, machine learning and artificial intelligence (AI), these virtual assistants can interact with users and help them with day-to-day tasks.
These voice-based assistant devices collect data through verbal interaction with the patients. Various platforms such as Echo Dot incorporate these capabilities and features to collect the necessary data that can benefit patients and healthcare professionals.