- Apr 11, 2018
Healthcare is one such industry that has always been open to the adoption of technological advances and has, in turn, greatly benefited from it.
Today, machine learning or ML (which is a subset of AI (Artificial Intelligence) is being used in several health-related areas, which include treating various chronic diseases, handling patient data & records, and developing new medical procedures.
Also, as per a survey by Healthcare IT News, 63% of healthcare professionals agree that AI and ML already deliver value when it comes to specialty care including pharma, pathology, and radiology.
From playing a major part in patient care, medical records, and billing, today, technology is enabling healthcare providers to offer smart healthcare, create alternate staffing models, reduce administrative & supply costs, and IP capitalization.
All these prove that ML is seeing steady acceptance within the healthcare sector. Moreover, as healthcare data increases, more researchers are starting to explore the benefits of AI and ML. Even physicians are trying to learn how they can leverage new technologies in their practice.
With that said, here are the top five applications of AI & ML in healthcare:
1. Detecting Diseases & Diagnosis
One key advantage of Machine Learning for Healthcare Applications is the detection and diagnosis of certain ailments and diseases that are otherwise often difficult to diagnose.
These types of advanced technology can not only help in detecting cancers that are generally hard to spot during the early stages, but also some other genetic diseases.
PATHAI is a chief example of how employing machine learning can help pathologists in making quick and accurate diagnoses and also in identifying patients that may benefit from the latest types of therapies or treatments. Quantitative Insights is also leveraging ML to diagnose breast cancer more speedily and accurately.
AI has also made its way into the radiology realm as it did with other medical fields. Artificial Intelligence algorithms, specifically deep learning, have shown exceptional progress in various image-recognition tasks.
Traditional radiology practice requires physicians to visually assess medical images for detecting, characterizing, and monitoring diseases. This often had the possibility of making manual errors.
Fortunately, the applications of AI in radiology has brought about a massive transformation in the radiological processes. Now, with the help of deep learning algorithms, physicians can extract useful insights from the medical images to make quick diagnoses.
Computer Vision, a breakthrough technology powered by both ML and deep learning has found acceptance in Microsoft’s research project called InnerEye.
The InnerEye initiative employs ML to produce 3D radiological images for detecting the exact location of tumors and enabling more precisely targeted radiotherapy. Several medical imaging software vendors have already begun employing AI in their devices. For instance, Daisee, an AI software company, is creating a deep learning tool to expedite and enhance the accuracy of brain scan diagnoses.
3. Robotic Surgery
This is among the crucial benchmark machine learning healthcare applications.
In the OR (Operating Room), it’s essential for surgeons to be always precise when performing incisions or other surgical tasks. However, surgical procedures are physically demanding.
To aid surgeons in the OR, AI & collaborative robots are being employed within the medical field nowadays.
Thanks to deep learning data & complex algorithms, AI can learn patterns within surgical operations to refine best practices and improve the accuracy of surgical robots to submillimeter precision. Suturing is another area where AI and deep learning have found their applications. Automation can help reduce surgeon fatigue by shortening the surgical procedure length.
4. Personalized Medicine
This is one of the important machine learning healthcare applications. ML helps in offering customize treatments that are efficient and effective as it pairs individual health and predictive analytics. It’s also helpful for further research as well as a better analysis of the disease.
Traditionally, treatments were typically symptoms-specific instead of patient-specific. This means patients with identical symptoms were given the same treatment and offered the same medications.
However, with the evolution of technology, medical methods, diagnostic tools, and treatments underwent a huge transformation. Today, patients with similar conditions don’t receive the same medications.
It’s important to understand that though the symptoms might be identical or generic, every patient is different. As such, the health structure and needs of one patient will be different from another.
The use of AI and ML in healthcare has made it possible for physicians to interpret, transform, and examine patient data to gather information and accordingly prescribe personalized medicine to each patient. IBM Watson Oncology, for instance, leverages patient’s medical history and helps in generating different treatment options.
5. Smart Health Records
One of the most exhaustive processes in healthcare is maintaining accurate, updated health records. Also, while technology has done its part in streamlining the process of data entry, it still takes up plenty of time to get most of the processes done.
In healthcare, machine learning has found its application in easing the processes, thereby saving time, money, and effort. Document classification techniques using ML-enabled OCR recognition methods and vector machines are gaining steam steadily.
The use of such advanced technology is seen in many healthcare companies for managing health data nowadays. For instance, Ciox Health uses ML for improving health data management and exchanging health information.
Additionally, KENCI employs machine learning and predicts diseases and medications to help doctors and payers intervene sooner. MIT is working towards developing the next generation of sophisticated, smart health records by incorporating ML-based tools to help with clinical treatment recommendations, diagnosis, etc.
In today’s digitalized world, AI and ML play a significantly major role in our everyday life. These sophisticated advanced technologies are being applied in a variety of domains, including weather forecasting, sales prediction, marketing applications, and of course healthcare.
By now, you’re probably aware that machine learning has already found its application in a number of health-related fields and has significantly transformed and eased the way physicians and healthcare professionals offer care to patients. This is an indication that the healthcare sector will likely see more widespread adoption of such technology in the years to come.