Advancements in healthcare AI have resulted in the creation of several game-changers for remote patient monitoring and medical decision-making, including digital technology that provides valuable patient data in the absence of clinical visits and puts a wealth of knowledge at physicians’ fingertips.
What may have sounded like science fiction years ago is a reality today. From pills that provide feedback about medication adherence or help doctors diagnose patient symptoms, to machine learning applications for data sharing, predictive modeling, and medical decision-making, advancements in healthcare AI deliver a constant source of innovation and excitement.
Here, we will highlight just a few that are or have the promise of becoming game-changers.
Smart Pills That Monitor Medication Adherence
The information is communicated to a wearable patch and shared with a corresponding smartphone app. Patients can review the information with their doctors or, with the patient’s permission, the data can be transmitted to a secure web-based portal where the patient can select which members of the care team and family can view their information.
Technology like this could help physicians track patient compliance with their dosage instructions, such as time and amount of the drug taken, and may encourage prescription adherence.
Ingestible Trackers That Support Diagnoses
Ingestible is also being designed and tested that can take the place of invasive probes to help doctors diagnose certain conditions.
In January 2018, researchers from RMIT University announced the completion of the first human trials of a gas-sensing ingestible capsule that could be used to help detect disorders of the gut - from improper absorption of nutrients to colon cancer.
As reported by RMIT University, the capsule (the size of a vitamin pill) detects and measures gut gases in real-time. The data can then be sent to a smartphone. The capsule could serve as a less invasive option for getting to the bottom of patient symptoms so that the proper treatment can begin sooner.
Machine Learning for Diagnosis and Decision-Making
Machine learning is a subset of artificial intelligence, and deep learning is a subset of machine learning. For this article, we will use the phrase machine learning.
Machine learning has far-reaching applications in healthcare, including enhancing diagnostic imaging, aiding physicians in diagnosing illnesses, extracting data that can help pinpoint the most appropriate course of treatment, and more.
For example, computers that can learn patterns and signs of disease can help physicians:
- Achieve higher diagnostic accuracy in oncology
- Model the progression and treatment of illness, such as cancerous conditions
- Gain insight into who benefits more from certain treatments, and at what stage of their disease they receive the most benefit
- Identify physiological and biometric patterns that can narrow down the list of possible genetic diseases a patient has
- Diagnose rare diseases through a combination of facial recognition software and machine learning
Additionally, with amazing processing speed and memory capacity, these computers can integrate and analyze vast amounts of healthcare data quickly and deliver the most relevant results to support healthcare professionals in medical decision-making.
What Makes Machine Learning Such a Game Changer in Healthcare?
Just imagine how AI could be used to enhance the collection, usability, and dissemination of electronic healthcare data, as well as the wealth of knowledge doctors and other healthcare, thought leaders amasses over their years of experience.
Through machine learning, computers could cull decades of historical data from hundreds of thousands of patients with a particular illness, as well as their corresponding treatments and results. Doctors could then use that data to help pinpoint the course of action that will most likely yield the intended outcome for someone facing that illness.
Machine learning could also be used to streamline the creation of clinical notes and apply filters that make them more accessible and usable. And it could be used to merge multiple sources of healthcare data - electronic medical records, clinical trials, genomic studies, pharmaceutical research, academic literature, etc. - to aid in medical decision-making.
While physicians couldn’t comb through the virtual mountains of data that exist on their own, a computer trained to aggregate, analyze, and deliver relevant data could, in a short time, but the experience of thousands of healthcare professionals worldwide at the physician’s fingertips to assist in the case, a “virtual collaboration” of sorts.
AI That Supports Remote Patient Monitoring and ElderCare
Machine learning also plays a crucial role in digital home monitoring systems and intelligent care management devices. These tools have bolstered the growing trend toward providing patient care at home or on the go. They have also provided many individuals with the opportunity to stay independent for as long as possible and support aging in place. Some examples of these devices include:
- Smart pillboxes with sensors connected to smartphone apps to support the monitoring and tracking of medication adherence
- Bluetooth-enabled systems that track and transmit blood pressure and glucose level readings, allowing for remote vital signs monitoring
- Systems that combine in-home sensors, smart wearable, and predictive analytics to track activity levels, monitor changes in behavior, detect falls, locate someone who might wander from safety, and more
- Diagnostic wearables like “smart bras” and monitors like mobile EKGs that allow doctors to monitor the condition of patients with breast cancer and heart disease without the need for clinic visits
These and other digital home monitoring and care management devices rely on machine learning to properly manage the influx of data and, in some cases, dispatch emergency responders or push the appropriate alerts to physicians, caregivers, and family members.
The Healthcare AI Revolution
These are only a few of the ways in which artificial intelligence is altering our healthcare landscape in positive ways. Artificial intelligence, associated with connected objects and our personal data, also opens the way to a new medicine that will be predictive and customizable. It will be possible to learn for yourself, in connection with health professionals, to prevent or detect the disease more quickly, and to act accordingly. This personalized medicine and this constant monitoring of the state of health will allow us to live better and longer.