Blog Summary:
AI is reshaping healthcare with its diverse applications. Early detection and diagnostic assistance, individualized treatment plans, assistance in minimally invasive procedures, patient support and monitoring, and predictive maintenance of medical equipment are key examples. These innovations promise more accurate diagnoses, personalized treatments, and improved patient outcomes, paving the way for a more efficient and effective healthcare system.
Artificial intelligence is an umbrella term that encompasses a wide range of technologies.
From natural language processing (NLP), which may assist with patient support and customer service, to machine learning, which can be used to analyze large amounts of medical data to identify patterns and trends for better patient care, to physical robots which can assist in surgery, there are a number of AI applications in healthcare and examples we could mention.
Here are five of the most important and impactful AI in healthcare examples.
1. Early Detection & Diagnostic Assistance
Early detection of certain conditions and diseases is often key to successful treatment and a positive patient outcome.
One of the most impactful AI use cases in the healthcare industry today is the development of technology that can quickly and thoroughly review patient data and interpret it against large datasets, leading to a greater chance of early detection and more accurate diagnosis.
For instance, one AI program in particular developed by researchers at Houston Methodist Research Institute in Texas “reliably interprets mammograms and translates patient data into diagnostic information 30 times faster than a human doctor, with 99 per cent accuracy.”
2. Individualized Treatment Plans & Decision Making
AI can help provide more accurate diagnoses. It can do so early on in the course of a disease’s progression. But what about treatment? According to PwC, that’s not off the table either:
“AI can help clinicians take a more comprehensive approach for disease management, better coordinate care plans and help patients to better manage and comply with their long-term treatment programmes”
In other words, it’s similar to the way that a physician might consult with other doctors in their field to provide a higher quality of patient care and more effective treatment plan tailored to the individual patient. That same physician might also turn to AI for increased confidence in the efficacy of a given course of treatment, or suggestions on ways to better tailor the treatment to the patient.
3. Assistance with Minimally Invasive Procedures
Medical professionals who regularly perform minimally invasive procedures are already using AI based technologies to provide assistance and improve accuracy during operations.
For instance, Philips’ Image Guided Therapy System, Azurion, “can help analyze CT images to automatically detect large vessel occlusion – a major source of stroke – as well as its location. The software then shares this analysis with physicians at the primary stroke center and at the intervention center where the patient is eventually treated.”
4. Patient Support & Patient Monitoring
Current AI in healthcare examples extend past the time that the patient is cleared to go home.
For instance, as HealthTech Magazine notes, “Medical providers have been able to expand remote patient monitoring because of wearable devices that use AI to track and analyze data such as blood pressure, glucose levels and sleep patterns.”
5. Predictive Maintenance of Medical Equipment
Many of the benefits of the above AI examples in healthcare help either providers or patients directly. But there are plenty of ways that AI is also being used on the administrative and operational side of healthcae.
For instance, AI can minimize downtime by predicting when medical equipment will require maintenance, and in so doing, improve the continuity of care for patients.
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This list of examples of artificial intelligence and machine learning in the healthcare industry is by no means comprehensive.
AI/ML technologies continue to develop on a daily basis, and new use cases are being tested and iterated on regularly. What’s more, there are various other types of AI/ML. These include deep learning AI algorithms, rule-based expert systems, and robotic process automation that we haven’t fully explored.
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