Maximizing Patient Care using Artificial Intelligence

Maximizing Patient Care using Artificial Intelligence
January, 04 2023

Maximizing Patient Care using Artificial Intelligence

Interpretable AI plays a big role in improving healthcare for patients. Also known as explainable AI, this term denotes a scenario where AI decisions and predictions may be clearly understood by humans.

Rapid Diagnosis

Timeliness in care of patients is of utmost importance. AI performs rapid diagnoses that help speed up patient treatment. Things where doctors may sometimes need a lot of data to process, AI can do much faster. AI has helped predict which individuals are at risk of sepsis or even a low-pressure event in surgery.

Patients who are at high-risk for disease or have co-morbidities can be better assisted through AI’s prediction models.

AI helps reduce overall hospitalization time and can suggest the correct treatments based on the larger pool of data it has analyzed.

Diagnosis Imaging

In recent years, artificial intelligence (AI) and machine learning (ML) have become increasingly important in diagnosis imaging.

According to a review of AI/ML-based medical devices approved in the US and Europe from 2015-2020, more than half of the approved or CE marked devices (129 in the US and 126 in Europe) were for radiological use.

Studies have shown that AI can perform at or exceed the level of human experts in image-based diagnoses for various medical specialties. For example, a convolutional neural network (CNN) trained with labeled frontal chest X-ray images outperformed radiologists in detecting pneumonia. Similarly, a CNN trained with clinical images was able to accurately classify skin lesions in dermatology, and an AI algorithm trained with whole-slide pathology images was able to detect lymph node metastases of breast cancer with results comparable to those of pathologists.

In cardiology, a deep learning algorithm was able to diagnose heart attacks with a performance comparable to that of cardiologists.

There are already some successful examples of AI-based diagnostic imaging in the National Health Service (NHS), such as the University of Leeds Virtual Pathology Project and the National Pathology Imaging Co-operative. It is expected that the widespread adoption and scaling up of these technologies will occur in the medium term.

Speedy Data Processing

AI is also ideal for catching irregularities in 60 million compliance investigation workflows that happen in an average hospital in the US.

If a health security officer were to perform the same task, they would need to deeply examine 300,000 records per hour, daily. This high level of performance by AI would require 23,000 professionals daily.

AI Screening for Diabetic Retniopathy

Diabetic retinopathy is a common complication of diabetes that can lead to vision loss. One way to help prevent this is by screening people with diabetes for the condition and treating it promptly. However, this can be costly due to the large number of people with diabetes and the limited number of eye care professionals available.

Artificial intelligence (AI) algorithms have been developed to help screen for diabetic retinopathy. Studies in the US, Singapore, Thailand, and India have shown that these AI algorithms are accurate and cost-effective. In fact, the Centers for Medicare & Medicaid Services in the US has approved the use of an AI algorithm called IDx-DR for Medicare reimbursement. This algorithm has shown to be 87% sensitive and 90% specific in detecting diabetic retinopathy.

AI Patient Twins

A really interesting long-term solution for patients would be the “AI Digital consult” – which would examine not the patient, but the patient’s AI twin. This would allow the healthcare professional to test out effectiveness of certain procedures or even medicines.

In severe cancer cases, having the AI twin patient undergo the treatment would allow for a safer and more accurate treatment.

AI in patient care is still in its early stages, but the future looks promising. With the development of advanced AI algorithms and their successful implementation in various healthcare settings, it is clear that AI has the potential to greatly improve patient care and make it more accessible and efficient.

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