The diagnosis of diseases is decisive for planning adequate treatment and guaranteeing the well-being of patients and the artificial intelligence is contributing its small (or large) grain of sand to collaborate in this process.
From a technology viewed with some suspicion, since the statements presented it as the replacement of doctors, the AI has evolved to become the second pair of eyes they needed.
Every year, medical diagnostic errors affect the health of millions of people and cost billions of euros and this type of technology can help identify hidden or complex patterns in diagnostic data to detect diseases earlier and improve treatments.
artificial intelligence has come to break the rules of the game and collaborate with medical teams on diagnosis, as well as medical decision making, management, automation, administration, and collaboration in workflows.
Artificial intelligence and its vast potential in the healthcare sector
AI is a very powerful sector, making use of data, algorithms, analysis, deep learning, neural networks, and knowledge that is constantly evolving and adapting to the needs of each sector, and healthcare is no exception.
A recent McKinsey study reports that currently AI is part of a whole range of areas in this field: apps that help patients manage their care themselves, online symptom checkers, virtual agents that can perform activities in hospitals, and even a bionic pancreas to help patients with diabetes.
the AI it is equally powerful in providing the radiology professional with great support, for example. Today, these professionals have to filter large amounts of images while still prioritizing urgent ones and managing patient care.
This is where artificial intelligence really shines in diagnostics doctor. AI solutions like Aidoc analyze the large number of images and flag those it deems to be of concern. Then, the radiologist can evaluate flagged images as a matter of priority, thus detecting urgent cases faster without compromising their existing workloads or cases.
This solution, for example, has already been implemented in more than 1,000 medical centers around the world. Of course, this is just the tip of the iceberg of a large field to investigate. There is a lot of room for growth and for the technology Learn what you can do to support the medical industry.
These models, adding a new example, could also be used to observe the vital signs of patients receiving intensive care and notify doctors of changes.
While medical devices like heart monitors can do this job, AI can collect the data from those devices and look for future outcomes like sepsis (a sudden immune response to infection). An IBM customer has developed an AI predictive model for preterm infants that is 75% accurate in detecting severe sepsis.
Something that, for example, is already glimpsed in some centers are virtual assistants. In this case, things would go further, and a health care system could offer patients 24-hour access to an AI-powered virtual assistant that could answer questions based on the patient’s medical history, preferences, and personal needs. patient.
AI also contributes to the exhaustion of doctors
In recent years, artificial intelligence in medical diagnostics has shown immense promise in changing the face of medical care and at the same time reduce the extreme pressures that everyone can see every day in the media.
In Medscape’s recent 2022 National Physician Burnout and Suicide Report, statistics pointed to the inherent risks of putting too much pressure on professionals, especially those trying to juggle families, retirement planning, and the complexities of their jobs..47% of doctors revealed that they are exhausted.
Artificial intelligence in diagnostics can not only reduce the pressure on doctors when working with large amounts of information and images, but can also be used to assume a large percentage of the administrative burden.
The scale of many solutions remains small, but their increasing adoption at the system level health indicates that the pace of change is accelerating. In most cases, the question is less whether AI can have an impact and more how to increase the potential.