Will AI Prevent You From Having A Heart Attack?
Preventative medicine is all the rage. It can cut long-term healthcare costs, help patients avoid painful procedures, and overall drives longer, better quality lives. So when does preventative medicine make the move from yearly checkups and traditional tests like mammograms and colonoscopies to something more high tech? We may be standing at that turning point right now.
Several new AI tools are currently being introduced that have the potential to detect future heart attack risks or even prioritize and direct emergency calls based on the likelihood the patient is having a cardiac event. Check out these exciting projects that are working to make heart attack deaths a thing of the past.
We call emergency services because we need immediate care, but not all emergency calls are as urgent as others. That’s why, in an attempt to prioritize call responses, four European countries are rolling out an AI algorithm that can detect cardiac arrest with up to 95% accuracy. In studies by the AI’s developer, human dispatchers could only perform this task with about 74% accuracy.
Patients experiencing cardiac arrest need the quickest care if they hope to be resuscitated, which is why it’s important for dispatchers to quickly and accurately assess incoming calls. With AI directing calls, more patients can receive expedited care, preventing brain and organ damage or death.
Decision-making AI is one of the main new frontiers in machine-enhanced medicine, and this dispatcher system is just one example. Other AI programs include an Israeli program for detecting intracranial hemorrhages, as well as one designed by Stanford scientists, which can identify cancerous skin markings.
Though there will always be an art to diagnosis, especially for rare conditions, AI is improving outcomes for patients across the globe by helping doctors make better treatment decisions. Despite all the hype, the role of AI is in reducing doctor errors; it doesn’t replace doctors.
It’s obviously important to maximize emergency dispatcher response to cardiac arrests, but what if we could treat heart problems before they became emergent? This is the primary goal of preventative cardiology programs – addressing cardiac risk factors such as high blood pressure, family history, obesity, and metabolic syndromes before they become critical. Recently, though, Google released a new algorithm that looks at an entirely different criterion to assess cardiac risk: the eyes.
What can the eyes tell us about future cardiac health? The back wall of the eye, known as the fundus, is rich in blood vessels. By scanning these vessels, the Google algorithm can detect factors including age, whether or not the individual smokes, blood pressure levels, and more. Right now the algorithm can correctly predict cardiac risk 70% of the time, a rate just slightly lower than the medical standard, the SCORE test, which is about 72% accurate.
The main difference between something like Google’s algorithm and the SCORE test is that the SCORE test is unlikely to become much more accurate, but with greater exposure and testing, Google’s algorithm will become more sensitive to small details. Machine learning makes most algorithms open-ended and constantly in development. The more they “see” and the more feedback they get, the better they perform.
Most medical knowledge is highly generalized. Doctors will tell patients they have an X percent likelihood of having a particular condition, for example, but that number might be based on the entire population – and the population numbers are often based on studies consisting largely of white men. Though we see differing levels of drug effectiveness and disease risk in minority populations in practice, studies and statistics rarely reflect these differences.
In partnership with Microsoft’s AI Network for Healthcare, Apollo Hospitals in India have developed a cardiac assessment AI based on the Indian population. Even in India, home to nearly one-seventh of the world’s population, most of the technologies and assessment tools currently in use were developed based on western populations. This led to errors in diagnosis and poor disease prevention efforts. The Apollo Hospital’s AI is the first of its kind to rethink who is centered in diagnostic technology.
Cardiovascular disease is the leading cause of death globally, and it’s on the rise in low- and middle-income countries as western dietary habits are exported and increasing wealth leads to greater meat consumption and a more sedentary lifestyle. This risk profile makes better disease detection and preventative efforts more important than ever before – and the AI tools are ready to change the game.
Several new AI tools are currently being introduced that have the potential to detect future heart attack risks or even prioritize and direct emergency calls based on the likelihood the patient is having a cardiac event. Check out these exciting projects that are working to make heart attack deaths a thing of the past.
Europe’s AI Decision Driver
We call emergency services because we need immediate care, but not all emergency calls are as urgent as others. That’s why, in an attempt to prioritize call responses, four European countries are rolling out an AI algorithm that can detect cardiac arrest with up to 95% accuracy. In studies by the AI’s developer, human dispatchers could only perform this task with about 74% accuracy.
Patients experiencing cardiac arrest need the quickest care if they hope to be resuscitated, which is why it’s important for dispatchers to quickly and accurately assess incoming calls. With AI directing calls, more patients can receive expedited care, preventing brain and organ damage or death.
Decision-making AI is one of the main new frontiers in machine-enhanced medicine, and this dispatcher system is just one example. Other AI programs include an Israeli program for detecting intracranial hemorrhages, as well as one designed by Stanford scientists, which can identify cancerous skin markings.
Though there will always be an art to diagnosis, especially for rare conditions, AI is improving outcomes for patients across the globe by helping doctors make better treatment decisions. Despite all the hype, the role of AI is in reducing doctor errors; it doesn’t replace doctors.
Predicting Future Disease
It’s obviously important to maximize emergency dispatcher response to cardiac arrests, but what if we could treat heart problems before they became emergent? This is the primary goal of preventative cardiology programs – addressing cardiac risk factors such as high blood pressure, family history, obesity, and metabolic syndromes before they become critical. Recently, though, Google released a new algorithm that looks at an entirely different criterion to assess cardiac risk: the eyes.
What can the eyes tell us about future cardiac health? The back wall of the eye, known as the fundus, is rich in blood vessels. By scanning these vessels, the Google algorithm can detect factors including age, whether or not the individual smokes, blood pressure levels, and more. Right now the algorithm can correctly predict cardiac risk 70% of the time, a rate just slightly lower than the medical standard, the SCORE test, which is about 72% accurate.
The main difference between something like Google’s algorithm and the SCORE test is that the SCORE test is unlikely to become much more accurate, but with greater exposure and testing, Google’s algorithm will become more sensitive to small details. Machine learning makes most algorithms open-ended and constantly in development. The more they “see” and the more feedback they get, the better they perform.
Making Details Matter
Most medical knowledge is highly generalized. Doctors will tell patients they have an X percent likelihood of having a particular condition, for example, but that number might be based on the entire population – and the population numbers are often based on studies consisting largely of white men. Though we see differing levels of drug effectiveness and disease risk in minority populations in practice, studies and statistics rarely reflect these differences.
In partnership with Microsoft’s AI Network for Healthcare, Apollo Hospitals in India have developed a cardiac assessment AI based on the Indian population. Even in India, home to nearly one-seventh of the world’s population, most of the technologies and assessment tools currently in use were developed based on western populations. This led to errors in diagnosis and poor disease prevention efforts. The Apollo Hospital’s AI is the first of its kind to rethink who is centered in diagnostic technology.
Cardiovascular disease is the leading cause of death globally, and it’s on the rise in low- and middle-income countries as western dietary habits are exported and increasing wealth leads to greater meat consumption and a more sedentary lifestyle. This risk profile makes better disease detection and preventative efforts more important than ever before – and the AI tools are ready to change the game.
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