AI And Machine Learning Could Help Predict Health Outcomes And Improve Longevity, Says Expert - medtigo



AI And Machine Learning Could Help Predict Health Outcomes And Improve Longevity, Says Expert

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Artificial intelligence (AI) and machine learning (ML) have made significant advancements in recent years, and their application in the healthcare industry is gaining momentum.

One of the areas where these technologies could be particularly useful is in predicting specific health outcomes for individuals based on medical data collected from large populations. By leveraging the power of AI and ML, healthcare providers can potentially identify individuals who are at higher risk of developing certain diseases, enabling them to take proactive measures to prevent or treat those conditions.

This approach has the potential to revolutionize healthcare by improving patient outcomes, reducing healthcare costs, and promoting a more personalized approach to medical care. In this context, the use of AI and ML systems represents a promising avenue for improving healthcare delivery and promoting better health outcomes for individuals.  

Dr Beena Ahmed, an Associate Professor at UNSW Sydney and an expert in applying machine learning and remote monitoring in healthcare and therapeutic applications, believes that using artificial intelligence (AI) and machine learning on large-scale medical data could help people live longer in the future.

Speaking ahead of the UNSW Engineering the Future event, ‘Technology and longevity – how far can we go?’, Dr Ahmed highlights that by collecting and analysing large amounts of data using AI, it may be possible to predict a person’s potential health outcomes. Similar to how ChatGPT predicts text content based on its huge dataset of words and natural human dialogues, AI systems could make predictions on a person’s health outcomes based on data collected and analysed quickly and efficiently.  

Dr. Ahmed’s research published by UNSW Sydney focuses on monitoring health in a non-invasive way, which includes monitoring from sensors on the body, activities being done, and even from a person’s speech. She believes that society has been reactive in terms of healthcare, only taking action when something is wrong. However, medical research has shown that what people do in their 20s, 30s, and 40s has a big influence on outcomes in later life. Therefore, it is becoming ever more important to monitor health at every stage.  

Although data collection has improved, there is currently no way of analysing massive amounts of medical data over a longer period of time. Dr. Ahmed suggests that the immense power of AI is not being put to use to help health professionals comprehensively evaluate and interpret such information.

While some medical researchers are building individual AI systems to analyse certain images for certain diseases, such as types of cancer, this approach is limited to a small number of hospitals. What would be transformative would be for all the medical and health data that is currently being collected to be brought together and for an AI system to be built that can analyse everything as a whole.  

Dr. Ahmed believes that applying the same sort of models used in that kind of artificial intelligence and machine learning to medical information could predict major health issues for an individual person based on their readings at any given time. For example, assessing that someone has a much greater chance of having a heart attack in the near future and treating them to prevent the heart attack from happening.  

Dr. Beena Ahmed, who specializes in machine learning and remote monitoring in healthcare, has emphasized the importance of data protection in the collection and sharing of medical information. She believes that the government or an autonomous body needs to take control and implement guidelines to ensure that the data collected is shared safely but easily. Without proper data protection, it is difficult to develop long-term systems to ensure people’s health.


Dr. Ahmed’s own work has utilized machine learning in detecting errors in speech and predicting the risk of dementia, as well as monitoring sleep and predicting mental stress levels using wearable sensors. She envisions even more intelligent ways of monitoring people’s health in the future, including lab-on-a-chip technology that could be implanted inside the body to continuously test and monitor health. However, research is still being done on how such a device could be made and powered over a long period of time. 


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