Science & Technology, Australia (Commonwealth Union) – The field of artificial intelligence (AI) is rapidly expanding and is being applied to a wide variety of areas, including healthcare. Population health, which draws its attention towards enhancing the health of entire communities or populations, is one area where AI is increasingly applied to drive innovation and improve outcomes.
A primary benefit of using AI in population health is the ability to analyze vast amounts of data quickly and accurately. With the growth of electronic health records and other digital health data sources, there is an unprecedented amount of health data available for analysis. AI algorithms can be used to identify patterns and insights in this data that would be difficult or impossible for humans to discern.
Dr Beena Ahmed, an Associate Professor at the University of New South Wales (UNSW) has indicated that the application of AI and machine learning on large-scale medical data may be the key to assisting us in having a longer life in the future.
Dr Ahmed, has expertise in applying machine learning and remote monitoring in healthcare and therapeutic applications and further indicated that
the method may be similar to ChatGPT, that predicts in terms of text content based on its large dataset of words along with natural human dialogues.
AI can also be used to enhance the accuracy of disease diagnosis and prediction. Machine learning algorithms can be trained on large datasets to identify patterns and risk factors associated with specific diseases or conditions. This information can be used to develop predictive models that can help clinicians identify patients who are at risk of developing a particular disease or condition, allowing for early intervention and improved outcomes.
In addition, AI can be used to optimize clinical workflows and improve care delivery. For example, natural language processing (NLP) can be used to extract information from clinical notes and other unstructured data sources, allowing clinicians to quickly access relevant patient information. AI-powered decision support tools can also help clinicians make more informed treatment decisions, improving patient outcomes and reducing costs.
There are, however, also potential challenges associated with the rise of AI in population health. One concern is the potential for bias in AI algorithms, which can result in differential treatment or outcomes for different populations. It is critical that AI algorithms are developed and trained using diverse datasets to ensure that they are unbiased and do not perpetuate health disparities.
Another challenge pointed out by Dr Ahmed is that with data collection and analysis in the medical field is the issue of privacy.
“I think this is something the government or another autonomous body needs to take control of and implement guidelines that everyone must follow. At the moment the data belongs to whoever is collecting it, and that is very often a tech company who can then just sell that information to the highest bidder,” said Dr. Ahmed.
“If governments are really serious about ensuring their people live healthier lives longer, we need to fast-track that data protection issue. Without that, there’s no way you can develop systems that can actually be properly implemented over the long-term to ensure that people stay healthy.”
The rise of AI in population health has the potential to transform the field and improve outcomes for patients and communities. By leveraging the power of AI to analyze data, improve diagnosis and prediction, and optimize care delivery, we can create a future where healthcare is more efficient, effective, and equitable. However, the potential challenges as indicated by experts in the field will need to be addressed to ensure that AI is developed and used in a way that prioritizes the needs and well-being of patients and communities.
