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AI Insights, Insights

AI is rapidly evolving across all industries, especially when it comes to meeting consumer needs and tackling tough business challenges. In the health industry, it’s particularly important to evolve AI accuracy without sacrificing transparency and trust. 

This difficult trade-off can be tackled more easily with AI optimisation, the cutting edge AI technology that enables organisations to build accurate, efficient and transparent AI models to drive better decision making in high-risk areas such as healthcare. 

Here are three ways AI optimisation can help shape the future of healthcare.

 

Enhancing medical image readings

According to recent studies, over the last two years the healthcare sector has lost between 20% and 30% of its workforce. This is where AI can be utilised to offload pressure from healthcare professionals. 

An IDC report explored how AI is also useful in assisting with diagnoses. By adopting accurate and efficient AI models in the diagnosing stage of a patient’s journey, the image-reading process can be sped up and so more patients can be seen and any serious health issues can be detected in time.

Not only can AI ease stress for healthcare staff, but it can even help to enhance their work. For example, AI models can achieve higher accuracy than professionals alone and can spot irregularities not easily visible to the human eye.

 

Predicting hospital readmissions

Reports show that nearly a third of Covid patients who had been discharged from hospital were readmitted within four months. This proves to be extremely costly for the NHS and its Trusts. 

A way to avoid this is to apply AI across the whole patient’s journey. From initial hospital stay to post-discharge. 

By creating accurate and explainable  AI models, practitioners can predict which patients are more at risk of readmission, and make better life-critical decisions with confidence.

 

Accelerating AI adoption

According to the same IDC report mentioned above, issues preventing wider adoption of AI in healthcare included the quality, accuracy and accessibility of data, and limited access to computing resources. There was a lack of trust amongst those interviewed regarding the quality of training data, including biases.

With AI optimisation, healthcare professionals can generate accurate and explainable AI to better understand data quality and detect bisas. Furthermore, AI optimisation can improve model efficiency, which means health organisations can adopt efficient AI that consumes less computing resources whilst reducing their carbon emissions.

 

The bottom line

Ultimately, AI optimisation can be used in a variety of ways to shape the future of healthcare and should be seen as a priority. 

The need for AI is even greater in a post-pandemic world, in order to help get hospitals back to full working capacity.

With clear benefits and its easy adoption highlighted, it is likely we will soon see these AI models deployed more readily around our healthcare institutions.