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How AI Optimization Can Help Shape the Future of Healthcare

Sector to sector, the use of AI is evolving incredibly fast when it comes to augmenting processes, meeting consumer needs, and tackling complex business challenges. In the healthcare industry, building efficient and explainable AI without sacrificing accuracy and trust is key. 

There are arguments in the industry about whether AI needs to be explainable in order to be used in high-risk areas such as healthcare. Some say that explainable AI can help promote trust with healthcare workers, provide transparency into the AI decision-making process, and potentially mitigate various kinds of bias. Others see this approach as driving false hope and reaching for the wrong decisions.

What Does AI Optimization mean for the Healthcare Industry?

We believe there are areas where the deployment of scalable AI and Machine Learning can enhance the end-to-end process in the healthcare industry. Below are two ways AI optimization can help shape the future of healthcare. 

Augmenting Medical Image Readings 

According to recent studies, over the last two years, the healthcare sector has lost between 20% and 30% of its workforce. The World Economic Forum, quoting the International Council of Nurses, says that there could be a shortage of 13 million nurses by 2030. This is where AI can play an important role in offloading pressure from healthcare professionals. According to an IDC report, the use of AI for reading images to assist with the diagnosis was cited among the top three use cases by 30% of the organizations. By adopting intelligent AI models, the image reading process can be sped up, meaning that higher volumes of patients can be managed. 

These models can also achieve higher accuracy than healthcare professionals alone. And, in some cases, identify irregularities not easily visible to the human eye. In addition, AI technology can help integrate these patients’ medical images with other historical data, providing a holistic view of the patient’s medical history.

Augmenting Medical Image Readings  

By creating accurate AI models, healthcare practitioners can predict which patients are more at risk of being readmitted and which treatments they should plan for. Through the use of AI optimization, health professionals can build accurate and explainable AI at scale, enabling them to make better life-critical decisions with confidence.  

The Slow Adoption of AI 

According to the same IDC report mentioned above, one of the issues preventing wider adoption of AI in healthcare is the quality, accuracy, and accessibility of data: 46% of people interviewed cited data volume and confidence in the data as critical factors for a successful AI adoption while 26% reported a lack of adequate volumes of quality training data, concern over trustworthiness/bias of data, and limited access to computing resources as key barriers to its success. 

But it doesn’t have to be this way: achieving high-quality data is possible and certainly within our reach now, not something for the future.

With AI optimization, businesses can generate scalable AI with high accuracy and efficiency. This can help businesses speed up the end-to-end data science process from months to weeks; optimize model efficiency for quicker inference speed, lower memory, and energy consumption, reducing carbon emissions. 

More should be done to widen the adoption of AI in the healthcare sector it is notorious for trailing behind. A McKinsey report, for example, says that the role AI would have on reducing time spent on routine, administrative tasks – which can take up to 70% of a healthcare worker’s time – would not only be uncontroversial but also help with speeding up adoption. 

The advantages for embracing AI in the healthcare sector are clear.


The bottom line with AI Optimization

Ultimately, AI optimization can be used in a variety of ways to shape the future of healthcare. The industry must, however, look at the adoption of this technology as a point of priority. The knowledge and expertise exist but COVID admissions, and now a focus by hospitals to get back to full working capacity, understandably see an implementation of this type of technology fall by the wayside. 

With clear benefits and easy-to-implement adoption highlighted, it is likely we will see these efficient and accurate models deployed more readily around our healthcare institutions.

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