AI is revolutionizing the medical industry.
The health care sector has been one of the fastest supporters and users of AI technologies, where its application is literally life-changing. It has been predicted that Medical AI will be a $ 200 billion industry by 2025 and it’s easy to see why. There has been an exponential increase in the amount of medical data being produced over the past few decades with estimation of accessible health-related data to be over 40 zettabytes (40 trillion gigabytes!). It’s obviously just not possible for any human practitioner to keep with this increase in data.
We are already seeing AI solutions being used in a variety of ways, including AI assisted medical robots, AI interactive apps and most notably using AI platforms to help diagnose illnesses based on medical images, such as CT scans, X-rays and MRIs. This field, in particular, has made tremendous strides in the past few years and, in fact, AI platforms are now able to more accurately diagnose a wide range of cases based on medical images than their human counterparts. Although implementing AI solutions in healthcare is a fairly new trend, it’s already becoming clear that this is an exciting area that may be hugely beneficial in terms of saving lives.
Although AI is being applied to solve and optimize a wide range of applications, there are of course some important points that need to be taken into consideration when implementing an AI solution in smaller scale niche markets, for example, accessibility and human-AI interaction.
This becomes relevant when trying to increase usage and expand the areas of application to a general market. It is important to have user-friendly interfaces that can help mediate the interaction and ensure correct deployment of the AI system.
Trustable AI is another important factor. This is vital when using such systems for delicate and intricate applications, especially when lives may be at stake as in the case of medical AI. Trustability is also closely linked to how transparent and understandable the operation of the AI is. As not all end users will be advanced programmers, such technical aspects can contribute to a bottleneck in distributing and propagating wider use of AI. Therefore, it is becoming more important for solution providers to develop user-friendly, easily integrated, and understandable systems. Unfortunately, such improved systems have widely been out of reach, until recently…
inwinSTACK Announces the Launch of ARI...
ARI, the latest development by inwinSTACK, has been specifically developed for medical practitioners and focuses on data sets, such as X-rays, CT scans, and MRIs. What is most exciting about the launch of ARI, is that this application contains at least three new features that are not only expected to greatly improve this appliance’s performance, but also ensures a more accessible and trustable platform. For more information, please visit: WWW.LINKEDIN.COM/COMPANY/INWINSTACK
As mentioned above, AI platforms can sometimes suffer from the ‘black box’ problem where lack of transparency and explanation on how the AI arrived at its decision can lead to end users doubting the outcomes. This is not the case for ARI, as inwinSTACK has gone through great lengths to make models and algorithms more transparent and explainable. This leads to users having a clearer idea of how the outcomes are generated, and thus a more trustable system.
The machine learning algorithms that AI applications apply in their operation is another vital component to assess their capabilities and effectiveness. Traditional systems have used a centralized technique where data gets uploaded to a single server or machine, then used for training. This is however not desirable when sensitive personal data is being used. ARI utilizes a more recent approach called federated learning, which allows learning across several devices or servers without the need to exchange or upload data sets to a central server. This ensures a high level of security of sensitive or personal data yet at the same time maximizes the ability to learn from heterogeneous noncentralized data sets.
Lifecycle Management: MLOps.
Although the machine learning aspect of AI appliances is a mature field, employment of such appliances is not always easy to accomplish in practice. This is because the entire life cycle of the AI requires expertise from both data scientists and operational engineers. ARI circumvents this problem by incorporating a protocol ensuring the operation is efficient, well documented, and easy to troubleshoot, the so-called MLOPs practice. This ensures that once implemented, ARIs life cycle can be properly understood, managed, and streamlined.
Future of AI in Healthcare
We are currently seeing an exponential growth in the use of AI in healthcare, and there is a serious need for new appliances that can operate efficiently, are easy to implement. With the recent new features, such as MLOps Lifecycle Management, Federated Learning, and Explainability, ARI possesses several competitive advantages over contemporary systems and offers a comprehensive, end-to-end AI server which combines hardware and open-source application stacks. This allows for users to quickly start and launch their machines, literally within minutes.
Thanks to these developments getting an AI solution up-and-running is now faster, easier, and more secure. The emergent field of AI in healthcare is exciting as it promises some life-changing solutions, and it’s very clear that we are only just scratching the surface of the potential of this technology. With ARI on our side, we are one step closer to realizing the true transformative power of AI in healthcare.