Understanding the fundamental value of AI in healthcare
Understanding the fundamental value of AI in healthcare

 

 

The era of AI

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The era of AI

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Spending on AI in healthcare is projected to grow at an annualized 48% between 2017 and 2023.

Spending on AI in healthcare is projected to grow at an annualized 48% between 2017 and 2023.

Artificial intelligence, or AI, is a general concept that machines can be taught to mimic human decision-making and learning behaviors. Medical fields that rely on imaging data, including oncology, mammography and neurology, have already begun to benefit from the implementation of AI methods.

One thing is certain: from hospital care to clinical research, drug development and insurance, AI applications can potentially revolutionize how the health sector works to boost productivity and improve patient outcomes. In fact, Business Insider Intelligence reported that spending on AI in healthcare is projected to grow at an annualized 48% between 2017 and 2023¹


Growth of AI in healthcare

+48%

Understanding the AI

The key to a deep learning system is large amounts of data⁠—the more data it is given, the better it performs.

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AI in healthcare mainly refers to applications that are designed to search vast data sets and surface critical patient information. Today’s intelligent systems use artificial neural networks, where computers learn to process raw data from examples rather than explicit programming. These artificial neural networks operate in the same way as the neurons in the human brain.

machine_learning

Through machine learning, a computer system gains the ability to use its neural network to process incoming data, identify patterns and make decisions with minimal human direction.

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A computer system’s ability to processes massive amounts of data quickly and prioritize the critical criteria for reaching a decision is enabled by deep learning. This is a type of machine learning in which systems can accomplish complex tasks by using multiple layers of choices based on the output of the previous layer, creating increasingly smarter and more abstract conclusions. Examples of deep learning at work include more complex AI tasks, such as analyzing images to recognize a face or an anomaly in a medical scan.

Read on and learn how AI applies to the healthcare industry and how it meets industry challenges.

Is AI healthy for healthcare?

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AI is being leveraged in many fields and industries, from finance and telecommunications to transportation. Yet a lot of the buzz surrounding AI’s emergence has centered around its ability to transform the way healthcare is delivered. AI is ideally suited to meet healthcare challenges, particularly for medical imaging, in three ways:

AI thrives on an overabundance of data.

Healthcare providers are drowning in data. As a result, they waste valuable time hunting for relevant patient information. For example, imaging studies in the past produced a handful of images, but advanced technologies today produce up to thousands of images per study.

The same image and data overload that is a burden for humans can help a deep learning system thrive. Through their ability to tackle large amounts of health data, AI systems can help provide quick access to a comprehensive patient record or the latest treatment guidelines.


AI can “see” what humans may miss.

AI excels in an inherently challenging area for humans: seeing patterns that are outside their scope of attention. In other words, people are good at finding what they are looking for, but not so good at finding what they’re not looking for, a phenomenon known as inattentional blindness.

AI systems, however, have no preconceived assumptions about expected findings that could blind them to unexpected results. Since missed findings can lead to adverse health outcomes, the ability of a system to catch an abnormality that a practitioner may have missed makes it very valuable in healthcare⁠—even essential.


AI integrates with — and helps leverage — existing systems and workflows.

AI systems are not very useful if they complicate workflows. A well-designed AI solution not only is compatible with existing infrastructure and workflows — causing minimal need for extra resources or disruption — but can help organizations utilize their existing systems more effectively

For example, the AI solution IBM Watson Imaging Patient Synopsis, which can search for relevant patient information in the unstructured data of an electronic health record (EHR), increases the value of the EHR.

With the capability to ingest large amounts of data, “see” hidden findings and fit into existing workflows, AI has great potential to help healthcare organizations achieve their central aim: improving their quality of care.


Read on and learn how AI can tackle the complexity of medical imaging and help professionals create a more personalized, informed and patient-centric approach to medical care.

With its future-oriented capabilities, AI has great potential to help healthcare organizations improve their quality of care.

With its future-oriented capabilities, AI has great potential to help healthcare organizations improve their quality of care.

How AI can address the complexity of medical imaging

How AI can address the complexity of medical imaging

Content for your platform can come from many sources: product data, marketing content, user-created content. And, you need the ability to be able to switch the content in and out where appropriate.

Today, the average radiologist interprets an image every three to four seconds, eight hours a day.²

As patient data increases in volume and complexity, there is growing pressure on radiologists to be more efficient and tackle larger patient volumes. At the same time, market pressures are driving organizations to look for ways to boost efficiency and productivity in order to meet their financial goals.

Number of images read by radiologists per day

With more incoming data and less time to review it, how are radiology practices to manage? One solution is to incorporate AI into the workflow of medical imaging professionals. With its powers of image and data analysis, AI may be able to assist providers by performing tasks such as:

—  Image acquisition

—  Initial reads and interpretations

—  Study prioritization and triage

—  Recommendations of relevant findings from patient records in the EHR

—  Recommendations of relevant findings from literature or clinical guidelines

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AI’s strengths pave the way for the prevention of diagnostic errors and, at the same time, can enable sustained productivity growth.

By quickly gathering pertinent data that may be difficult to find or access manually, AI has the potential to help providers be both more informed and more efficient. Everyone in the organization can benefit from AI’s capacities:

business_staff

Business staff see increased levels of productivity.

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Health providers can better manage their workload and focus on patient care.

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Patients feel confident that comprehensive and up-to-date information is helping inform their diagnoses and treatment.

Read about how radiologists at Hardin Memorial Health managed to transform a cumbersome and difficult to search EHR using the power of AI to extract more patient information while reading an imaging study.

You can explore more information about the capabilities of our comprehensive radiology solution suite.

Read on and learn more about Watson Health Imaging and how it can enhance the quality of healthcare worldwide.

Read about how radiologists at Hardin Memorial Health managed to transform a cumbersome and difficult to search EHR using the power of AI to extract more patient information while reading an imaging study.

About Watson Health Imaging

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Prescription for change

Watson Health Imaging, a segment of IBM Watson Health, is a leading provider of innovative artificial intelligence, enterprise imaging and interoperability solutions that seek to advance healthcare. Its Merge branded enterprise imaging solutions facilitate the management, sharing and storage of billions of patient medical images.

With solutions that have been used by providers for more than 25 years, Watson Health Imaging is helping to reduce costs, improve efficiencies and enhance the quality of healthcare worldwide.

“I think Watson is going to revolutionize not only radiology, but all of medicine. It’s going to allow us to gain insight into our patients in a way that we’ve never had before.”

“I think Watson is going to revolutionize not only radiology, but all of medicine. It’s going to allow us to gain insight into our patients in a way that we’ve never had before.”

Alexander J. Towbin, MD Associate Chief of Operations Radiology and Informatics Cincinnati Children’s Hospital Medical Center