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How to use next-generation artificial intelligence for disease diagnosis
How to use next-generation artificial intelligence for disease diagnosis
The accuracy of disease diagnosis directly affects medical treatment and its efficiency. By leveraging AI diagnostics, medical experts can effectively evaluate patient information, analyze large amounts of data, and make the best decisions in each case. Let’s dive into the most common ways AI can help doctors diagnose disease.
Improved Medical Image Processing
Medical imaging requires sophisticated equipment and skilled experts who can interpret CT or MRI scans. According to statistics, in the United States, medical professionals perform 30 million MRI scans each year, and AI diagnostics can help them accomplish this task by:
Improving the speed at which MRI scans are created . Research shows that machine learning models reduce errors by dynamically obtaining MRI reconstructions.
Improve patient comfort. Artificial intelligence allows medical professionals to reduce the time required for scanning, resulting in a better experience for patients, especially those who are uncomfortable with static postures. Additionally, modern developments in healthcare and artificial intelligence may help eliminate distortions associated with unexpected patient movement during scanning.
Greater patient safety. With the help of artificial intelligence, iterative reconstruction can be used for computed tomography and obtain high-quality scans while exposing the patient to a smaller dose of X-rays.
In addition to scanning, AI systems can improve hospital workflow by prioritizing cases and detecting disease. Engineers train AI models to identify patterns and deviations by feeding them medical images that describe certain medical conditions. These systems learn how to detect diseases in their early stages. Why is this critical? For example, in the case of cancer, early diagnosis can save lives and significantly reduce the cost of treatment. According to Statista, cancer was listed as one of the leading causes of death from March 2020 to January 2022, so AI for cancer detection could literally save lives.
Perfect clinical trials
Traditional clinical research is a lengthy process due to large-scale testing and post-marketing studies. Only 10% of drugs receive regulatory approval, according to Deloitte Insights. In this way, pharmaceutical companies can collect large amounts of data and statistical reports. With all the data at their disposal, companies should clean, store and manage the information. Artificial intelligence facilitates daily tasks related to data processing, reducing the number of human errors. In addition, the adoption of artificial intelligence brings more benefits, such as:
Simplified experimental design. After analysis by artificial intelligence technology, data obtained from previous clinical trials can serve as guidance for new research and even reduce the probability of failure.
Improve patient selection. AI examines and interprets data from different sources, namely imaging and electronic health records, and selects optimal patient enrichment by reducing population heterogeneity, selecting patients with appropriate clinical endpoints, and assuming better response to treatment.
Intelligent automation of contracts. AI can streamline data insertion, data extraction, and risk identification by automating data entry and risk assessment.
Artificial intelligence can improve clinical trials in other ways. Physicians can apply text mining to search for insights in available data sources. This approach is suitable for in-depth text analysis. However, AI can track patterns not only in documents but also in human behavior, allowing us to notice any deviations.
Better identification of mental disorders
Detecting mental illness and promoting health is getting easier with the help of AI diagnostics. So, which AI-driven technologies are playing a big role here?
Speech analysis systems monitor the slightest changes in speech. Monotonous, soft speech with pauses may indicate depression. Rapid speech with frequent breathing indicates anxiety. By leveraging deep learning models and taking sound characteristics into account, engineers have created systems that predict mental disorders and illnesses. Dementia, schizophrenia and post-traumatic syndrome to name a few. A brief recording is enough to reveal these health issues.
For example, using AI for early dementia diagnosis helps doctors identify symptoms of the disease (struggles with reasoning, attention problems, and memory loss) in the early stages. If recorded on audio, these defects could be used as material for training classification models that distinguish healthy people from sick people. Using recordings from neuropsychological tests, patients can become aware of early signs of dementia long before brain cell damage occurs.
While AI diagnostics will not replace doctors, the methods described for identifying mental disorders are applicable to everyday clinical scenarios. The current results suggest that AI can identify signs of disease at an early stage and pass this data to doctors for further study and confirm or deny a diagnosis. Artificial intelligence techniques, including convolutional neural networks, identified anxiety-related behaviors with over 92% accuracy, according to research specifically focused on the identification of anxiety-expressing activities. This study and others show how the adoption of artificial intelligence will develop in the field.
Emerging Trends in Artificial Intelligence Diagnostics and Health Monitoring
Metaverse trends are not bypassing the healthcare industry. Artificial intelligence, augmented reality, and virtual reality will drive the next level of telemedicine. With the Metaverse app, patients can easily access virtual healthcare facilities and receive qualified support remotely. This method is useful in remote areas and allows for remote treatment. For example, to treat psychosis, a team from Oxford developed gameChange (virtual reality therapy). It helps people with psychosis safely immerse themselves in a controlled environment and learn to cope with their fears by re-enacting everyday situations (e.g. going to a cafe, shopping, etc.).
Another trend is wearable medical technology that will boom and can be enhanced by artificial intelligence. Fitness trackers, smartwatches, and biometric sensors are becoming common devices and use ML algorithms to understand how you walk, run, move, or perform any physical activity. Areas like the Internet of Body open up a host of smart devices, such as hearing aids, ingestible sensors, and smart pills, that can collect data about our bodies in real time and use it for health monitoring and diagnosis. Furthermore, further research in this field has led to the emergence of a new generation of devices, namely wearable devices for the visually impaired for detecting obstacles or screening for diabetic eye disease, introduced by the Google Brain Initiative.
Final Thoughts
The development of artificial intelligence technology benefits all business systems. In the healthcare industry, AI is opening up better ways to monitor health and diagnose disease more efficiently, even in its early stages. Timely and more accurate diagnosis allows you to choose the best treatment option and significantly increase its effectiveness. Additionally, the development of AI-based healthcare startups helps patients independently monitor key indicators of their health without missing early symptoms. This increases patient engagement in health and makes medicine a more innovative field that can transform our lives.
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