Artificial Intelligence

How to Use Next-Generation AI for Disease Diagnostics

The accuracy of disease diagnostics has a direct impact on medical treatment and its efficiency. By leveraging AI diagnostics, medical specialists can competently assess patient information, analyze large volumes of data, and make the best decisions in each situation. Let’s dive into the most common ways artificial intelligence can help physicians with disease diagnosis.

Improved Processing of Medical Images

Medical imaging requires complex equipment and skilled experts who can interpret CT or MRI scans. In the U.S., medicals professionals perform 30 million MRI scans each year, according to statistics, and AI diagnostics can help them with this task in the following ways:

  • Improved speed of creating MRI scans. Research shows that machine learning models reduce errors by dynamically obtaining an MRI reconstruction.
  • Enhanced patient comfort. AI allows medical professionals to reduce the time needed for scanning sessions, resulting in a better experience for patients, especially for those who feel uncomfortable due to static position. Moreover, modern developments in healthcare and AI may help to eliminate distortions related to unexpected moves made by the patient during the scanning process.
  • Greater patient safety. With AI, it has become possible to use iterative reconstruction for computer tomography and get high-quality scans while exposing patients to smaller doses of X-rays.

Besides scanning, AI systems are applied to improve workflows in hospitals via prioritization of cases and disease detection. Engineers train AI models to identify patterns and deviations by providing them with medical images that depict certain medical conditions. These systems learn how to detect diseases at early stages. Why is this crucial? For example, in the case of cancer, early diagnosis may save lives and significantly cut treatment costs. According to Statista, cancer was named among the leading causes of death from March 2020 to January 2022, so AI for cancer detection is indeed capable of saving lives.      

Refining Clinical Trials

Traditional clinical research is a lengthy process due to mass testing and post-marketing studies. According to Deloitte Insights, only 10 percent of drugs become regulatory approved. In this way, pharmaceutical companies can gather enormous data and statistical reports. And having all the data at their disposal, companies should clean, store, and manage the information. AI facilitates routine tasks related to data processing, reducing the number of human errors. Moreover, AI adoption brings far more benefits, such as:

  • Streamlining of trial design. After being analyzed with AI technologies, data obtained from previous clinical trials can serve as a guide for new research and even reduce the probability of failure.
  • Improvement of patient selection. AI checks and interprets data from different sources, namely, imaging and electronic health records, and enables opting for the best patient enrichment by reducing population heterogeneity, choosing patients with suitable clinical endpoints, and, hypothetically, a better response to treatment.
  • Smart automation for contracts. AI can streamline data insertion, data extraction, and risk identification with automated data entry and risk assessment.

AI is capable of refining clinical trials in other ways. Text mining can be applied by doctors to search for insights in available data sources. This approach is suitable for in-depth text analysis. However, AI can also track patterns not only in documents but in human behavior, thus allowing us to notice any deviations.            

Better Identifying Mental Disorders

With AI diagnostics, it’s becoming easier to detect mental illnesses and promote health and wellness. So what AI-driven technologies play a significant role here?

Voice analysis systems monitor the slightest change in speech. Monotone, soft speech with pauses might indicate depression. And fast speech with frequent breaths signals anxiety. By leveraging deep learning models and taking into account vocal features, engineers create systems that predict mental disorders and illnesses. Dementia, schizophrenia, and post-traumatic syndrome, to name a few. Short voice recordings can be enough to reveal these health issues. 

For example, using AI for early dementia diagnosis helps physicians identify symptoms of the disease in the early stages (reasoning struggles, focusing problems, and memory loss). If recorded on audio, these defects can be used as material for training a classification model that will distinguish between a healthy person and a sick person. Using recordings of neuropsychological tests, patients can become aware of the early signs of dementia long before brain cell damage occurs.

While AI diagnostics won’t replace doctors, the described approach to the identification of mental disorders fits in everyday clinical scenarios. Current results show that AI can identify signs of diseases in the early stages and pass this data on to doctors for further study and confirmation or denial of the diagnosis. According to the study dedicated to anxiety-displaying activities recognition, AI technologies, including Convolution Neural Networks, identify anxiety-related behavior with more than 92 percent accuracy. This and other studies indicate how AI adoption will evolve in this area.        

The trend of the metaverse has not bypassed the healthcare industry. AI, Augmented Reality, and Virtual Reality will drive the development of a new level of telemedicine. With metaverse applications, patients can easily visit virtual healthcare facilities and get qualified support remotely. This approach is useful for distant regions and enables remote treatment. For instance, to treat psychosis, the team from Oxford has 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 like going to a cafe, shopping, and so on.

Another trend is wearable healthcare technology that will thrive and can be enhanced with AI. Fitness trackers, smartwatches, and biosensors are becoming common devices and use ML algorithms to understand how you walk, run, move, or do any physical activity. Such a field as the Internet of Bodies has opened up for us a lot of smart devices like hearing aids, ingestible sensors, and smart pills that can collect data from our bodies in real time and use it for health monitoring and diagnostics. Moreover, further studies in this area have resulted in the advent of a new generation of devices, namely wearables for visually-impaired people that detect obstacles or diabetic eye disease screening introduced by the Google Brain initiative.

Final Thoughts

The development of artificial intelligence technology benefits all business systems. In the healthcare industry, AI opens up better ways to monitor health and more effectively diagnose diseases, even at an early stage. A timely and more accurate diagnosis allows you to choose the best treatment option and significantly increase its effectiveness. Moreover, the development of AI-based healthcare startups helps patients independently monitor the main indicators of their health and not miss early symptoms. This increases the engagement of patients in taking care of their health and makes medicine an even more innovative field that can change our lives.

MobiDev

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