How artificial intelligence will advance medical diagnostics

Machine Learning

An accurate medical diagnosis influences the effectiveness of treatment and also makes it possible to predict possible side effects and speed up recovery with appropriately adjusted medication.

statistics show It is predicted that 80% of cancer patients and 40% of diabetes patients will benefit from early detection using AI by 2024. These figures demonstrate the need for further development and application of AI technology in medical diagnosis.

AI technology can help medical professionals diagnose patients in three ways:

  • Accelerating early diagnosis
  • Fast and accurate inspection and image interpretation
  • Detailed analysis of similar cases


This was the first area of ​​medicine to actively embrace digital technology. When AlexNet came on the scene in 2012, machines learned to recognize images. Combining image recognition with cloud storage and automation, this solution has become indispensable for radiologists.

Radiology departments can now benefit from a variety of AI-based assistants.

  • Mammogram readers provide another competent opinion to human experts.
  • Battery-powered X-ray machines detect tuberculosis and pneumonia in remote areas of Africa.
  • AI-powered image recognition systems can interpret photos taken with minimal radiation doses and even notice slight variations from the norm.
  • An ultrasound system equipped with AI capabilities can check fetal weight, quality, amniotic fluid volume, and more.
  • AI-enabled stethoscopes can help diagnose heart diseases.
  • Innovative MRI tools allow healthcare professionals to Cost reductionLow-field magnets are portable, allowing scans to be done at the patient's bedside, eliminating the need for a separate room. Conversely, high-field magnets speed up cancer scans and reduce patient discomfort as integrated AI generates instant explanations.


AI technology can help measure retinal biomarkers such as retinal thickness and fluid volume. Analysis and testing typically involves: four times This takes less time than using traditional tools. This is made possible by cloud storage of the data. Some processes run simultaneously, for example, while you are uploading part of the data, an expert may be reviewing another part.


Swedish developers are working on an AI-powered Dermalyser that can detect malignant melanoma and prevent the risk of skin cancer. Early results from clinical trials demonstrate high diagnostic accuracy. The AI ​​algorithm compares images of the patient's melanoma with relevant datasets. Deep learning enables accurate diagnosis.


Machine learning tools in dentistry also help in image recognition and interpretation. Computers can identify even the slightest variations in the shades of gray in X-rays. Thus, AI vision pays more attention to detail than the human eye. Thousands of images and the conclusions based on them serve as input data for machine learning technologies. For a dentist, this is like consulting many colleagues at the same time.


EarliTec Diagnostics has devised a test that can detect autism in children aged 16 months and older. Children watch a short video and a special AI-based tool records their eye movements. Children with autism focus on the video unlike other children. New research shows that autism is Diagnosed It affects 1 in 36 children and is a very concerning issue, and the sooner parents and doctors know about it, the better for the child.

From the above facts, we can conclude that data is the key to making the technology work perfectly. Computers can make accurate diagnoses only if the input data is of high quality. BI and Data Analytics Step in. Carefully collected and conveniently stored information allows doctors to effectively screen health conditions and provide individualized treatment plans for each patient.

Medical data can be stored in traditional relational databases or more sophisticated Healthcare Analytics DatabaseIn my experience, medical centers today are choosing software solutions that combine the functionality of a CRM platform with BI.


An electronic medical record (EMR) stores a digital version of your medical history, test results, and all other documentation within your healthcare provider.

An electronic health record (EHR) is a similar system that allows for a more holistic approach to each patient. The EHR contains data from all the hospitals, clinics, nursing homes, and other healthcare facilities a patient has visited. So, by consulting the EHR, a doctor receives a complete medical history and can take more facts into account when making a diagnosis.

As confirmed by one of our clients, the customized software has relieved staff from tedious administrative tasks, minimizing human errors and speeding up work.

Most companies recognize the need to modernize their datasets and implement AI tools, but they haven’t assessed their company’s readiness for change.

Dramatic improvements must be implemented incrementally, and in my experience, healthcare providers must first evaluate business and technology drivers.

  • Is your current EHR or EMR up to date with recent demands?
  • Is it possible to upgrade legacy systems to meet business requirements?
  • Is the upgrade affordable?
  • What are the risks of postponing an upgrade?

Discussing opportunities and risks can help providers gain clarity on further steps to take.

AI features such as data extraction, alerting for missing information, summarization, and highlighting key issues make EHRs well-suited to assist medical professionals. Further application of multimodal medical data will lead to the development of explainable AI, a system that makes it clear to non-developers how to work with AI-powered tools and interpret their output. Within healthcare organizations, more medical staff will be able to deal with new tools and systems, leading to improved skills. This trend will reduce the need to hire expensive experts.

Another trend is the development of clinical decision support systems (CDSS). Simply put, these are systems that not only assist with diagnosis but also provide consultation on treatment plans. They contain various alerts, reminders and guidelines to facilitate staff in their daily work.

When it comes to AI in medical diagnostics, the prospects for its development are limitless. However, every business owner needs to understand that technology for technology's sake is a dead-end strategy. The implementation of AI tools starts with clear data from which the algorithms learn.

About the author:

Dmitry Baraishuk is Partner and Chief Innovation Officer at software development company Belitsoft (a subsidiary of Noventiq) and has over 19 years of experience in digital healthcare, custom e-learning software development, and business intelligence (BI) implementation.

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