May 25, 2022
10 minutes read
Machine Learning (ML) algorithms focused on medical image analysis have revolutionized the diagnosis process. Now doctors can detect more diseases than previously. For example, more genetic illnesses can be recognized that were not diagnosed before. Other conditions, such as Asperger’s or Parkinson’s, can be detected better by ML-based facial recognition solutions.
ML-based algorithms that analyze medical images will positively impact different diagnostic fields. Let’s talk about some most popular areas:
Using ML methods by analyzing medical images, a human physician can significantly improve the quality of medical care.
There are many possibilities of how automated and high-precision feature extraction can support healthcare organizations in everyday life. For example:
Medical treatment can be personalized and matched to individual characteristics. Physicians can develop a more effective treatment with a complete picture of the patient’s medical data. Now doctors can choose diagnoses or avoid risks not only after analyzing each patient’s medical history but also by using prognosticate abilities of ML.
ML algorithms can support physicians in giving better advice about behavior and lifestyle to improve patients’ health.
ML can make pharmaceutical drugs more affordable by reducing production time and costs.
Clinical trials can become cheaper but still be accurate. Algorithms can find clinical trial candidates by comparing data from medical history records. ML solutions can monitor the clinical trial process, track results, etc.
Medical imaging is beneficial in the field of robotic surgery as well. It allows doctors to operate more precisely and minimize risks.
ML algorithms can soon suggest the regions where epidemic outbreaks are possible by comparing information taken from satellites, social media, and other information sources.
While diagnostic confidence never reaches 100%, combining machines plus physicians reliably enhances system performance. The computerized module for detecting abnormalities in marked regions of medical images is essential. For example, applications for detecting lung diseases are a new machine learning solution that can help improve the diagnostic process. With this solution, doctors can recognize a lot of conditions very quickly.
Doctors can make diagnosis more accessible, faster, and more precise with these applications. The solution offers better visibility of infected areas because they are highlighted.
Related: Benefits of implementing AI in medical imaging
These programs help identify a particular disease more quickly, suggesting diagnoses from the database. The algorithm calculates the probability of each condition in percentages. Doctors can share the received data with colleagues to discuss the diagnosis over a network.
Modern solutions for the detection of lung diseases are a necessary assistant when many patients need medical help immediately, and time for each patient has significantly decreased. Because doctors spend less time on diagnostics, they can focus on the decision-making and treatment of patients.
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