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.
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.
Top 7 tips for cybersecurity in healthcare
Today we all use a lot of online services. All systems, from financial and medical institutions to the grids that power entire cities, rely on digital technologies. Cyber protection allows them to function correctly and is a wall that keeps cyberattacks at bay. Your health records, banking details, private messages, or online activities will become…
Machine learning in healthcare: fundamental challenges vs. immense opportunities
The latest developments in medical care mean a lot of pros to everybody, beginning with reduced unnecessary disability and increased life spans to better health equity and life quality. Besides all those benefits, the changing healthcare landscape means a heavy burden, especially for the finances — the USA is planning to spend no less than…
How machine learning reduces costs spent on treatment and care
How does ML respond to the real needs of healthcare organizations? According to research made by Syft in 2018, hospitals spend over $25 billion more than necessary in their supply chains despite having the ability to save an average of 17.7% in their total supply expenses. Artificial intelligence (AI) and machine learning (ML) can decrease…
What is predictive analytics in healthcare and why it is important
Predictive analytics can seriously change the medical industry shortly. Based on the use of big data, this technology will help, for example, prevent the occurrence of chronic diseases in patients and correctly allocate doctors’ resources. For the industry, the load can be reduced, and, in general, its work can be made more predictable. What is…
Top 7 features of chronic care management software
Information processes are present in all areas of medicine and healthcare. The visibility of the industry, which functions as a whole, and the management effectiveness depend on order. The essential properties are objectivity, completeness, reliability, adequacy, accessibility, and relevance. The properties of information depend both on the properties of the data and the properties of…
Develop products in HIPAA-compliant cloud infrastructure
Implement secure, scalable, and fully HIPAA-compliant cloud infrastructure without having to establish in-house technical teams and manage all aspects of healthcare regulations. HIPAA-compliant cloud infrastructure is a customizable solution that allows the rapid provision of healthcare solutions on AWS. Built as a highly scalable, readily available, and secure infrastructure, such solutions are reinforced through complementary…
Predictive analytics in healthcare: benefits and challenges
Modern technologies, including Big Data and machine learning, have opened new horizons for the predictive analytics and decision support systems market. With proper use, they will be the next step in the digital transformation of any industry, including healthcare. Today, the market for predictive analytics is exceptionally fast-growing. According to a study by AMR, from…