Peter Wachira

Data science for medical imaging Have you ever wondered how doctors interpret medical images for patient diagnosis? It is no longer through intuition like in the past but rather with the help of data science. Modern medicine relies on data science in medical imaging to diagnose patients. Technological advances in healthcare have seen the use of deep learning to interpret scanned images. Images from MRIs, CT scans, X-Rays, sonography, and many more are complicated to discern. However, computers today, through artificial intelligence (AI) and machine learning (ML), help doctors identify defects in a patient's body accurately. The technique uses algorithms fed from previous related samples that improve accuracy with time. Clinicians like radiologists use algorithms to compare data with corresponding data sets to develop proper diagnoses and treatment plans. Healthcare data science enables doctors to formulate an effective treatment strategy for conditions like cancer, organ issues, and arterial complications. With the ability to detect microscopic deformities in scanned images at their early stage, they can effectively treat a disease before it intensifies. Furthermore, access to previously shared genomic databases of patients like those with cancer can help doctors note how specific genetic makeups respond to a particular treatment plan. https://www.simplilearn.com/how-ai-and-data-science-is-changing-the-role-of-radiologists-article https://www.knowledgehut.com/blog/data-science/data-science-in-healthcare https://intellipaat.com/blog/data-science-applications-in-healthcare/#:~:text=Data%20Science%20helps%20in%20the,CT%20scan%2C%20and%20many%20more. https://data-flair.training/blogs/data-science-in-healthcare/ https://eleks.com/blog/data-science-in-healthcare/