Yes, there are several software and websites available that utilize AI in the field of medical statistics. Here are a few examples:
IBM Watson Health: IBM Watson Health offers a range of AI-powered solutions for healthcare analytics and research. Their platform provides tools for data analysis, predictive modeling, clinical decision support, and population health management.
Google Cloud Healthcare: Google Cloud Healthcare provides various AI-based tools and services for medical data analysis and research. Their offerings include machine learning APIs, data storage and processing solutions, and healthcare-specific algorithms for tasks like image analysis and natural language processing.
SAS Healthcare Analytics: SAS is a well-known provider of analytics software, and they offer specialized solutions for healthcare analytics. Their healthcare analytics platform incorporates AI and machine learning to analyze clinical and administrative data, generate predictive models, and support population health management.
Clinical Data Science: Clinical Data Science is a web-based platform that utilizes AI and machine learning algorithms to analyze clinical data. It provides tools for data visualization, predictive analytics, and decision support, allowing researchers and healthcare professionals to gain insights from medical datasets.
MedMiner: MedMiner is a web-based tool developed by the National Cancer Institute (NCI) that integrates AI and natural language processing techniques to facilitate data mining and analysis of biomedical literature. It allows researchers to extract and analyze information from vast amounts of published research articles.
Mendel.ai: Mendel.ai is an AI-powered platform that assists with clinical trial matching and patient recruitment. It uses natural language processing and machine learning to match patients to appropriate clinical trials based on their medical records and eligibility criteria.
These are just a few examples, and there are many other software platforms and websites available that leverage AI in the field of medical statistics. It's always a good idea to thoroughly research and evaluate the features, capabilities, and data privacy considerations of any software or website before using it for medical statistics analysis.
Hey, one software that can give you a quick overview of the data and apply a whole set of standard algorithms is RapidMiner. Also, MatLab has an extensive catalog of algorithms that can be used for analysis. For a distributed analysis, however, one would implement a Python program that uses, for example, the Pandas, Sklearn, or Pytorch packages.
At the Institute for Artificial Intelligence in Medicine, we specialize in applying AI to medical data - we are always happy about new collaborations.
I think, you need to be clear about your aim. If your aim is clinical prediction (including diagnosis and prognosis), you can consider machine learning methods such as logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), etc.. If your goal is clustering, you can use K-means clustering, principal component analysis (PCA), and other methods. If your data involves medical images, you can consider convolutional neural network (CNN).
You can use the R language, Python, or Matlab for software