AI in healthcare is used in a wide range of applications from patient care management, epidemiology, drug discovery, disease prediction, early diagnosis and many more. The implementation of machine learning in healthcare follows a typical AI lifecycle of problem definition, data identification and collection, algorithm selection, model development, evaluation and deployment. Research studies typically don’t involve large-scale deployment

As a result, healthcare application sees the application of general-purpose AI tools as well as specialized tools for applications such as drug discovery.  Python NumPy Pandas and R remain the base software platforms for data processing.  A large number of Python libraries and R packages are used for specific classes of applications.  The list below highlights common tools, libraries and packages

Tools Used

At MedicalWriting4u we believe in using the tools that support the analytical requirements of the research  considering the  evidentiary needs and ontological basis

 

Tool Purpose Language Application
Pandas

Basic data structures and data manipulation library for Python. Includes analysis methods for basic statistical analysis

 Python Data Structure and Basic Statistical Analysis
Scikit-learn

An open-source machine learning library that provides a wide range of algorithms for regression, classification, clustering, and other machine learning tasks.

 Machine Learning Algorithms
PyCaret

PyCaret is an open-source, low-code machine-learning library in Python and support regression, classification and statistical analysis model

 Python Full ML lifecycle
StatsModel

Statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. Similar to R

 Python Statistical Analysis
MatplotLib

Plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots

 Python Visualization
Spacy

Python library for NLP. It is known for its speed and accuracy, and it is widely used by researchers. SpaCy provides a variety of features for NLP tasks, including tokenization, tagging, parsing, and named entity recognition.

 Python NLP
NLTK

It is a comprehensive toolkit that provides a wide range of features for NLP tasks, including tokenization, tagging, parsing, classification, and sentiment analysis. NLTK is widely used in academia and research, and it is also used by some developers in industry.

 Python NLP
Stanford CoreNLP

NLP toolkit developed by Stanford University. It provides a variety of features for NLP tasks, including tokenization, tagging, parsing, named entity recognition, and coreference resolution widely used in academia and research

 Python NLP
SpaCy

spaCy is an open-source Python library for natural language processing (NLP). It provides a variety of features for NLP tasks, including Part-of-speech tagging,

Named entity recognition, classify text into different categories, such as positive and negative sentiment.

 Python NLP

Sentiment Analysis
Tensor Flow

Library for numerical computation using data flow graphs. TensorFlow is used for machine learning and deep learning.

TensorFlow is used for a wide variety of tasks, including Image recognition,

Natural language processing, Machine translation, Speech recognition, Computer vision, Medical imaging

 Machine Learning, Deep Learning and neural network models
PyTorch

PyTorch is used for applications such as computer vision and natural language processing. Used in healthcare to develop medical imaging analysis algorithms and drug discovery tools.

 Python Deep Learning

Computer Vision

NLP
Keras

Keras is a high-level library for developing and deploying deep learning models. It is built on top of the TensorFlow library, and it provides a number of features that make it easy to create and train deep learning models, including:

 Python and  R Deep Learning and different architectures of neural networks such as CNN, LSTM, RNN, GAN etc
OpenCV

Provides a wide range of functions for image processing, video analysis, and machine learning.

 

 R and Python Image and Video Analysis
Scikit-image

Library for image processing and analysis. It provides a variety of functions for image filtering, segmentation, and object detection.

 Python Image Analysis
Imager

R package that provides a variety of functions for image processing and analysis. It is easy to use and provides a wide range of features, including image filtering, segmentation, and object detection.

 R Image Analysis

Conclusion

The list is a very short list of available tools with a focus on FOSS and R and Python as the core platforms. Some of the life cycle tools are not included as they relate to the deployment of large-scale models