Yogesh Subhash Chaudhari Several non-coding platforms allow non-IT personnel, especially in the healthcare sector, to implement AI solutions for research and development without requiring programming expertise. These platforms simplify AI integration through drag-and-drop interfaces, pre-built templates, and automated tools. Below is a list of widely used platforms:

1. Google AutoML

Example: Imagine a doctor using Google AutoML to classify X-ray images into categories like normal and abnormal without writing code. The platform provides tools to train custom machine learning models with minimal effort. Key Features: Image recognition, language processing, and predictive analytics.

2. DataRobot

Example: A healthcare researcher studying patient recovery rates can use DataRobot to predict outcomes based on medical histories. It automates the process of building and deploying machine learning models. Key Features: Automated model building, interpretable AI, and predictive modeling.

3. IBM Watson Health

Example: IBM Watson helps physicians identify cancer patterns by analyzing unstructured clinical data like reports and patient histories. Key Features: AI-driven analytics, medical imaging support, and chatbot integration for patient interactions.

4. Microsoft Azure Machine Learning Studio

Example: Researchers can predict patient admission rates by dragging data sets and connecting tools visually, making analysis faster and simpler. Key Features: Cloud-based, pre-built templates, and seamless integration with Excel.

5. KNIME Analytics Platform

Example: A researcher evaluating drug effectiveness can use KNIME to visually process data sets and test AI algorithms without coding. Key Features: Data visualization, workflow-based design, and integration with statistical tools.

6. Orange Data Mining

Example: A nutritionist evaluating dietary habits can analyze patterns through drag-and-drop widgets. Key Features: Data visualization, machine learning models, and statistical analysis.

7. RapidMiner

Example: A hospital administrator can use RapidMiner to predict staffing needs based on past patient inflows. Key Features: Workflow-based model creation, text mining, and predictive analysis.

These platforms bridge the gap between healthcare experts and AI technology, enabling doctors, researchers, and administrators to harness AI without learning programming. They offer accessible tools to support predictive analytics, medical imaging, drug discovery, and patient care improvements.

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Yogesh Subhash Chaudhari

There are several such platforms for non-IT personnel, especially in the healthcare sector. You can explore the following:

• No-Code AI Platforms: https://toolplate.ai/blog/top-no-code-ai-platforms
• https://www.blaze.tech/post/no-code-ai-applications-for-healthcare-and-other-traditional-industries
• https://appmaster.io/blog/future-of-healthcare-no-code-ai

Almost every no-code platform can be implemented in IT healthcare, processes are more crucial for successful implementation.

Here is a list of no-code AI platforms for healthcare research:

These platforms help non-IT personnel implement AI in healthcare research and development.