  Like other fields of Traditional Medicines, Unani Medicines have been found
as an effective medical practice for ages. It is still widely used in the
subcontinent, particularly in Pakistan and India. However, Unani Medicines
Practitioners are lacking modern IT applications in their everyday clinical
practices. An Online Clinical Decision Support System may address this
challenge to assist apprentice Unani Medicines practitioners in their
diagnostic processes. The proposed system provides a web-based interface to
enter the patient's symptoms, which are then automatically analyzed by our
system to generate a list of probable diseases. The system allows practitioners
to choose the most likely disease and inform patients about the associated
treatment options remotely. The system consists of three modules: an Online
Clinical Decision Support System, an Artificial Intelligence Inference Engine,
and a comprehensive Unani Medicines Database. The system employs advanced AI
techniques such as Decision Trees, Deep Learning, and Natural Language
Processing. For system development, the project team used a technology stack
that includes React, FastAPI, and MySQL. Data and functionality of the
application is exposed using APIs for integration and extension with similar
domain applications. The novelty of the project is that it addresses the
challenge of diagnosing diseases accurately and efficiently in the context of
Unani Medicines principles. By leveraging the power of technology, the proposed
Clinical Decision Support System has the potential to ease access to healthcare
services and information, reduce cost, boost practitioner and patient
satisfaction, improve speed and accuracy of the diagnostic process, and provide
effective treatments remotely. The application will be useful for Unani
Medicines Practitioners, Patients, Government Drug Regulators, Software
Developers, and Medical Researchers.
