Medibot

In the rapidly evolving healthcare landscape, individuals frequently require quick answers regarding symptoms, medications, or post-treatment care. Medibot's sophisticated AI features guarantee prompt and precise responses. Whether offering advice on medication usage or insights on post-surgery recovery, the chatbot stands as a dependable information source, streamlining healthcare decision-making.

Generative AI Healthcare Chatbot that encompasses the following features: AI health consultation and AI prescription models.

How it works:

We intend to create a basic medical chatbot utilizing OpenAI's GPT-3 language model. Develop a chatbot specialized in the medical domain capable of addressing typical health inquiries, supplying information on medical topics, and offering advice regarding symptoms and treatments, all powered by the GPT-3 language model.

Using GPT-3 Model

During the training phase, the train.py file is run, leading to the creation of a new database. The database files, all in yml format, undergo training in the initial phase of the application model.

New Training Model

We are developing a Healthcare chatbot system based on AI and NLP (Natural Language Processing), designed to be user-friendly and more secure than the existing system in the market. This system aims to not only treat diseases but also contribute to maintaining overall health, thereby reducing the likelihood of illnesses. It will offer precise information about health symptoms and medications to patients. Additionally, the government will be able to monitor the distribution of medicines to medical facilities and hospitals.

AI + NLP

The chatbot will present Symptom 1 to the patient, and if the patient answers "Yes," the chatbot will recognize the associated disease along with other symptoms commonly linked to it. In the event of a "No" response, the chatbot will then present Symptom 2. If the patient responds "Yes," the chatbot will again identify the associated disease and other symptoms commonly associated with it.

S&A Dynamic Response Sequence

Support Vector Machines (SVM) excel in managing intricate classification tasks compared to the K-nearest neighbor (KNN) classifier and the naive Bayes classifier, which are more suited for rapid and straightforward classification tasks. SVM classifiers demonstrate faster training times with optimal feature space dimensions, enhancing performance particularly for small or medium-sized datasets. In direct comparison, SVM exhibits higher accuracy, nearly 94 percent greater than the Naive Bayes and KNN methods.

Support Vector Machines (SVM)

Medibot currently employs DeepDDI2 as an enhanced iteration of DeepDDI, an artificial intelligence-based model for predicting drug interactions established in 2018. DeepDDI2 can calculate and analyze 113 types of drug-drug interactions (DDIs), expanding beyond the 86 DDI types addressed by the existing DeepDDI.