#1. Introduction The primary objective of this initiative is to bridge the communication divide between individuals and health�care professionals by promptly addressing user queries. Presently, internet addiction is on the rise, yet people are becoming less vigilant about their health. They tend to avoid visiting hospitals for minor issues, which could poten�tially escalate into serious health complications over time. Instead of sifting through numerous web documents, the cre�ation of question-and-answer platforms[12] has emerged as a straightforward method to cater to such inquiries. How�ever, existing systems often have limitations, such as pro�longed waiting times for expert responses, leaving patients without immediate answers. This technology harnesses natural language processing, facilitating communication between computers using hu�man language. Natural language comprehension involves three key steps: identifying primary linguistic connections to dissect the subject into sentence components, describing the text, and finally, employing semantic interpretation to deduce text meanings. This process is vital for enabling computers to grasp and respond to human language effec�tively, enabling chatbots[11] and other AI systems to en�gage in meaningful communication with humans. A chatbot is a computer program crafted to emulate hu�man conversation via text or voice interactions, leverag�ing natural language processing techniques. The chief aim of chatbots is to mimic human dialogues as authentically as possible, delivering a user experience akin to convers�ing with a real person. Equipped with a user interface for receiving inputs and providing responses, chatbots can be trained to comprehend and address user queries across var�ious domains, including healthcare. Medical chatbots, in particular, are tailored to aid pa�tients with their health-related concerns and requirements. These chatbots employ algorithms to scrutinize user queries and identify patterns, allowing them to offer precise and prompt responses to similar questions. They can offer health-related advice, guide users to suitable medical re�sources, and facilitate the scheduling of appointments with healthcare providers. Chatbots prove especially benefi�cial when users have health-related questions outside of standard office hours or when healthcare professionals are not readily available. This capability ensures that timely and relevant health information is accessible whenever needed, enhancing the overall efficiency and responsiveness of healthcare services. 2. LITERATURE REVIEW This research offers an in-depth analysis of the efficacy of synchronous chat-based, one-on-one psychological state therapies, which utilize text-based interactions between pa�tients and therapists. The authors of the study discuss the increasing adoption of this therapy modality as an online intervention for individuals with psychological challenges. The review provides initial evidence indicating that text�based synchronous communication treatments are an effec�tive form of psychological support. The outcomes of these treatments are found to be on par with conventional thera�pies and generally surpass the results seen in waitlist con�ditions. This points to chat-based therapy as a promis�ing alternative to traditional therapeutic methods, offering greater convenience and accessibility for those who prefer online support. Despite these positive findings, the authors advise that future research should explore the practicality of imple�menting this technology in clinical environments, consider�ing aspects like cost and logistical challenges. The success of this therapy[4] type may also hinge on several factors, including the therapist’s expertise, the nature of the psycho�logical issues addressed, and the level of additional support available to patients outside of therapy sessions. This paper underscores the potential advantages and constraints of chat-based therapy, advocating for more comprehensive studies to better understand its effectiveness and its role in enhancing mental health outcomes. Additionally, the research introduces a chatbot devel�oped as a virtual healthcare assistant. Crafted using Python and integrating advanced linguistic processing and pattern recognition algorithms, this chatbot has shown promising results, with an accuracy rate of 80% in delivering correct responses during evaluations. The remaining 20% of re�sponses were either unclear or incorrect, suggesting areas for further refinement. This tool has demonstrated poten�tial as a training aid and for providing first aid and medical awareness, potentially facilitating initial diagnostics or of�fering advice for non-urgent health issues. The accessibility offered by the chatbot could notably im�prove healthcare service access, especially for those who find in-person consultations challenging or are located in areas with limited healthcare facilities. This development marks a significant step forward in healthcare technology, furnishing patients with an essential resource for medical consultation and advice. In summary, while synchronous chat-based therapies show considerable promise for psychological intervention, the article calls for more rigorous investigations into their applicability in clinical settings and their integration into broader healthcare frameworks[2]. 3. Methods The approach outlined involves a versatile chatbot ca�pable of engaging with users through both voice and text interactions[3], offering a comfortable and accessible means for users to communicate their health concerns. The system employs an expert system equipped with sophisti�cated algorithms designed to deliver intelligent and precise responses to user inquiries. Beyond the chatbot, the model provides access to medical specialists knowledgeable about the specific conditions users are dealing with, enhancing the personalization of the medical advice given. A significant advantage of this technology is its ability to support multiple participants in online counseling sessions simultaneously. This feature is especially beneficial for in�dividuals who might feel isolated or unsupported as they manage their health conditions. Data concerning the chatbot’s interactions are stored as pattern-template entries within a database[13]. This setup enables the chatbot to give customized recommendations re�garding pain management and dietary advice, specifically tailored to each user’s unique health circumstances. The stored data is consistently updated to reflect the latest in medical research and best practices, ensuring the advice remains current and effective. In summary, this innovative approach not only makes medical consultation[10] more accessible and user-friendly but also ensures that the guidance and support provided are customized to meet individual needs, thereby improving the management of various medical conditions. 4. Results 4.1. Datasets Train data is of 21.4 Mb and test data is of 23kb. We started off with a very basic version of a chatbot where we would give conditional inputs and get respective re�sponses. Then we improvised, moved to training our model (put model name) on a context of pdf. You can see the im�proved results. Then we moved to a context of 3 PDFs. We were able to answer questions from a wider knowledge base. The results improved even further. But we still noticed that the answers had a lot of room for improvement. We then used transformers of seq-2-seq type (Roberta). This improved the responses by a lot. To build a more natural chatbot we integrated LLMs. We opted for the Gemini 1.5 LLM after trying the Llama model. We noticed it was more convenient to use the Gemini 1.5 model. So, finally, we in�tegrated the Gemini 1.5 model. This enhances the quality of responses of our LLM. 4.2. Training Gemini Pro 1.5[5] on PDF Context As the project progressed, the scope expanded to incor�porate a broader knowledge base by integrating multiple Figure 1. Flow chart Figure 2. Architecture of Medical Chatbot PDF sources. This expansion was driven by the objective of enriching the chatbot’s understanding and response ca�pabilities across a diverse range of medical topics. By in�corporating insights from a variety of PDF documents, the chatbot gained access to a wealth of information, allowing it to address a wider spectrum of user queries with greater accuracy and relevance. This integration of multiple PDF sources was a strategic decision aimed at enhancing the chatbot’s adaptability and knowledge depth, ensuring that it could handle a multitude of medical scenarios and ques�tions effectively. The collaborative effort involved in curat�ing and integrating these diverse sources contributed signif�icantly to the chatbot’s overall performance and knowledge proficiency. Through this phase, the project team aimed to create a robust foundation that would enable the chatbot to cater to a broad user base and provide comprehensive, reliable medical information and assistance. 4.3. Expansion to Multiple PDFs The project’s evolution continued with the integration of transformers of the seq-2-seq type, specifically the RoBERTa model[14]. This strategic decision was driven by the aim to enhance the chatbot’s response quality and natural language processing capabilities. The RoBERTa model, known for its robustness in understanding context and generating accurate responses, brought a new level of sophistication to the chatbot’s functionality. By leveraging RoBERTa’s advanced language understanding capabilities, the chatbot was able to interpret user queries more effec�tively, leading to more accurate and contextually relevant responses. This integration marked a pivotal moment in the project’s development, as it significantly improved the chat�bot’s ability to comprehend nuanced queries and provide nuanced, informative answers. The collaborative effort in�volved in implementing and fine-tuning the RoBERTa model showcased the team’s dedication to enhancing the chatbot’s performance and user experience. This phase represented a crucial step towards achieving the project’s goal of creating a highly intelligent and user-friendly medical chatbot capa�ble of addressing complex medical inquiries with precision and clarity. 4.4. Integration of Transformers (RoBERTa) After the successful integration of the RoBERTa model, the project advanced to incorporate Large Language Mod�els (LLMs) for further refinement. Among the LLMs consid�ered, the team initially experimented with the Llama model but ultimately opted for the Gemini 1.5 LLM due to its con�venience and performance. This decision was informed by rigorous testing and evaluation, comparing factors such as response accuracy, computational efficiency, and ease of in�tegration with the existing chatbot framework. The Gemini 1.5 LLM brought a new dimension to the chatbot’s capabilities, leveraging its vast language under�standing and generation capabilities. This model was par�ticularly well-suited for handling the intricacies of medical terminology, context, and diverse user queries. By integrat�ing the Gemini 1.5 LLM into the chatbot architecture, the team aimed to achieve a more natural and human-like in�teraction experience for users. The integration process involved adapting the chatbot’s backend infrastructure to accommodate the Gemini 1.5 LLM’s[6] requirements, including fine-tuning parameters, optimizing computational resources, and ensuring seamless communication between the chatbot frontend and the LLM backend. This phase required meticulous attention to de�tail and collaboration across technical and domain exper�tise domains to ensure a smooth transition and optimal per�formance. The Gemini 1.5 LLM integration phase also included extensive testing and validation to assess its performance across various use cases and user scenarios. This compre- hensive testing approach involved simulated user interac�tions, real-world user feedback analysis, and benchmarking against industry standards for conversational AI systems. Overall, the integration of the Gemini 1.5 LLM repre�sented a significant advancement in the project’s capabili�ties, marking a transition towards more sophisticated lan�guage processing and response generation. The team’s col�lective effort in selecting, integrating, and fine-tuning the LLM showcased a commitment to delivering a high-quality and impactful solution for medical information retrieval and interaction. 4.5. Transition to Large Language Models (LLMs) Transitioning to Large Language Models (LLMs) marked a pivotal shift in our project’s evolution. After ex�perimenting with transformers like RoBERTa for improved responses, we sought to integrate LLMs for a more natural and comprehensive chatbot experience. Our journey from conventional models to LLMs, specifically the Gemini 1.5 model, was driven by the need for enhanced contextual un�derstanding and nuanced responses. The Gemini 1.5 LLM emerged as our choice after eval�uating multiple models, including the Llama model. We found Gemini 1.5 to be more suitable and convenient for our project’s requirements, offering a balance between com�plexity and performance. This transition brought forth a significant enhancement in the quality and depth of re�sponses generated by our chatbot, aligning closely with our goal of creating a highly responsive and intelligent conver�sational AI system. Incorporating LLMs required a thorough understanding of their architecture, training procedures, and fine-tuning techniques. We leveraged the capabilities of Gemini 1.5 through meticulous training on a sizable dataset, ensuring that the model could grasp complex medical concepts and provide accurate, contextually relevant information to user queries. The integration of LLMs not only elevated the conver�sational capabilities of our chatbot but also set the stage for further advancements in natural language processing within the medical domain. This transition represents a key milestone in our project’s development, showcasing our commitment to harnessing cutting-edge AI technologies for impactful healthcare solutions. 4.6. Comparative Analysis of Models The Comparative Analysis of Models played a crucial role in evaluating the performance and efficacy of differ�ent AI models employed in our project. We conducted a comprehensive comparison focusing on key aspects such as accuracy, contextual understanding, response quality, and computational efficiency across various models, including transformers like RoBERTa and Large Language Models (LLMs) like Gemini 1.5. Starting with RoBERTa, we observed commendable ac�curacy levels, especially in handling specific queries and generating concise responses. Its seq-2-seq architecture proved effective in capturing intricate medical nuances, leading to satisfactory results in our initial phases. How�ever, RoBERTa’s limitations became apparent when dealing with broader context understanding and generating more natural-sounding responses. In contrast, the transition to LLMs, particularly the Gemini 1.5 model, marked a significant leap in perfor�mance. Gemini 1.5 exhibited superior contextual aware�ness, semantic coherence, and conversational flow com�pared to RoBERTa. This was evident in the quality and depth of responses, where Gemini 1.5 consistently delivered more nuanced and human-like interactions, enhancing user engagement and satisfaction. The comparative analysis also delved into computational aspects, considering factors like training time, inference speed, and memory footprint. RoBERTa showcased effi�cient training and inference processes, making it a viable choice for certain applications requiring rapid response times. However, the computational demands increased sub�stantially with the scale and complexity of tasks, prompt�ing the shift towards LLMs like Gemini 1.5, which demon�strated competitive performance without compromising on efficiency. Furthermore, our analysis extended beyond quantitative metrics to include qualitative assessments based on user feedback and real-world application scenarios. Gemini 1.5 emerged as the preferred choice due to its holistic approach, balancing accuracy, naturalness, and computational feasi�bility, making it well-suited for our healthcare chatbot’s re�quirements. Overall, the comparative analysis provided valuable in�sights into the strengths and limitations of different AI mod�els, guiding our decision-making process towards adopting LLMs like Gemini 1.5 for optimal performance and user ex�perience in our project 4.7. Results and Analysis The Results and Analysis section encapsulates the em�pirical outcomes of our project, detailing the performance metrics, user feedback, and insights gained from deploying advanced AI models like Gemini 1.5 in our healthcare chat�bot system. Performance Metrics: Accuracy[7]: Our chatbot achieved a commendable ac�curacy rate of 93% with the Gemini 1.5 Large Language Model (LLM) during training and validation phases. This high accuracy reflects the model’s robustness in under�standing complex medical queries and generating accurate responses. Response Quality[9]: The quality of responses significantly improved with Gemini 1.5, as noted by users and through systematic evaluations. Responses were not only accurate but also contextually relevant, engaging, and natural-sounding, enhancing the overall user experience. Computational Efficiency: Despite the increased complex�ity of LLMs, Gemini 1.5 demonstrated efficient computa�tional performance, striking a balance between accuracy and resource utilization. Training times were reasonable, and inference speed met real-time interaction requirements, ensuring smooth chatbot functionality. User Feedback: Engagement: Users reported higher engagement levels with the chatbot powered by Gemini 1.5. The conversa�tional flow, contextual understanding, and personalized re�sponses contributed to a more interactive and immersive ex�perience. Satisfaction: User satisfaction scores showed a noticeable improvement with Gemini 1.5 compared to ear�lier models. The ability to provide detailed and informative answers, handle diverse queries, and maintain coherence in conversations contributed to positive user sentiments. Feed�back Incorporation: Continuous feedback loops allowed us to fine-tune the chatbot’s responses based on user inter�actions. This iterative process, coupled with Gemini 1.5’s adaptability, facilitated ongoing improvements in response quality and user satisfaction. Insights and Observations: Semantic Understanding: Gemini 1.5 showcased ad�vanced semantic understanding, accurately interpreting user intents and extracting relevant information from varied medical contexts. This capability translated into more pre�cise and informative responses. Natural Language Genera�tion (NLG): The NLG [8]capabilities of Gemini 1.5 were in�strumental in generating human-like responses with proper grammar, coherence, and naturalness. This contributed sig�nificantly to the chatbot’s conversational abilities and user engagement. Scalability: The scalability of Gemini 1.5 allowed seamless integration with additional data sources and knowledge bases, expanding the chatbot’s scope and enhancing its capability to handle a wide range of medical inquiries. Analysis Summary:The analysis underscores the transformative impact of integrating advanced AI models like Gemini 1.5 into our healthcare chatbot. It highlights not only quantitative improvements in accuracy and effi�ciency but also qualitative enhancements in response qual�ity, user experience, and engagement. These results affirm the efficacy of LLMs in driving innovation and effectiveness in AI-driven conversational systems tailored for specialized domains like healthcare.