Chikitsak: An Integrated Healthcare Assistant for Symptom Analysis and Radiological DiagnosisAbstractThis paper introduces Chikitsak, an integrated healthcare assistant that combines a symptom-based conversational interface with radiological diagnostic modules. The system collects basic user information and, via an interactive chatbot, gathers detailed symptom data. Based on user input, the chatbot provides three probable disease predictions along with precautionary measures, dietary recommendations, and suggested medications. In parallel, the system offers automated diagnostic support for pneumonia and brain tumor detection by analyzing chest X-ray and brain MRI images using deep learning models. Experimental evaluation shows promising accuracy, and the integration of natural language processing (NLP) with radiological image analysis can significantly aid early disease detection and patient engagement.KeywordsHealthcare, Chatbot, Natural Language Processing, Deep Learning, Pneumonia Detection, Brain Tumor Detection, Medical Imaging, AI in Healthcare1. IntroductionThe rapid growth of artificial intelligence (AI) and deep learning in healthcare has catalyzed the development of systems that enhance early disease detection and patient management. Chikitsak leverages these technologies by integrating a symptom-based chatbot with state-of-the-art diagnostic models. The chatbot collects patient demographics and symptom descriptions, engages in a structured query process, and provides preliminary diagnoses with actionable recommendations. Complementing this, specialized models assess radiological images to detect pneumonia and brain tumors, offering a dual approach to patient screening. Such systems hold the potential to reduce the burden on healthcare professionals and improve patient outcomes by providing timely and accessible preliminary assessments.2. Related Work2.1 Chatbots in HealthcareRecent research demonstrates that chatbots can effectively deliver preliminary health advice, triage patients, and improve healthcare accessibility. Studies such as “Chatbots in Healthcare: A Systematic Review” have underscored the benefits of conversational agents in patient engagement and symptom assessment (NCBI PMC). Chatbots have been shown to reduce the workload on clinical staff while offering consistent, round-the-clock support.2.2 Deep Learning for Radiological DiagnosisDeep learning models have revolutionized medical imaging analysis. For instance, CheXNet, a convolutional neural network, achieved radiologist-level performance in detecting pneumonia from chest X-rays (CheXNet, arXiv). Similarly, CNN-based approaches have been applied successfully to brain MRI data for tumor detection, achieving high sensitivity and specificity (NCBI PMC). These advances provide a solid foundation for integrating radiological diagnostics into a comprehensive patient assessment system like Chikitsak.3. Methodology3.1 System ArchitectureThe Chikitsak system is divided into two primary modules: Symptom Analysis Module (Chatbot): User Data Collection: Users provide basic information (name, age, sex) through an initial form. Conversational Interface: A chatbot engages the user in a dynamic dialogue, asking a series of five targeted questions based on initial symptom input. Natural language processing (NLP) techniques are applied to understand and classify symptoms. Preliminary Diagnosis: After gathering sufficient data, the system predicts three potential diseases, accompanied by recommended precautions, dietary suggestions, and medications. A downloadable report is generated for user reference and clinical consultation. Radiological Diagnosis Module: Image Upload and Preprocessing: Users can upload chest X-ray and brain MRI images. Model Inference: Two separate convolutional neural network (CNN) models are deployed: Pneumonia Detection: Trained on large-scale chest X-ray datasets, the model analyzes X-ray images to predict pneumonia. Brain Tumor Detection: A dedicated CNN model processes MRI images to detect brain tumors. Output Generation: The results, including probability scores and visualizations of the detection areas, are incorporated into the final downloadable diagnostic report. 3.2 Data and Model Training Chatbot Training: The NLP engine is trained on medical conversation datasets to improve its ability to recognize symptom descriptions and contextual nuances. Data augmentation techniques are applied to ensure robust performance in varied linguistic expressions. Radiological Models: Pneumonia Model: Leveraging architectures similar to CheXNet, the model is fine-tuned using publicly available chest X-ray datasets (e.g., ChestX-ray14). Brain Tumor Model: The MRI-based model is developed using annotated brain imaging datasets available from sources such as Kaggle or institutional repositories. Both models undergo rigorous cross-validation and testing to ensure accuracy, sensitivity, and specificity.3.3 Integration and User WorkflowThe front-end interface guides users sequentially through the system: Initial Data Entry: User demographics and symptom input. Chatbot Interaction: The conversational agent asks follow-up questions to refine symptom details. Diagnostic Prediction: Based on the dialogue, the system predicts possible diseases. Image Analysis (Optional): For users opting for further diagnostics, the radiological module processes uploaded images. Report Generation: A consolidated report detailing the preliminary diagnoses, recommended next steps, and radiological analysis (if applicable) is generated and made available for download. The seamless integration of these modules ensures a comprehensive preliminary assessment that can be shared with healthcare professionals.4. Experimental Evaluation4.1 Evaluation MetricsFor the chatbot module, performance is measured using: Precision and Recall: Accuracy in predicting disease categories. User Satisfaction Scores: Evaluated through usability studies and feedback. For the radiological modules, the following metrics are used: Accuracy, Sensitivity, and Specificity: Standard performance metrics for medical image classification. Area Under the Receiver Operating Characteristic Curve (AUC-ROC): To evaluate model discrimination capabilities. 4.2 ResultsPreliminary experiments indicate that the chatbot achieves high accuracy in symptom classification, with user satisfaction ratings suggesting ease of use and clarity in communication. The pneumonia detection model demonstrates performance comparable to established benchmarks such as CheXNet (CheXNet, arXiv), while the brain tumor detection model shows promising accuracy, with further improvements anticipated through additional training on more diverse datasets.4.3 DiscussionThe integration of a symptom-based chatbot with radiological diagnostics provides a multi-modal approach to early disease detection. Although the system is not intended to replace professional medical advice, it serves as an effective triage tool, guiding users towards timely clinical evaluation. Challenges remain in ensuring robust performance across diverse populations and addressing data privacy concerns. Future iterations will focus on expanding the disease prediction library and refining image analysis algorithms.5. ConclusionChikitsak represents a significant step towards the integration of AI-driven tools in healthcare. By combining NLP-powered symptom analysis with deep learning-based radiological diagnostics, the system offers a comprehensive, user-friendly interface for preliminary disease assessment. While initial results are promising, ongoing work will refine the models, extend functionality, and further validate the system through clinical trials. This approach not only has the potential to enhance early diagnosis but also to empower patients with actionable health information.6. Future WorkFuture enhancements for Chikitsak include: Expansion of Diagnostic Capabilities: Incorporating additional disease models beyond pneumonia and brain tumors. Clinical Trials: Conducting rigorous clinical evaluations to validate the system's efficacy and safety. User Experience Enhancements: Improving the chatbot’s conversational depth and integrating multi-language support. Data Security: Strengthening data privacy measures and compliance with healthcare regulations (e.g., HIPAA, GDPR). References CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. Retrieved from https://arxiv.org/abs/1711.05225 Brain Tumor Detection Using Convolutional Neural Network. NCBI PMC Article Chatbots in Healthcare: A Systematic Review. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6460224/ WHO Guidelines on Digital Health Interventions. Retrieved from https://www.who.int/publications/i/item/9789241511642