• Mohammed Faisal
• Pranit Chatterjee
• Praveen A
• Shriddhi Sinha
• Pavana H

Bridging the gap between symptom description and visual diagnosis remains a profound challenge in modern healthcare, often leaving patients and practitioners seeking more integrated support. Recent advances in multimodal AI—combining natural‑language understanding with medical image interpretation—promise to transform this landscape by delivering unified, context‑aware insights. This review examines state‑of‑the‑art systems that fuse transformer‑based language models with convolutional and vision‑transformer encoders to handle text queries alongside radiology scans, dermatology photographs, and pathology slides. We explore data‑fusion strategies, explainability mechanisms, and deployment optimizations that enable real‑time, trustworthy recommendations. By reflecting on design principles, performance trade‑offs, and ethical considerations, we illuminate how such chatbots can evolve from technical curiosities into philosophical partners in care—machines that not only analyze but also empathize. The overall synthesis offers a roadmap for building AI assistants that enhance clinical decision‑making while honoring the human experience of illness.

Multimodal AI, Medical chatbot, Deep learning, Image analysis, Natural language understanding

"A COMPREHENSIVE REVIEW OF MULTIMODAL AI CHATBOTS FOR MEDICAL DIAGNOSIS AND CARE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.c252-c257, May-2025, Available :http://www.jetir.org/papers/JETIR2505231.pdf

"A COMPREHENSIVE REVIEW OF MULTIMODAL AI CHATBOTS FOR MEDICAL DIAGNOSIS AND CARE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppc252-c257, May-2025, Available at : http://www.jetir.org/papers/JETIR2505231.pdf