INTRODUCTION: One of the fastest developing technologies in the present world is the Virtual assistants. These assistants could manage with the medical-related tasks and helps both the providers as well as the patients. Through Artificial Intelligence, users can communicate through voice or text interface with the chatbot and also receive responses based on that. Basically, a chatbot can converse with a real person. OBJECTIVES: In this paper, a chatbot named Medbot stands for Medical Diagnosis System using Artificial Intelligence mainly focusing of skin and eye related problems. METHODS: The Medbot helps in connecting the available patients, make their appointments, helps in finding the specialized person and provide them access to get the accurate treatment. RESULTS: This Medbot helps in providing healthcare support online for 24×7 and it also responds to common and precise questions. CONCLUSION: It helps in generating the leads and forwarding them to the sales automatically. By querying the patients sequentially, it guides them with the problem they are facing with.

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In recent years, the concept of image encryption and data concealing has become a major area of research that significantly improves the secure communication of images with hidden data in them. The usage of the cloud has been outstanding in several aspects so as its security to ensure privacy over the data stored in it. The concept of reversible image steganography is one such stream where the cloud users are highly relying upon the privacy of the data, they store in the cloud servers. The reversible image steganography has the potential to recover the original image from the stego‐image once the embedded data is extracted. In this research work, an effective distributive Tian's method is proposed to embed the data onto an encrypted image without degrading the performance of the encrypted image. The cross‐verification of lossless image steganography can be identified when the original image is getting extracted from the encrypted image. The result of the proposed method ensures that the distributive Tian's method outperforms the recent state‐of‐art methods. The proposed method is deployed onto six different images in different aspects to ensure its significance.

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