This project introduces a novel approach GPTFX, an AI-based mental detection with GPT frameworks. This approach leverages GPT embeddings and the fine-tuning of GPT-3. This approach exhibits superior performance in both classifying mental health disorders and generating explanations with accuracy of around 87% in classification and Rouge-L of around 0.75. We utilized GPT embeddings with machine learning models for the classification of mental health disorders. Additionally, GPT-3 was fine-tuned for generating explanations related to the predictions made by these machine learning models. Notably, the proposed algorithm proves well-suited for realtime monitoring of mental health by deploying in AI-IoMT devices, as it has demonstrated greater reliability when compared to traditional algorithms.

In this project, we delve into the fascinating world of 2D nanostructures and their potential as telemedicine platforms for future healthcare systems. Telemedicine, with its continuous surveillance capabilities, holds immense promise for personalized health monitoring. We explore how 2D materials like graphene and MXene are revolutionizing this field with their unique properties. Graphene, with its exceptional conductivity and surface-to-volume ratio, emerges as a versatile tool for biosensing, drug delivery, and bioimaging in telemedicine applications. Meanwhile, MXene offers excellent biocompatibility and surface chemistry, making it ideal for biosensing and drug delivery. But the real magic happens when these materials are combined into hybrid nanocomposites, opening up new avenues for enhanced telemedicine platforms. Furthermore, integrating 2D nanomaterials with artificial intelligence amplifies their potential, enabling personalized healthcare solutions through real-time data analysis and modeling.

The future of telemedicine is bright, and these advancements in 2D nanomaterials and AI are paving the way for elevated patient outcomes and cutting-edge healthcare solutions.

Globally, the number of cancer cases is rising, with no difference between rich and undeveloped countries. Although affluent nations have made great improvements in the quality of cancer detection and treatment, many nations still face serious obstacles. Basic cancer screening facilities and industry-standard cancer management tools are still inaccessible in these nations. Currently, there are many challenges in the field of cancer research, from the earliest screening procedures to dealing with cancer recurrence. These difficulties highlight the urgent requirement for quick screening tools for better cancer management and a reduction in the mortality rate. In this review, we focus on early-stage disease management, quicker analysis of patient samples, multiple diagnostics for better recovery, and continuous monitoring to avoid disease recurrence and future possibilities. Point of care (POC) technology has been introduced in the health sector to reduce the mortality rate of any disease by detecting early symptoms. Current cancer treatment technology has reduced the mortality rate, but early detection, early diagnosis, and frequent monitoring through various POC systems can provide better survival of the patient and reduce the death rate. Lab on a chip (LOC), organ on a chip (OOC), 3D printing, wearable sensors, internet of things (IoT), artificial intelligence (AI), and next-generation sequencing can provide a smart and digital way to manage cancer by monitoring an individual's physiological and mental biometrics.