Table 1 Comparative study (1).
Research problem | Research contributions | Findings/research gaps | References |
|---|---|---|---|
The integration of biomedical sensors for infection management of cancer symptoms and diagnosis using MXenes | The major contribution of this paper is to bring an attention on authorities employ bio-nanotech to deploy an accurate, robust, and cheap biosensors to diagnose biomedical symptoms that involved in cancer diseases | MXene synthesis and sensing characteristics MXene biosensor limitations in terms of other diseases diagnosis | |
In this proposal, we investigate the integrational approach that is based on of AI-enabled machine learning and deep learning algorithms used for training models regarding biomedical diagnosis, especially cancer diagnosis and treatment | A list of approaches presented, even though self-learning enabled architecture Highlighting positive and negative results after evaluation | Standardized data hierarchy is missing No data organization and optimization strategy utilized simultaneously | |
A real-time monitoring approach is presented in the proposed work, where the association of AI is critical in process of collection, management, organization, optimization, and presentation throughout | The proposed work targets the industrial healthcare 4.0 Presenting improved self-management of chronic disease | Costly approach for real-time monitoring due to the requirement of wearable integration Scope of data privacy is another challenging prospect | |
The role of nanorobotics, like nanospheres, nanorods, and nanoparticles for biomedical diagnosis, including cancer diagnosis is proposed in this paper | Used crafting techniques for miniature machines Transformative real-time development for cancer diagnosis | Challenging on fabrication technique Privacy and security concerns | |
The problem that addressed in this paper is the designing hierarchy of interpretable machine learning system for enhancing trustworthiness in healthcare environment using collaborative approach | This paper highlights the associative challenges involved in the interpretable machine learning The proposal is designed for cancer diagnosis and their clinical trails | Lack in AI clinical decision that leads mistrust between decision makers System’s interoperability issues |
