Modernization in the healthcare industry is happening with the support of artificial intelligence and blockchain technologies. Collecting healthcare data is done through any Google survey from different governing bodies and data available on the Web of Sciences. However, the researchers continually suffered on developing effective classification approaches. In the recently developed models, deep learning is used for better generalization and training performance using a massive amount of data. A better learning model is built by sharing the data from organizations like research centers, testing labs, hospitals, etc. Each healthcare institution requires proper data privacy, and thus, these industries desire to use efficient and accurate learning systems for different applications. Among various diseases in the world, lung cancer is one of a hazardous diseases. Thus, early identification of lung cancer and followed by the appropriate treatment can save a life. Hence, the Computer Aided Diagnosis (CAD) model is essential for supporting healthcare applications. Therefore, an automated lung cancer detection models are developed to identify cancer from the different modalities of medical images. As a result, the privacy concern in clinical data restricts data sharing between various organizations based on legal and ethical problems. Hence, for these security reasons, the blockchain comes into focus. Here, there is a need to get access to the blockchain by healthcare professionals for displaying the clinical records of the patient, which ensures the security of the patient’s data. For this purpose, artificial intelligence utilizes numerous techniques, large quantities of data, and decision-making capability. Thus, the medical system must have democratized healthcare, reduced costs, and enhanced service efficiency by combining technological advancement. Therefore, this paper aims to review several lung cancer detection approaches in data sharing to help future research. Here, the systematic review of lung cancer detection models is done based on ML and DL algorithms. In recent years, the fundamental well-performed techniques have been discussed by categorizing them. Furthermore, the simulation platforms, dataset utilized, and performance measures are evaluated as an extended review. This survey explores the challenges and research findings for supporting future works. This work will produce many suggestions for future professionals and researchers for enhancing the secure data transmission of medical data.
