Generative AI in Disease Diagnosis

Artificial intelligence AI is a sophisticated model or algorithm that has transformed a number of industries, including the medical field. In generative artificial intelligence, content is created using models or algorithms. Generative AI in healthcare revealed numerous new application domains for illness diagnosis. It has given models new outputs that they may use to create unique and fresh content. These can be used to learn new patterns that closely resemble the original data. The models covered in this review article serve as diagnostic tools in a variety of medical imaging applications. Medical specialists use generative AI in medical imaging analysis as a preliminary tool to generate large amounts of data that they can then correlate and extract useful information from. It is widely used in medical imaging, such as x-rays, magnetic resonance imaging, CTscan, COVID, cancer areas are utilized as AI diagnostic tools. Artificial intelligence (AI) has undergone a revolution because of to deep learning, which has made it possible for machines to learn from vast amounts of data and carry out complicated tasks based on that data. One of the most exciting uses of deep learning is in the creation of generative models, which are deep learning models that can produce realistic images, videos, and audio. These models have a variety of uses in the healthcare industry; including imaging. Medical imaging now has a plethora of options because to generative AI, a subfield of AI that focuses on producing original information. With improved patient outcomes, tailored treatment regimens, and greater diagnostic capabilities, it gives healthcare providers more control. Generative AI has transformed medical picture processing, interpretation, and application in clinical practice by harnessing the power of deep learning algorithms. Better diagnosis, New medicine discovery, Personalized medicine Improved medical imaging: The quality of medical images can be raised with the application of generative AI. With the aid of this technology, physicians may be able to diagnose and treat patients early by seeing more detail in images, More effective surgery: Virtual patient models can be produced using generative artificial intelligence, Surgeons can be trained and surgery plans can be made using these models, enhanced rehabilitation ,better mental health care: Chatbots that can provide patients therapy can be developed using generative AI. Medical imaging analysis is a field of healthcare that has revolutionized the way we approach patient care. The ability to examine medical images such as X-rays, CT scans, and MRI scans has allowed doctors and healthcare professionals to gain a better understanding of a patient's condition, monitor their illness, and plan their treatment accordingly. However, with the vast amounts of data produced by medical imaging technology, it is essential to have specialist analysis to retrieve useful information. One of the most significant advancements in medical imaging analysis has been the development of generative image AI models. These models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Auto encoders, have enabled healthcare professionals to analyze medical images more effectively and efficiently. The GAN model is one of the most popular generative picture AI models. It comprises two neural networks - a generator network and a discriminator network. The discriminator network's primary role is to discern between artificial and real visuals, while the generator network creates synthetic images. In an adversarial training process, the discriminator and generator work together to accurately categorize artificial and real images, with the generator trying to trick the discriminator. This technique has proved useful in radiology, where GANs are used for tasks such as image synthesis, image de-noising, and image segmentation. Image segmentation is a technique used in medical imaging analysis to split an image into several areas or segments. This has proved in identifying specific structures or regions in medical images, such as tumors, blood vessels, or organs. Generative deep models such as VAEs, GANs, and diffusion models have been applied to the augmentation of medical data, providing healthcare professionals with a more comprehensive understanding of a patient's condition. The development of generative image AI models has revolutionized medical imaging analysis, providing healthcare professionals with a more efficient and effective way to analyze medical images. Medical imaging has been a vital tool for healthcare professionals in diagnosing and treating various medical conditions. However, the interpretation of medical images is often challenging due to the complexity of the human body and the variations in anatomical structures. Therefore, image processing techniques are utilised to enhance the quality of medical images and extract relevant information. In this regard, fundamental image processing operations such as image segmentation, registration, and feature extraction are commonly utilised. However, the application of these operations might not always be appropriate due to the intricate structures of medical images, which include various anatomical variations and irregular tumour shapes. In the case of brain imaging, the presence of different tissues and structures, such as grey matter, white matter, and cerebrospinal fluid, can make it challenging to distinguish between healthy and diseased areas. More advanced image processing techniques such as machine learning algorithms are becoming increasingly popular for medical image analysis. The creation of irrelevant images that disturb the logical structure of the image can also occur due to the complexity of medical images. This adversely affect the accuracy of the interpretation of the image, leading to incorrect diagnoses and treatment plans. Hence, there is a need for sophisticated image processing techniques that can remove unwanted features and highlight the crucial information in the image. The performance of the model employed for image analysis might also be adversely affected by aberrant data and image deformations. Pre-processing techniques like noise reduction and picture normalization are essential for guaranteeing that the image is free of aberrations that could affect the interpretation's correctness. In conclusion, more sophisticated methods are needed to overcome the difficulties posed by the complexity of the images, fundamental image processing processes can be useful in improving the quality of medical images. In the end, these methods may improve patient outcomes by diagnosis.