Applications of Artificial Intelligence in Medicine
Advancements in medical technology have allowed physicians to better diagnose and treat multiple patients. Thanks to the continuous development of technology in the medical field, countless lives have been saved and the overall quality of life in poor continues could be improved over time.
Medical imaging has demonstrated its utility in improving and accelerating the medical diagnosis of several illness. Medical imaging has also become more and more important in diagnosis with the development of image processing and computer techniques. Huge amounts of medical images are obtained by X-ray radiography, CT, and MRI. They provide essential information for prompt and accurate diagnosis based on advanced computer vision techniques.
Recent advances in digital imaging and computer hardware technology have led to an explosion in the use of digital images in a set of medical applications. These applications result from the interaction between fundamental scientific research on the one hand and the development of new and high-standard technology on the other.
Artificial intelligence has emerged as a powerful tool for information processing, decision making, and knowledge management. The techniques of Artificial Intelligence have been successfully developed in areas such as neural networks, fuzzy systems, and evolutionary algorithms. It is predictable that in the near future Artificial Intelligence will play a more important role in the medical context.
Medical images that can be considered for diagnosis has several artefacts and noise produced by the physical method with which they are taken. Normally, classical image processing methods often face great difficulties while dealing with images containing noise and distortions. Under such conditions, the use of Artificial Intelligence approaches has been recently extended to address challenging real-world image processing problems.
The interest on medical imaging with Artificial Intelligence approaches among researchers is increasing day by day as it is branded by huge volumes of research works that get published in leading international journals and international conference proceedings.
Under all these considerations, in my research, I will develop computer algorithms to aid clinicians in the interpretation of medical images and thereby improve the diagnostic process.
In this PhD research, the anomaly detection task is approached as an optimization problem, and artificial intelligence techniques are used to build the shape approximation. These methods are mainly evolutionary computation techniques (ECT), which are based on several biological or social phenomena.
In the detection process, ECT search the entire parameter space of the optimization problem through candidate solutions (individuals). An objective function is employed to evaluate the quality of the detection. Conducted by the values of such objective function, the group of candidate solutions are modified through evolutionary operators so that the optimal detection can be found.
Along my previous studies, I have already had several experiences in the develop of efficient algorithms for leukocyte detection, and I try to expand such concepts to automated detection and diagnosis in breast MRI, ultrasound and tomosynthesis, chest radiographs and chest CT, prostate MRI, neuro-imaging and the analysis of retinal and digital pathology images. It is my goal to have a significant impact on healthcare by bringing such advances to the clinic context.