The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.
Disease diagnoses could be sometimes very easy tasks, while others may be a bit trickier. There are large data sets available; however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods which are used to diagnose a disease are manual and error-prone. Usage of Artificial Intelligence (AI) predictive techniques enables auto diagnosis and reduces detection errors compared to exclusive human expertise.
There was a need for an automatic diagnostic system that provides benefits from both human knowledge and accuracy of the machine. A suitable decision support system is needed to achieve accurate results from the diagnosis process with reduced costs. Classification of diseases depending upon various parameters is a complex task for human experts but AI would help to detect and handle such kinds of cases. Currently, various AI techniques have been used in the field of medicine to accurately diagnose illnesses.
AI is an integral part of computer science by which computers become more intelligent. The vital need for any intelligent system is learning. There are various techniques in AI that are based on Learning like deep learning, machine learning, etc. Some specific AI methods that are significant in the medical field named as a Rule-based intelligent system, provides a set of if-then rules in healthcare, which act as a decision support system. Gradually, intelligent systems are being replaced in the medical field by AI-based automatic techniques where human intervention is very less. The neural network or artificial neural network (ANN) is a large collection of neural units designed based on biological neurons connected in the brain. It is a simulation of the human brain and works exactly like it. Each neural unit is linked with many other neurons approximately similar to the bipartite graph. These kinds of systems learn and are trained automatically.
Artificial Intelligence has three important features which are very important for the healthcare sector; Deep Learning, Machine Learning and Fuzzy Logic.
Deep Learning:
Finding the possibilities and predictions regarding health issues is a tedious task for doctors and surgical experts. In some cases, ANN provides decisions regarding healthcare at rapid speed wherein the systems can collect data, understand
it, and detect pieces that will play a vital role in prediction. Deep learning based on algorithms, is used in the medical field to assist specialists for the examination of any illness. Thus, resulting in better medical decisions. Deep learning provides benefits in different fields such as drug discovery, medical imaging, Genome, detecting Alzheimer’s disease.
Deep learning has got great interest in each field and especially in medical image analysis. The term deep learning refers to the utilization of deep neural network models. The main component of the neural network is the simulation of the human brain in the form of neurons. It works on the scenario in which different signals are used as input, join them using weights and pass those joined signals to produce output. The AANs (artificial neural networks) and deep learning can be differentiated by the variations in a number of hidden layers, their inter-connectivity and the efficiency to yield a suitable result of the inputs. The ANNs are generally constituted of three different layers and are instructed to retrieve well-structured information that could be suitably utilized only to perform the specialized task.
Fuzzy Logic:
The combination of Artificial Neural Networks and Fuzzy Logic Systems enables the representation of real-world problems via the creation of intelligent and adaptive systems. By adapting the interconnections between layers, Artificial Neural networks are able to learn. A computing framework based on the concept of fuzzy set and rules as well as fuzzy reasoning is offered by fuzzy logic inference systems. The fusion of the aforementioned adaptive structures is called a "Neuro-Fuzzy" system. This fusion could be applied for pattern recognition in medical applications.
Machine Learning:
It is a field that comes within the broader area of AI in which by training, a machine learns itself and performs tasks. In machine learning, there are algorithms for supervised learning (under the control and “guidance” of a human expert) in which we are initially aware about both input and results, as well as unsupervised learning (requiring very little human intervention or domain expert’s service) where we are not aware of what will be the results. A machine is trained to learn a concept by giving examples and creating pattern models that are supposed to differentiate between two or more objects. In the medical field, machine learning assists the experts to handle large and complicated medical data and also helps to investigate the results. The output of this process can be used for further research. Therefore, when machine learning is applied in healthcare, it increases the trust level of patients in medical science in order to predict a disease by implementing machine learning algorithms.
Artificial Intelligence promises to revolutionize clinical decision making and diagnosis. However, existing Artificial Intelligence approaches to diagnosis are purely associative, identifying diseases that are strongly correlated with a patient’s symptoms.
