Introduction
Drastic improvements in Artificial Intelligence imitate the human brain to a great extent. This feature is one of the reasons for the major achievement of AI in healthcare. This is achieved by the vast and widespread availability of health care techniques and medical datasets. This article mainly concentrates on the medical applications of AI in the current day to day life and discusses the future of AI in healthcare. Both structured and unstructured Machine Learning techniques are made use of to summarize our results. AI finds its major advantages in the medical field categories like cancer, cardiology, neurology and many more. An AI System mainly finds its application in the medical field in helping to reduce diagnostic and therapeutic errors that are inevitable in human clinical practice. In the near future, AI will definitely assist human physicians in making better clinical decisions by replacing human judgment in certain functional areas like radiology and pathology.
Foot print of AI in healthcare
An AI system retrieves information from a large group of patient population to guide in making real time alerting and predicting health risks. Sophisticated algorithms will be used in collecting large healthcare dataset. Some of the learning techniques for structured data like Classical Support Vector Machine algorithms, Neural Networks and Modern Deep Learning algorithms will be used. Similarly, unstructured data uses Natural Language Processing techniques. AI Systems will then be trained for screening, diagnosis, treatment assignment and so on. With the help of the obtained insights, AI helps in assisting clinical practice. It can even inculcate self-correcting abilities in improving accuracy using the collected feedback. The big data analytic method goes a long way in making this possible.
AI- healthcare Data sources
AI Systems reviews literatures related to diagnosis imaging, genetic testing and electro diagnosis. Others data sources include physical examination notes and clinical laboratory results. They will be distinguished with image, genetic and electro physiological data because of the presence of large portions of unstructured narrative texts. AI Systems will then concentrate on the conversion of this unstructured text to machine-understandable EMR (Electronic Medical Record). Data sources like medical records, disease registers and peer reviewed literatures will also be used.
AI techniques in Healthcare
The underlying techniques are mainly categorized as classical Machine Learning (ML) Techniques for structured data and Deep Learning techniques, Natural Language Processing (NLP) methods for unstructured data. ML method aims in inferring the probability of the disease outcomes while the NLP methods extract information from unstructured data and convert them to machine readable structured ones. Deep Learning is able to learn from data that is both unstructured and unlabelled.
Machine Learning
ML in healthcare has recently made headlines. Cancerous Tumors on mammograms can be identified by Google’s ML Algorithm. Stanford uses ML Algorithm in identification of skin cancer. Lot of Deep Machine Learning Algorithms are even capable of diagnosing Diabetic Retinopathy in retinal images. All these algorithms aims in training the system to look at images identify abnormalities and point to areas that need attention.
Pattern Classification in Machine Learning
Supervised Learning
In Supervised Learning, the machine will be trained with a given data set that is already labeled or classified. Supervised algorithms will be trained to classify patterns from given data set and give the desired outcome. This technique makes use of two categories of algorithms. ‘Classification algorithms’ is the first type where the output variable is a category. The second type is ‘Regression’ where the output variable is a real value like ‘dollars’ or ‘weight’.
Unsupervised Learning
Unsupervised Learning allows the algorithm to act without guidance. It is a type of self-organized learning which helps physicians in a long term aspect. Machines will be trained using unlabelled or unclassified data and the task of the machines here will be a bit tedious. We expect the machines to group unsorted information based on similarities and differences in patterns without prior training of data. Unsupervised learning goes a long way in finding features useful for categorization. Chatbots, self-driving cars, facial recognition programs, expert systems and robots are some common examples. The technique employs two categories of algorithms like ‘Clustering’ and ‘Association’. Clustering techniques deal with finding the structure or pattern in a collection of uncategorized data. Clustering algorithms will analyze input data and identify clusters if they exist. Some of the available clustering types include
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- Exclusive clustering or partitioning -where one data can belong to only one cluster. K-means clustering comes under this type.
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- Agglomerative Clustering -where every data is a cluster. Hierarchical clustering is Agglomerative type.
- Overlapping Clustering-where fuzzy set technology is employed to cluster data. Each point will be a part of two or more clusters having separate degrees of membership. The Fuzzy C-Means comes under this type.
Reinforcement Learning
Here machines learn by trial and error method. Systems are trained by virtually “rewarding” for a correct guess and “punishing” for a wrong one. The machine observes its environment and makes decisions. If the observation becomes negative, the algorithm trains itself to make a better decision next time. The algorithms usually try their best to achieve greatest value for rewards.
Deep Learning
This is a statistical learning wherein features or attributes are extracted from raw data sets. Multi-layered Artificial neural networks with multiple stacked layers will be the main concept of this technique. Algorithms here will be more sophisticated and will need more powerful resources.
Feature Extraction and Pattern Classification in Deep Learning
Neural Networks
Our brain is a very complex network with 10 billion neurons each connected to 10 thousand other neurons. Electrochemical signals will be received by each of these neurons and then be passed to other neurons. Deep learning, inspired by the functionality of the brain neurons lead to the evolution of Artificial Neural Network concept. This technique uses layers of artificial neurons that will receive input. An activation function along with a human threshold set will then be applied to receive the expected output. Image classification, Speech or handwriting recognition, and autonomous driving are some of the common examples.
Natural Language Processing
This deals with the application of Artificial Intelligence in Interactions between computers and human languages. It is the automated manipulation of speech and text by software. Machines will be trained to read, decipher, understand and make sense of human natural languages. Google translate, Interactive voice response, Personal Assistant Applications are some common examples.
AI Technologies dominate healthcare
AI has shown to be very accurate and effective in the diagnosis of various health risks. AI-based grading algorithms can be used to screen Fundus photographs of diabetic patients and then based on the suggestions; the patients can be advised for ophthalmologist visits. Several deep learning and neural network algorithms are believed to provide accurate diagnosis and treatment decisions for congenital cataracts. Well trained AI algorithms are capable of diagnosing and classifying skin cancer with an equal level of competence as can be done by a well-qualified dermatologist. Cardiovascular risk prediction is another great area of focus by AI algorithms. Deep learning algorithms can use the MRI of the brain to predict autism in 6 to 12 months old infants. This is another valuable achievement in the field of neuroscience.
AI has been of great importance even by Psychiatrists to evaluate the mental illness of patients. All these puts AI in direct competence with physicians in two great evolving fields like radiology and pathology due to its diagnostic and predictive capabilities. Three Dimensional cardiac motions on cardiac MRI can predict survival outcome irrespective of conventional risk factors for newly diagnosed pulmonary hypertension patients. The list goes on and there are limitless applications of AI in the medical industry. AI has not left out even a single branch of medical science without rendering its algorithmic diagnostic and suggestive techniques.
Conclusion
AI may be as good as or sometimes even better than human physicians in the prediction and suggestion of treatment measures. But we should always remember that no algorithms can replace the noble human love and care that a patient is being offered by professional doctors. All the enhancements in AI-based technologies should be applied for the well being of human lives legally and ethically.
