Bioinformatics

Integration of Machine Learning and AI in Diseases Diagnose

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Key summons for the translation of AI systems in healthcare include those intrinsic to the science of machine learning, logistical difficulties in implementation, and consideration of the barriers for adoption as well as of the necessary sociocultural or pathway changes. Performance metrics should aim to capture real clinical applicability and be understandable to intended users. Regulation that balances the pace of innovation with the potential for harm, alongside thoughtful post-market surveillance, is needed to ensure that patients are not exposed to dangerous interventions nor deprived of access to beneficial innovations. Mechanisms to enable direct comparisons of AI systems must be developed, including the use of independent, local and representative test sets. Developers of AI algorithms must be vigilant to potential dangers, including dataset shift, accidental fitting of confounders, unintended discriminatory bias, the challenges of generalization to new populations, and the unintended negative consequences of new algorithms on health outcomes.

Machine learning and computer vision have increased many aspects of human visual perception to identify clinically meaningful patterns in, e.g., imaging data,10 and neural networks are been used for variety of tasks ranging from medical image segmentation, generation, classification, and prediction of clinical data sets. Broadly academic research labs, biotechnology corporations, and technology companies have been exploring the use of AI and ML in three key areas:

  • Machine-based learning to predict pharmaceutical properties of molecular compounds and targets for drug discovery 
  • Using pattern recognition and segmentation techniques on medical images (from, e.g., retinal scans, pathology slides and body surfaces, bones and internal organs) to enable faster diagnoses and tracking of disease progression generative algorithms for computational augmentation of existing clinical and imaging data sets
  • Developing deep-learning techniques on multimodal data sources such as combining genomic and clinical data to detect new predictive models.

The purpose of Artificial Intelligence is to make computers more useful in solving problematic healthcare challenges and by using computers we can interpret data which is obtained by diagnosis of many chronic diseases like Alzheimer, Diabetes, Cardiovascular diseases and various types of cancers like breast cancer, colon cancer etc. It helps in early detection of various chronic diseases which reduces economic burden and severity of disease. Various automated systems and tools like Brain-computer interfaces (BCIs), arterial spin labeling (ASL) imaging, ASL-MRI, biomarkers, iT bra, Natural language processing (NLP)and various algorithms helps to minimize errors and control disease progression. The computer helped diagnosis, decision support systems, expert systems and implementation of software may assist physicians to minimize the intra and inter-observer variability. To streamline the process of diagnosis of artificial intelligence methods specifically artificial neural networks (ANN), Fuzzy approach can be implemented to handle diverse types of medical data. ANN technique discovers the hidden patterns and correlation in medical data and is effective in designing support systems in the clinical field. The application of AI facilitates interpretation of results with high accuracy and speed. 

Use of AI in Many Fields

  • Artificial intelligence in breast cancer
  • Management of Alzheimer’s disease with artificial intelligence
  • Management of diabetic complications using artificial intelligence
  • Artificial neural network in diagnosis of cardiovascular diseases

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