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By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.
Recent advancements in biomedical research in the era of AI and ML
Prediction of cardiovascular diseases using random forest and naive Bayes algorithm
Big data analytics for personalized medicine
Intellection of biological life in current era
Integrating artificial intelligence techniques for analysis of next-generation sequencing data
Artificial intelligence: the future of neuroscience
Role of big data and artificial intelligence for COVID-19 and cancer diagnosis and treatments
Integrating screening modalities for early and precision-oriented evidence-based screening of cervical cancer – a holistic approach
Role of artificial intelligence and machine learning in diagnosis and treatment of women centric cancer
The role of artificial intelligence, machine learning and deep learning in the diagnosis, prognosis and treatment of cancers primarily associated with women
Oropharyngeal cancer prognosis based on clinicopathologic and quantitative imaging biomarkers with multiparametric model and machine learning methods
Artificial intelligence and machine learning in healthcare: an ethical perspective
Artificial intelligence in dentistry: current issues and perspectives
AI for pattern recognition and objectivity: the case of melanoma detection
Ethical horizons of biobank-based artificial intelligence in biomedical research