Taran Rishit Undru1, Utkarsha Uday2, Jyothi Tadi Lakshmi3, Ariyanachi Kaliappan4, Saranya Mallamgunta5, Shalam Sheerin Nikhat6, V Sakthivadivel7, Archana Gaur8. 1. Systems Engineer at TCS, Backend developer at TCS-Apple, Hyd, India. 2. West Bengal University of Health Sciences, Kolkata, India. 3. Department of Microbiology, All India Institute of Medical Sciences, Bibinagar, India. 4. Department of Anatomy, AIIMS Bibinagar, Bibinagar, Yadadri-Bhuvanagiri dist., India. 5. Department of Microbiology, ESIC Medical College and Hospital Sanath Nagar, Hyderabad, India. 6. Department of Microbiology, AIIMS Bibinagar, Bibinagar, Yadadri-Bhuvanagiri dist., India. 7. Department of General Medicine, AIIMS Bibinagar, Bibinagar, Yadadri-Bhuvanagiri dist., India. 8. Department of Physiology, AIIMS Bibinagar, Bibinagar, Yadadri-Bhuvanagiri dist., India.
Abstract
Introduction: The development of medical artificial intelligence (AI) is related to programs intended to help clinicians formulate diagnoses, make therapeutic decisions and predict outcomes. It is bringing a paradigm shift to healthcare, powered by the increasing availability of healthcare data and rapid progress in analytical techniques (1). Artificial intelligence techniques include machine learning methods for structured data, such as classical support vector machines and neural networks, modern deep learning (DL), and natural language processing for unstructured data. Methodology:More than 50 articles were reviewed and 41 of them were shortlisted. The review was based on a literature search in PubMed, Embase, Google Scholar, and Scopus databases. Review:Laboratory medicine incorporates new technologies to aid in clinical decision-making, disease monitoring, and patient safety. Clinical microbiology informatics is progressively using AI. Genomic information from isolated bacteria, metagenomic microbial results from original specimens, mass spectra recorded from grown bacterial isolates and large digital photographs are examples of enormous datasets in clinical microbiology that may be used to build AI diagnoses. Conclusion: Technological innovation in healthcare is accelerating and has become increasingly interwoven with our daily lives and medical practices such as smart health trackers and diagnostic algorithms.
Introduction: The development of medical artificial intelligence (AI) is related to programs intended to help clinicians formulate diagnoses, make therapeutic decisions and predict outcomes. It is bringing a paradigm shift to healthcare, powered by the increasing availability of healthcare data and rapid progress in analytical techniques (1). Artificial intelligence techniques include machine learning methods for structured data, such as classical support vector machines and neural networks, modern deep learning (DL), and natural language processing for unstructured data. Methodology:More than 50 articles were reviewed and 41 of them were shortlisted. The review was based on a literature search in PubMed, Embase, Google Scholar, and Scopus databases. Review:Laboratory medicine incorporates new technologies to aid in clinical decision-making, disease monitoring, and patient safety. Clinical microbiology informatics is progressively using AI. Genomic information from isolated bacteria, metagenomic microbial results from original specimens, mass spectra recorded from grown bacterial isolates and large digital photographs are examples of enormous datasets in clinical microbiology that may be used to build AI diagnoses. Conclusion: Technological innovation in healthcare is accelerating and has become increasingly interwoven with our daily lives and medical practices such as smart health trackers and diagnostic algorithms.
Authors: Orly Ardon; Marc Roger Couturier; Blaine A Mathison; Jessica L Kohan; John F Walker; Richard Boyd Smith Journal: J Clin Microbiol Date: 2020-05-26 Impact factor: 5.948
Authors: Adam Yala; Regina Barzilay; Laura Salama; Molly Griffin; Grace Sollender; Aditya Bardia; Constance Lehman; Julliette M Buckley; Suzanne B Coopey; Fernanda Polubriaginof; Judy E Garber; Barbara L Smith; Michele A Gadd; Michelle C Specht; Thomas M Gudewicz; Anthony J Guidi; Alphonse Taghian; Kevin S Hughes Journal: Breast Cancer Res Treat Date: 2016-11-08 Impact factor: 4.872