11 Artificial intelligence in diagnostics
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A. H. D. Pushpa Latha
Abstract
A comprehensive analysis of test results, medical history, and symptoms is necessary for an accurate medical diagnosis. Healthcare practitioners employ a variety of diagnostic procedures, such as blood tests, biopsies, and imaging methods, including X-rays, MRIs, and CT scans, to create efficient treatment regimens. By evaluating intricate medical data and simulating how physicians evaluate symptoms and test results, artificial intelligenceartificial intelligence (AI) improves diagnosis precision. AI systems use machine learning, particularly deep learning, to learn from massive datasets that include lab results, vital signs, demographic information, biosignals (such as electrocardiogram, electroencephalogram, and electromyography), medical pictures, and electronic health records. This facilitates accurate forecasts and well-informed medical judgments. Incorporating genetic, behavioral, and environmental factors may enhance the detection of complicated illnesses, even if existing AI models mostly rely on statistical connections. Despite its potential, AI is yet only a helpful tool that cannot take the place of skilled medical professionals’ knowledge and discretion when providing safe, individualized care.
Abstract
A comprehensive analysis of test results, medical history, and symptoms is necessary for an accurate medical diagnosis. Healthcare practitioners employ a variety of diagnostic procedures, such as blood tests, biopsies, and imaging methods, including X-rays, MRIs, and CT scans, to create efficient treatment regimens. By evaluating intricate medical data and simulating how physicians evaluate symptoms and test results, artificial intelligenceartificial intelligence (AI) improves diagnosis precision. AI systems use machine learning, particularly deep learning, to learn from massive datasets that include lab results, vital signs, demographic information, biosignals (such as electrocardiogram, electroencephalogram, and electromyography), medical pictures, and electronic health records. This facilitates accurate forecasts and well-informed medical judgments. Incorporating genetic, behavioral, and environmental factors may enhance the detection of complicated illnesses, even if existing AI models mostly rely on statistical connections. Despite its potential, AI is yet only a helpful tool that cannot take the place of skilled medical professionals’ knowledge and discretion when providing safe, individualized care.
Kapitel in diesem Buch
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
Kapitel in diesem Buch
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
