Artificial Intelligence, Machine Learning, and Deep Learning in Health Care
| Autor: | Harsh Kailash Rathi, Pratibha Dawande, Shriram Kane, Amol Gaikwad |
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| Rok vydání: | 2022 |
| Zdroj: | ECS Transactions. 107:15981-15987 |
| ISSN: | 1938-6737 1938-5862 |
| Popis: | Artificial Intelligence (AI) and related technologies are widely used in business and community, and are integrated into health care. This technology has the potential to transform many aspects of patient care, as well as management functions within providers, payers, and pharmaceutical companies. A growing number of research shows that artificial intelligence (AI) can work better or better than humans in important health care tasks such as diagnostics. As machine learning (ML) models become more common in our daily lives, concerns about their potential for injury are growing. In medicine, optimism in the human-health function of ML is influenced by ethical concerns about the potential of tools to magnify existing health disparities. Recent research, for example, has found that when applied to women, minority races, and ethnic groups, as well as those with social insurance, modern clinical prediction algorithms do well. When popular language language models are trained in science textbooks, they complete the templates of clinical notes to compliment hospitals for violent white fathers, according to other studies. Conclusion: While in-depth learning as a means of analysis and modeling has some advantages at the moment, it also highlights a major trend: the integration of health sciences with data. Obstacles to access to health care will be higher than many other industries, where billions of people use in-depth learning and other forms of electronic learning software every day. |
| Databáze: | OpenAIRE |
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