Curing with Code: The Intersection of AI, ML, and Medical Science
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2025
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| _version_ | 1866902161121607680 |
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| author | Jaskirat Singh Chawla |
| author_facet | Jaskirat Singh Chawla |
| contents | <p>Artificial Intelligence (AI) has emerged as a transformative force in medical science, revolutionizing healthcare technology by enhancing diagnostic accuracy, optimizing treatment strategies, and automating clinical workflows. AI-driven machine learning (ML) algorithms are widely used in medical imaging, enabling early disease detection and precise identification of abnormalities in radiology, pathology, and dermatology. Additionally, AI-powered predictive analytics assists in prognosis estimation, personalized medicine, and drug discovery, allowing for more effective and targeted treatments.</p> <p>AI has also facilitated advancements in robotic surgery, improving precision and minimizing surgical risks. Furthermore, virtual health assistants and AI-driven natural language processing (NLP) are transforming patient interactions and optimizing electronic health records (EHRs) by extracting meaningful insights from vast unstructured medical data. However, despite these advancements, challenges such as algorithmic bias, data privacy, ethical AI in healthcare, and regulatory challenges remain significant barriers to widespread adoption. Ensuring the responsible deployment of AI requires addressing these concerns while maximizing its potential for clinical decision support and healthcare automation.</p> <p>This paper explores the role of AI in medical science, analyzing key applications, recent advancements, and potential future developments. By overcoming existing limitations, AI can revolutionize modern healthcare, paving the way for a more efficient, accurate, and personalized medical landscape.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17430980 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Curing with Code: The Intersection of AI, ML, and Medical Science Jaskirat Singh Chawla AI-driven machine learning (ML) Artificial Intelligence natural language processing (NLP) <p>Artificial Intelligence (AI) has emerged as a transformative force in medical science, revolutionizing healthcare technology by enhancing diagnostic accuracy, optimizing treatment strategies, and automating clinical workflows. AI-driven machine learning (ML) algorithms are widely used in medical imaging, enabling early disease detection and precise identification of abnormalities in radiology, pathology, and dermatology. Additionally, AI-powered predictive analytics assists in prognosis estimation, personalized medicine, and drug discovery, allowing for more effective and targeted treatments.</p> <p>AI has also facilitated advancements in robotic surgery, improving precision and minimizing surgical risks. Furthermore, virtual health assistants and AI-driven natural language processing (NLP) are transforming patient interactions and optimizing electronic health records (EHRs) by extracting meaningful insights from vast unstructured medical data. However, despite these advancements, challenges such as algorithmic bias, data privacy, ethical AI in healthcare, and regulatory challenges remain significant barriers to widespread adoption. Ensuring the responsible deployment of AI requires addressing these concerns while maximizing its potential for clinical decision support and healthcare automation.</p> <p>This paper explores the role of AI in medical science, analyzing key applications, recent advancements, and potential future developments. By overcoming existing limitations, AI can revolutionize modern healthcare, paving the way for a more efficient, accurate, and personalized medical landscape.</p> |
| title | Curing with Code: The Intersection of AI, ML, and Medical Science |
| topic | AI-driven machine learning (ML) Artificial Intelligence natural language processing (NLP) |
| url | https://doi.org/10.5281/zenodo.17430980 |