Deep Learning for Personalized Medicine - Enhancing Precision Health With AI
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Keywords:
Deep Learning, Personalized Medicine, Precision Health, Artificial Intelligence, Healthcare, Genomics, Imaging, Clinical Records, Tailored Treatment, DiagnosisAbstract
Personalized medicine, a paradigm that tailors medical treatment to individual characteristics, is revolutionizing healthcare. One of the key enablers of this revolution is deep learning, a subset of artificial intelligence (AI) that excels at extracting patterns from complex data. This paper explores the role of deep learning in personalized medicine, focusing on its contributions to enhancing precision health initiatives through tailored treatment and diagnosis. We discuss how deep learning algorithms analyze diverse datasets, including genomics, imaging, and clinical records, to generate insights that guide personalized interventions. We also examine the challenges and ethical considerations associated with implementing deep learning in personalized medicine. Through this investigation, we highlight the transformative potential of deep learning in advancing precision health and improving patient outcomes.
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License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.