Efficacy Evaluation in the Era of Precision Medicine: The Scope for AI
Patient stratification and the use of real-world evidence in regulatory decision-making are two key areas where algorithms are having an impact on drug development. The two are linked: increased patient stratification makes it harder to recruit patients into randomized-controlled trials, increasing...
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2020
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/71925 |
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| _version_ | 1879164728696111104 |
|---|---|
| author | Magirr, Dominic |
| author_browse | Magirr, Dominic |
| author_facet | Magirr, Dominic |
| author_sort | Magirr, Dominic |
| building | IntechOpen |
| collection | Acceso abierto |
| description | Patient stratification and the use of real-world evidence in regulatory decision-making are two key areas where algorithms are having an impact on drug development. The two are linked: increased patient stratification makes it harder to recruit patients into randomized-controlled trials, increasing the pressure on drug developers to find alternative sources of evidence for showing efficacy. In addition to real-world evidence, we are also seeing the emergence of more efficient ‘master protocol trials’, where multiple targeted agents can be evaluated simultaneously. In this chapter, I will review these developments and investigate the limitations for AI in terms of demonstrating the efficacy of novel targeted agents. |
| doi_str_mv | 10.5772/intechopen.90455 |
| first_indexed | 2026-10-15T21:38:57Z |
| format | Book Chapter |
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<dc:title>Efficacy Evaluation in the Era of Precision Medicine: The Scope for AI</dc:title>
<dc:creator>Dominic Magirr</dc:creator>
<dc:subject>Physical Sciences, Engineering and Technology</dc:subject>
<dc:description>Patient stratification and the use of real-world evidence in regulatory decision-making are two key areas where algorithms are having an impact on drug development. The two are linked: increased patient stratification makes it harder to recruit patients into randomized-controlled trials, increasing the pressure on drug developers to find alternative sources of evidence for showing efficacy. In addition to real-world evidence, we are also seeing the emergence of more efficient ‘master protocol trials’, where multiple targeted agents can be evaluated simultaneously. In this chapter, I will review these developments and investigate the limitations for AI in terms of demonstrating the efficacy of novel targeted agents.</dc:description>
<dc:publisher>IntechOpen</dc:publisher>
<dc:date>2020-09-09</dc:date>
<dc:type>Chapter, Part Of Book</dc:type>
<dc:identifier>https://www.intechopen.com/chapters/71925</dc:identifier>
<dc:identifier>doi:10.5772/intechopen.90455</dc:identifier>
<dc:language>en</dc:language>
<dc:relation>ISBN:978-1-78984-689-8</dc:relation>
<dc:rights>https://creativecommons.org/licenses/by/3.0/</dc:rights>
<dc:source>https://www.intechopen.com/books/10107 ; Artificial Intelligence in Oncology Drug Discovery and Development</dc:source>
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| id | intech-71925 |
| institution | IntechOpen |
| isbn | 978-1-78984-689-8 |
| language | English |
| last_indexed | 2026-10-15T21:38:57Z |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | oai_dc |
| spelling | intech-719252020-09-09 Efficacy Evaluation in the Era of Precision Medicine: The Scope for AI Dominic Magirr Physical Sciences, Engineering and Technology Patient stratification and the use of real-world evidence in regulatory decision-making are two key areas where algorithms are having an impact on drug development. The two are linked: increased patient stratification makes it harder to recruit patients into randomized-controlled trials, increasing the pressure on drug developers to find alternative sources of evidence for showing efficacy. In addition to real-world evidence, we are also seeing the emergence of more efficient ‘master protocol trials’, where multiple targeted agents can be evaluated simultaneously. In this chapter, I will review these developments and investigate the limitations for AI in terms of demonstrating the efficacy of novel targeted agents. IntechOpen 2020-09-09 Chapter, Part Of Book https://www.intechopen.com/chapters/71925 doi:10.5772/intechopen.90455 en ISBN:978-1-78984-689-8 https://creativecommons.org/licenses/by/3.0/ https://www.intechopen.com/books/10107 ; Artificial Intelligence in Oncology Drug Discovery and Development |
| spellingShingle | Physical Sciences, Engineering and Technology Magirr, Dominic Efficacy Evaluation in the Era of Precision Medicine: The Scope for AI |
| title | Efficacy Evaluation in the Era of Precision Medicine: The Scope for AI |
| title_full | Efficacy Evaluation in the Era of Precision Medicine: The Scope for AI |
| title_fullStr | Efficacy Evaluation in the Era of Precision Medicine: The Scope for AI |
| title_full_unstemmed | Efficacy Evaluation in the Era of Precision Medicine: The Scope for AI |
| title_short | Efficacy Evaluation in the Era of Precision Medicine: The Scope for AI |
| title_sort | efficacy evaluation in the era of precision medicine the scope for ai |
| topic | Physical Sciences, Engineering and Technology |
| url | https://www.intechopen.com/chapters/71925 |
| work_keys_str_mv | AT magirrdominic efficacyevaluationintheeraofprecisionmedicinethescopeforai |