The role of machine learning in clinical research: transforming the future of evidence generation

Abstract Background Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the curr...
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Autor*in:	E. Hope Weissler [verfasserIn] Tristan Naumann [verfasserIn] Tomas Andersson [verfasserIn] Rajesh Ranganath [verfasserIn] Olivier Elemento [verfasserIn] Yuan Luo [verfasserIn] Daniel F. Freitag [verfasserIn] James Benoit [verfasserIn] Michael C. Hughes [verfasserIn] Faisal Khan [verfasserIn] Paul Slater [verfasserIn] Khader Shameer [verfasserIn] Matthew Roe [verfasserIn] Emmette Hutchison [verfasserIn] Scott H. Kollins [verfasserIn] Uli Broedl [verfasserIn] Zhaoling Meng [verfasserIn] Jennifer L. Wong [verfasserIn] Lesley Curtis [verfasserIn] Erich Huang [verfasserIn] Marzyeh Ghassemi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Clinical trials as topic; Machine learning Artificial intelligence Research design Research ethics

Übergeordnetes Werk:	In: Trials - BMC, 2006, 22(2021), 1, Seite 15
Übergeordnetes Werk:	volume:22 ; year:2021 ; number:1 ; pages:15

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s13063-021-05489-x

Katalog-ID:	DOAJ071224432

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allfields	10.1186/s13063-021-05489-x doi (DE-627)DOAJ071224432 (DE-599)DOAJ3b9a18a768594d159cdbecc73cb339d5 DE-627 ger DE-627 rakwb eng R5-920 E. Hope Weissler verfasserin aut The role of machine learning in clinical research: transforming the future of evidence generation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum. Results Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas. Conclusions ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence. Clinical trials as topic; Machine learning Artificial intelligence Research design Research ethics Medicine (General) Tristan Naumann verfasserin aut Tomas Andersson verfasserin aut Rajesh Ranganath verfasserin aut Olivier Elemento verfasserin aut Yuan Luo verfasserin aut Daniel F. Freitag verfasserin aut James Benoit verfasserin aut Michael C. Hughes verfasserin aut Faisal Khan verfasserin aut Paul Slater verfasserin aut Khader Shameer verfasserin aut Matthew Roe verfasserin aut Emmette Hutchison verfasserin aut Scott H. Kollins verfasserin aut Uli Broedl verfasserin aut Zhaoling Meng verfasserin aut Jennifer L. Wong verfasserin aut Lesley Curtis verfasserin aut Erich Huang verfasserin aut Marzyeh Ghassemi verfasserin aut In Trials BMC, 2006 22(2021), 1, Seite 15 (DE-627)326173552 (DE-600)2040523-6 17456215 nnns volume:22 year:2021 number:1 pages:15 https://doi.org/10.1186/s13063-021-05489-x kostenfrei https://doaj.org/article/3b9a18a768594d159cdbecc73cb339d5 kostenfrei https://doi.org/10.1186/s13063-021-05489-x kostenfrei https://doaj.org/toc/1745-6215 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 1 15
spelling	10.1186/s13063-021-05489-x doi (DE-627)DOAJ071224432 (DE-599)DOAJ3b9a18a768594d159cdbecc73cb339d5 DE-627 ger DE-627 rakwb eng R5-920 E. Hope Weissler verfasserin aut The role of machine learning in clinical research: transforming the future of evidence generation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum. Results Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas. Conclusions ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence. Clinical trials as topic; Machine learning Artificial intelligence Research design Research ethics Medicine (General) Tristan Naumann verfasserin aut Tomas Andersson verfasserin aut Rajesh Ranganath verfasserin aut Olivier Elemento verfasserin aut Yuan Luo verfasserin aut Daniel F. Freitag verfasserin aut James Benoit verfasserin aut Michael C. Hughes verfasserin aut Faisal Khan verfasserin aut Paul Slater verfasserin aut Khader Shameer verfasserin aut Matthew Roe verfasserin aut Emmette Hutchison verfasserin aut Scott H. Kollins verfasserin aut Uli Broedl verfasserin aut Zhaoling Meng verfasserin aut Jennifer L. Wong verfasserin aut Lesley Curtis verfasserin aut Erich Huang verfasserin aut Marzyeh Ghassemi verfasserin aut In Trials BMC, 2006 22(2021), 1, Seite 15 (DE-627)326173552 (DE-600)2040523-6 17456215 nnns volume:22 year:2021 number:1 pages:15 https://doi.org/10.1186/s13063-021-05489-x kostenfrei https://doaj.org/article/3b9a18a768594d159cdbecc73cb339d5 kostenfrei https://doi.org/10.1186/s13063-021-05489-x kostenfrei https://doaj.org/toc/1745-6215 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 1 15
allfields_unstemmed	10.1186/s13063-021-05489-x doi (DE-627)DOAJ071224432 (DE-599)DOAJ3b9a18a768594d159cdbecc73cb339d5 DE-627 ger DE-627 rakwb eng R5-920 E. Hope Weissler verfasserin aut The role of machine learning in clinical research: transforming the future of evidence generation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum. Results Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas. Conclusions ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence. Clinical trials as topic; Machine learning Artificial intelligence Research design Research ethics Medicine (General) Tristan Naumann verfasserin aut Tomas Andersson verfasserin aut Rajesh Ranganath verfasserin aut Olivier Elemento verfasserin aut Yuan Luo verfasserin aut Daniel F. Freitag verfasserin aut James Benoit verfasserin aut Michael C. Hughes verfasserin aut Faisal Khan verfasserin aut Paul Slater verfasserin aut Khader Shameer verfasserin aut Matthew Roe verfasserin aut Emmette Hutchison verfasserin aut Scott H. Kollins verfasserin aut Uli Broedl verfasserin aut Zhaoling Meng verfasserin aut Jennifer L. Wong verfasserin aut Lesley Curtis verfasserin aut Erich Huang verfasserin aut Marzyeh Ghassemi verfasserin aut In Trials BMC, 2006 22(2021), 1, Seite 15 (DE-627)326173552 (DE-600)2040523-6 17456215 nnns volume:22 year:2021 number:1 pages:15 https://doi.org/10.1186/s13063-021-05489-x kostenfrei https://doaj.org/article/3b9a18a768594d159cdbecc73cb339d5 kostenfrei https://doi.org/10.1186/s13063-021-05489-x kostenfrei https://doaj.org/toc/1745-6215 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 1 15
allfieldsGer	10.1186/s13063-021-05489-x doi (DE-627)DOAJ071224432 (DE-599)DOAJ3b9a18a768594d159cdbecc73cb339d5 DE-627 ger DE-627 rakwb eng R5-920 E. Hope Weissler verfasserin aut The role of machine learning in clinical research: transforming the future of evidence generation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum. Results Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas. Conclusions ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence. Clinical trials as topic; Machine learning Artificial intelligence Research design Research ethics Medicine (General) Tristan Naumann verfasserin aut Tomas Andersson verfasserin aut Rajesh Ranganath verfasserin aut Olivier Elemento verfasserin aut Yuan Luo verfasserin aut Daniel F. Freitag verfasserin aut James Benoit verfasserin aut Michael C. Hughes verfasserin aut Faisal Khan verfasserin aut Paul Slater verfasserin aut Khader Shameer verfasserin aut Matthew Roe verfasserin aut Emmette Hutchison verfasserin aut Scott H. Kollins verfasserin aut Uli Broedl verfasserin aut Zhaoling Meng verfasserin aut Jennifer L. Wong verfasserin aut Lesley Curtis verfasserin aut Erich Huang verfasserin aut Marzyeh Ghassemi verfasserin aut In Trials BMC, 2006 22(2021), 1, Seite 15 (DE-627)326173552 (DE-600)2040523-6 17456215 nnns volume:22 year:2021 number:1 pages:15 https://doi.org/10.1186/s13063-021-05489-x kostenfrei https://doaj.org/article/3b9a18a768594d159cdbecc73cb339d5 kostenfrei https://doi.org/10.1186/s13063-021-05489-x kostenfrei https://doaj.org/toc/1745-6215 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 1 15
allfieldsSound	10.1186/s13063-021-05489-x doi (DE-627)DOAJ071224432 (DE-599)DOAJ3b9a18a768594d159cdbecc73cb339d5 DE-627 ger DE-627 rakwb eng R5-920 E. Hope Weissler verfasserin aut The role of machine learning in clinical research: transforming the future of evidence generation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum. Results Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas. Conclusions ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence. Clinical trials as topic; Machine learning Artificial intelligence Research design Research ethics Medicine (General) Tristan Naumann verfasserin aut Tomas Andersson verfasserin aut Rajesh Ranganath verfasserin aut Olivier Elemento verfasserin aut Yuan Luo verfasserin aut Daniel F. Freitag verfasserin aut James Benoit verfasserin aut Michael C. Hughes verfasserin aut Faisal Khan verfasserin aut Paul Slater verfasserin aut Khader Shameer verfasserin aut Matthew Roe verfasserin aut Emmette Hutchison verfasserin aut Scott H. Kollins verfasserin aut Uli Broedl verfasserin aut Zhaoling Meng verfasserin aut Jennifer L. Wong verfasserin aut Lesley Curtis verfasserin aut Erich Huang verfasserin aut Marzyeh Ghassemi verfasserin aut In Trials BMC, 2006 22(2021), 1, Seite 15 (DE-627)326173552 (DE-600)2040523-6 17456215 nnns volume:22 year:2021 number:1 pages:15 https://doi.org/10.1186/s13063-021-05489-x kostenfrei https://doaj.org/article/3b9a18a768594d159cdbecc73cb339d5 kostenfrei https://doi.org/10.1186/s13063-021-05489-x kostenfrei https://doaj.org/toc/1745-6215 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 1 15
language	English
source	In Trials 22(2021), 1, Seite 15 volume:22 year:2021 number:1 pages:15
sourceStr	In Trials 22(2021), 1, Seite 15 volume:22 year:2021 number:1 pages:15
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Clinical trials as topic; Machine learning Artificial intelligence Research design Research ethics Medicine (General)
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container_title	Trials
authorswithroles_txt_mv	E. Hope Weissler @@aut@@ Tristan Naumann @@aut@@ Tomas Andersson @@aut@@ Rajesh Ranganath @@aut@@ Olivier Elemento @@aut@@ Yuan Luo @@aut@@ Daniel F. Freitag @@aut@@ James Benoit @@aut@@ Michael C. Hughes @@aut@@ Faisal Khan @@aut@@ Paul Slater @@aut@@ Khader Shameer @@aut@@ Matthew Roe @@aut@@ Emmette Hutchison @@aut@@ Scott H. Kollins @@aut@@ Uli Broedl @@aut@@ Zhaoling Meng @@aut@@ Jennifer L. Wong @@aut@@ Lesley Curtis @@aut@@ Erich Huang @@aut@@ Marzyeh Ghassemi @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	326173552
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language_de	englisch
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callnumber-first	R - Medicine
author	E. Hope Weissler
spellingShingle	E. Hope Weissler misc R5-920 misc Clinical trials as topic; Machine learning misc Artificial intelligence misc Research design misc Research ethics misc Medicine (General) The role of machine learning in clinical research: transforming the future of evidence generation
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topic_title	R5-920 The role of machine learning in clinical research: transforming the future of evidence generation Clinical trials as topic; Machine learning Artificial intelligence Research design Research ethics
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title	The role of machine learning in clinical research: transforming the future of evidence generation
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author_browse	E. Hope Weissler Tristan Naumann Tomas Andersson Rajesh Ranganath Olivier Elemento Yuan Luo Daniel F. Freitag James Benoit Michael C. Hughes Faisal Khan Paul Slater Khader Shameer Matthew Roe Emmette Hutchison Scott H. Kollins Uli Broedl Zhaoling Meng Jennifer L. Wong Lesley Curtis Erich Huang Marzyeh Ghassemi
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title_sort	role of machine learning in clinical research: transforming the future of evidence generation
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title_auth	The role of machine learning in clinical research: transforming the future of evidence generation
abstract	Abstract Background Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum. Results Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas. Conclusions ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence.
abstractGer	Abstract Background Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum. Results Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas. Conclusions ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence.
abstract_unstemmed	Abstract Background Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum. Results Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas. Conclusions ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence.
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title_short	The role of machine learning in clinical research: transforming the future of evidence generation
url	https://doi.org/10.1186/s13063-021-05489-x https://doaj.org/article/3b9a18a768594d159cdbecc73cb339d5 https://doaj.org/toc/1745-6215
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author2	Tristan Naumann Tomas Andersson Rajesh Ranganath Olivier Elemento Yuan Luo Daniel F. Freitag James Benoit Michael C. Hughes Faisal Khan Paul Slater Khader Shameer Matthew Roe Emmette Hutchison Scott H. Kollins Uli Broedl Zhaoling Meng Jennifer L. Wong Lesley Curtis Erich Huang Marzyeh Ghassemi
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