Laying the foundations for artificial intelligence in health:
Artificial intelligence (AI) has the potential to make health care more effective, efficient and equitable. AI applications are on the rise, from clinical decision-making and public health, to biomedical research and drug development, to health system administration and service redesign. The COVID-1...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Paris
OECD Publishing
2021
|
| Schriftenreihe: | OECD Health Working Papers
|
| Schlagworte: | |
| Online-Zugang: | kostenfrei |
| Zusammenfassung: | Artificial intelligence (AI) has the potential to make health care more effective, efficient and equitable. AI applications are on the rise, from clinical decision-making and public health, to biomedical research and drug development, to health system administration and service redesign. The COVID-19 pandemic is serving as a catalyst, yet it is also a reality check, highlighting the limits of existing AI systems. Most AI in health is actually artificial narrow intelligence, designed to accomplish very specific tasks on previously curated data from single settings. In the real world, health data are not always available, standardised, or easily shared. Limited data hinders the ability of AI tools to generate accurate information for diverse populations with potentially very complex conditions. Having appropriate patient data is critical for AI tools because decisions based on models with skewed or incomplete data can put patients at risk. Policy makers should beware of the hype surrounding AI and identify and focus on real problems and opportunities that AI can help address. In setting the foundations for AI to help achieve health policy objectives, one key priority is to improve data quality, interoperability and access in a secure way through better data governance. More broadly, policy makers should work towards implementing and operationalising the OECD AI Principles, as well as investing in technology and human capital. Strong policy frameworks based on inclusive and extensive dialogue among all stakeholders are also key to ensure AI adds value to patients and to societies. AI that influences clinical and public health decisions should be introduced with care. Ultimately, high expectations must be managed, but real opportunities should be pursued |
| Beschreibung: | 1 Online-Ressource (33 Seiten) |
| DOI: | 10.1787/3f62817d-en |
Internformat
MARC
| LEADER | 00000nam a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV047928824 | ||
| 003 | DE-604 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 220413s2021 xx o|||| 00||| eng d | ||
| 024 | 7 | |a 10.1787/3f62817d-en |2 doi | |
| 035 | |a (ZDB-13-SOC)068280327 | ||
| 035 | |a (OCoLC)1312699781 | ||
| 035 | |a (DE-599)BVBBV047928824 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-384 |a DE-91 |a DE-473 |a DE-824 |a DE-29 |a DE-739 |a DE-355 |a DE-20 |a DE-1028 |a DE-1049 |a DE-188 |a DE-521 |a DE-861 |a DE-898 |a DE-92 |a DE-573 |a DE-19 | ||
| 100 | 1 | |a Oliveira Hashiguchi, Tiago Cravo |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Laying the foundations for artificial intelligence in health |c Tiago Cravo Oliveira Hashiguchi, Luke Slawomirski and Jillian Oderkirk |
| 264 | 1 | |a Paris |b OECD Publishing |c 2021 | |
| 300 | |a 1 Online-Ressource (33 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a OECD Health Working Papers | |
| 520 | |a Artificial intelligence (AI) has the potential to make health care more effective, efficient and equitable. AI applications are on the rise, from clinical decision-making and public health, to biomedical research and drug development, to health system administration and service redesign. The COVID-19 pandemic is serving as a catalyst, yet it is also a reality check, highlighting the limits of existing AI systems. Most AI in health is actually artificial narrow intelligence, designed to accomplish very specific tasks on previously curated data from single settings. In the real world, health data are not always available, standardised, or easily shared. Limited data hinders the ability of AI tools to generate accurate information for diverse populations with potentially very complex conditions. Having appropriate patient data is critical for AI tools because decisions based on models with skewed or incomplete data can put patients at risk. Policy makers should beware of the hype surrounding AI and identify and focus on real problems and opportunities that AI can help address. In setting the foundations for AI to help achieve health policy objectives, one key priority is to improve data quality, interoperability and access in a secure way through better data governance. More broadly, policy makers should work towards implementing and operationalising the OECD AI Principles, as well as investing in technology and human capital. Strong policy frameworks based on inclusive and extensive dialogue among all stakeholders are also key to ensure AI adds value to patients and to societies. AI that influences clinical and public health decisions should be introduced with care. Ultimately, high expectations must be managed, but real opportunities should be pursued | ||
| 650 | 4 | |a Social Issues/Migration/Health | |
| 650 | 4 | |a Science and Technology | |
| 700 | 1 | |a Slawomirski, Luke |4 ctb | |
| 700 | 1 | |a Oderkirk, Jillian |4 ctb | |
| 856 | 4 | 0 | |u https://doi.org/10.1787/3f62817d-en |x Verlag |z kostenfrei |3 Volltext |
| 912 | |a ZDB-13-SOC | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-033310318 | |
Datensatz im Suchindex
| _version_ | 1818805972186955776 |
|---|---|
| adam_text | |
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Oliveira Hashiguchi, Tiago Cravo |
| author2 | Slawomirski, Luke Oderkirk, Jillian |
| author2_role | ctb ctb |
| author2_variant | l s ls j o jo |
| author_facet | Oliveira Hashiguchi, Tiago Cravo Slawomirski, Luke Oderkirk, Jillian |
| author_role | aut |
| author_sort | Oliveira Hashiguchi, Tiago Cravo |
| author_variant | h t c o htc htco |
| building | Verbundindex |
| bvnumber | BV047928824 |
| collection | ZDB-13-SOC |
| ctrlnum | (ZDB-13-SOC)068280327 (OCoLC)1312699781 (DE-599)BVBBV047928824 |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| doi_str_mv | 10.1787/3f62817d-en |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000zc 4500</leader><controlfield tag="001">BV047928824</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">220413s2021 xx o|||| 00||| eng d</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1787/3f62817d-en</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-13-SOC)068280327</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1312699781</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047928824</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-384</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-824</subfield><subfield code="a">DE-29</subfield><subfield code="a">DE-739</subfield><subfield code="a">DE-355</subfield><subfield code="a">DE-20</subfield><subfield code="a">DE-1028</subfield><subfield code="a">DE-1049</subfield><subfield code="a">DE-188</subfield><subfield code="a">DE-521</subfield><subfield code="a">DE-861</subfield><subfield code="a">DE-898</subfield><subfield code="a">DE-92</subfield><subfield code="a">DE-573</subfield><subfield code="a">DE-19</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Oliveira Hashiguchi, Tiago Cravo</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Laying the foundations for artificial intelligence in health</subfield><subfield code="c">Tiago Cravo Oliveira Hashiguchi, Luke Slawomirski and Jillian Oderkirk</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Paris</subfield><subfield code="b">OECD Publishing</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (33 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">OECD Health Working Papers</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Artificial intelligence (AI) has the potential to make health care more effective, efficient and equitable. AI applications are on the rise, from clinical decision-making and public health, to biomedical research and drug development, to health system administration and service redesign. The COVID-19 pandemic is serving as a catalyst, yet it is also a reality check, highlighting the limits of existing AI systems. Most AI in health is actually artificial narrow intelligence, designed to accomplish very specific tasks on previously curated data from single settings. In the real world, health data are not always available, standardised, or easily shared. Limited data hinders the ability of AI tools to generate accurate information for diverse populations with potentially very complex conditions. Having appropriate patient data is critical for AI tools because decisions based on models with skewed or incomplete data can put patients at risk. Policy makers should beware of the hype surrounding AI and identify and focus on real problems and opportunities that AI can help address. In setting the foundations for AI to help achieve health policy objectives, one key priority is to improve data quality, interoperability and access in a secure way through better data governance. More broadly, policy makers should work towards implementing and operationalising the OECD AI Principles, as well as investing in technology and human capital. Strong policy frameworks based on inclusive and extensive dialogue among all stakeholders are also key to ensure AI adds value to patients and to societies. AI that influences clinical and public health decisions should be introduced with care. Ultimately, high expectations must be managed, but real opportunities should be pursued</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social Issues/Migration/Health</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Science and Technology</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Slawomirski, Luke</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Oderkirk, Jillian</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1787/3f62817d-en</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-13-SOC</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033310318</subfield></datafield></record></collection> |
| id | DE-604.BV047928824 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:34:54Z |
| indexdate | 2024-12-18T19:02:21Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033310318 |
| oclc_num | 1312699781 |
| open_access_boolean | 1 |
| owner | DE-384 DE-91 DE-BY-TUM DE-473 DE-BY-UBG DE-824 DE-29 DE-739 DE-355 DE-BY-UBR DE-20 DE-1028 DE-1049 DE-188 DE-521 DE-861 DE-898 DE-BY-UBR DE-92 DE-573 DE-19 DE-BY-UBM |
| owner_facet | DE-384 DE-91 DE-BY-TUM DE-473 DE-BY-UBG DE-824 DE-29 DE-739 DE-355 DE-BY-UBR DE-20 DE-1028 DE-1049 DE-188 DE-521 DE-861 DE-898 DE-BY-UBR DE-92 DE-573 DE-19 DE-BY-UBM |
| physical | 1 Online-Ressource (33 Seiten) |
| psigel | ZDB-13-SOC |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | OECD Publishing |
| record_format | marc |
| series2 | OECD Health Working Papers |
| spelling | Oliveira Hashiguchi, Tiago Cravo Verfasser aut Laying the foundations for artificial intelligence in health Tiago Cravo Oliveira Hashiguchi, Luke Slawomirski and Jillian Oderkirk Paris OECD Publishing 2021 1 Online-Ressource (33 Seiten) txt rdacontent c rdamedia cr rdacarrier OECD Health Working Papers Artificial intelligence (AI) has the potential to make health care more effective, efficient and equitable. AI applications are on the rise, from clinical decision-making and public health, to biomedical research and drug development, to health system administration and service redesign. The COVID-19 pandemic is serving as a catalyst, yet it is also a reality check, highlighting the limits of existing AI systems. Most AI in health is actually artificial narrow intelligence, designed to accomplish very specific tasks on previously curated data from single settings. In the real world, health data are not always available, standardised, or easily shared. Limited data hinders the ability of AI tools to generate accurate information for diverse populations with potentially very complex conditions. Having appropriate patient data is critical for AI tools because decisions based on models with skewed or incomplete data can put patients at risk. Policy makers should beware of the hype surrounding AI and identify and focus on real problems and opportunities that AI can help address. In setting the foundations for AI to help achieve health policy objectives, one key priority is to improve data quality, interoperability and access in a secure way through better data governance. More broadly, policy makers should work towards implementing and operationalising the OECD AI Principles, as well as investing in technology and human capital. Strong policy frameworks based on inclusive and extensive dialogue among all stakeholders are also key to ensure AI adds value to patients and to societies. AI that influences clinical and public health decisions should be introduced with care. Ultimately, high expectations must be managed, but real opportunities should be pursued Social Issues/Migration/Health Science and Technology Slawomirski, Luke ctb Oderkirk, Jillian ctb https://doi.org/10.1787/3f62817d-en Verlag kostenfrei Volltext |
| spellingShingle | Oliveira Hashiguchi, Tiago Cravo Laying the foundations for artificial intelligence in health Social Issues/Migration/Health Science and Technology |
| title | Laying the foundations for artificial intelligence in health |
| title_auth | Laying the foundations for artificial intelligence in health |
| title_exact_search | Laying the foundations for artificial intelligence in health |
| title_exact_search_txtP | Laying the foundations for artificial intelligence in health |
| title_full | Laying the foundations for artificial intelligence in health Tiago Cravo Oliveira Hashiguchi, Luke Slawomirski and Jillian Oderkirk |
| title_fullStr | Laying the foundations for artificial intelligence in health Tiago Cravo Oliveira Hashiguchi, Luke Slawomirski and Jillian Oderkirk |
| title_full_unstemmed | Laying the foundations for artificial intelligence in health Tiago Cravo Oliveira Hashiguchi, Luke Slawomirski and Jillian Oderkirk |
| title_short | Laying the foundations for artificial intelligence in health |
| title_sort | laying the foundations for artificial intelligence in health |
| topic | Social Issues/Migration/Health Science and Technology |
| topic_facet | Social Issues/Migration/Health Science and Technology |
| url | https://doi.org/10.1787/3f62817d-en |
| work_keys_str_mv | AT oliveirahashiguchitiagocravo layingthefoundationsforartificialintelligenceinhealth AT slawomirskiluke layingthefoundationsforartificialintelligenceinhealth AT oderkirkjillian layingthefoundationsforartificialintelligenceinhealth |