Leveraging physiology and artificial intelligence to deliver advancements in health care
Artificial intelligence in health care has experienced remarkable innovation and progress in the last decade. Significant advancements can be attributed to the utilization of artificial intelligence to transform physiology data to advance health care. In this review, we explore how past work has sha...
Saved in:
| Published in: | Physiological reviews Vol. 103; no. 4; p. 2423 |
|---|---|
| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
01.10.2023
|
| Subjects: | |
| ISSN: | 1522-1210, 1522-1210 |
| Online Access: | Get more information |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Artificial intelligence in health care has experienced remarkable innovation and progress in the last decade. Significant advancements can be attributed to the utilization of artificial intelligence to transform physiology data to advance health care. In this review, we explore how past work has shaped the field and defined future challenges and directions. In particular, we focus on three areas of development. First, we give an overview of artificial intelligence, with special attention to the most relevant artificial intelligence models. We then detail how physiology data have been harnessed by artificial intelligence to advance the main areas of health care: automating existing health care tasks, increasing access to care, and augmenting health care capabilities. Finally, we discuss emerging concerns surrounding the use of individual physiology data and detail an increasingly important consideration for the field, namely the challenges of deploying artificial intelligence models to achieve meaningful clinical impact. |
|---|---|
| AbstractList | Artificial intelligence in health care has experienced remarkable innovation and progress in the last decade. Significant advancements can be attributed to the utilization of artificial intelligence to transform physiology data to advance health care. In this review, we explore how past work has shaped the field and defined future challenges and directions. In particular, we focus on three areas of development. First, we give an overview of artificial intelligence, with special attention to the most relevant artificial intelligence models. We then detail how physiology data have been harnessed by artificial intelligence to advance the main areas of health care: automating existing health care tasks, increasing access to care, and augmenting health care capabilities. Finally, we discuss emerging concerns surrounding the use of individual physiology data and detail an increasingly important consideration for the field, namely the challenges of deploying artificial intelligence models to achieve meaningful clinical impact. Artificial intelligence in health care has experienced remarkable innovation and progress in the last decade. Significant advancements can be attributed to the utilization of artificial intelligence to transform physiology data to advance health care. In this review, we explore how past work has shaped the field and defined future challenges and directions. In particular, we focus on three areas of development. First, we give an overview of artificial intelligence, with special attention to the most relevant artificial intelligence models. We then detail how physiology data have been harnessed by artificial intelligence to advance the main areas of health care: automating existing health care tasks, increasing access to care, and augmenting health care capabilities. Finally, we discuss emerging concerns surrounding the use of individual physiology data and detail an increasingly important consideration for the field, namely the challenges of deploying artificial intelligence models to achieve meaningful clinical impact.Artificial intelligence in health care has experienced remarkable innovation and progress in the last decade. Significant advancements can be attributed to the utilization of artificial intelligence to transform physiology data to advance health care. In this review, we explore how past work has shaped the field and defined future challenges and directions. In particular, we focus on three areas of development. First, we give an overview of artificial intelligence, with special attention to the most relevant artificial intelligence models. We then detail how physiology data have been harnessed by artificial intelligence to advance the main areas of health care: automating existing health care tasks, increasing access to care, and augmenting health care capabilities. Finally, we discuss emerging concerns surrounding the use of individual physiology data and detail an increasingly important consideration for the field, namely the challenges of deploying artificial intelligence models to achieve meaningful clinical impact. |
| Author | Wu, Zhenqin Wu, Eric Wu, Matthew Zou, James Wu, Joseph C Snyder, Michael P Zhang, Angela |
| Author_xml | – sequence: 1 givenname: Angela surname: Zhang fullname: Zhang, Angela organization: Greenstone Biosciences, Palo Alto, California, United States – sequence: 2 givenname: Zhenqin surname: Wu fullname: Wu, Zhenqin organization: Department of Chemistry, Stanford University, Stanford, California, United States – sequence: 3 givenname: Eric orcidid: 0000-0001-8379-3373 surname: Wu fullname: Wu, Eric organization: Department of Electrical Engineering, Stanford University, Stanford, California, United States – sequence: 4 givenname: Matthew surname: Wu fullname: Wu, Matthew organization: Greenstone Biosciences, Palo Alto, California, United States – sequence: 5 givenname: Michael P orcidid: 0000-0003-0784-7987 surname: Snyder fullname: Snyder, Michael P organization: Department of Genetics, School of Medicine, Stanford University, Stanford, California, United States – sequence: 6 givenname: James surname: Zou fullname: Zou, James organization: Department of Computer Science, Stanford University, Stanford, California, United States – sequence: 7 givenname: Joseph C orcidid: 0000-0002-6068-8041 surname: Wu fullname: Wu, Joseph C organization: Department of Radiology, School of Medicine, Stanford University, Stanford, California, United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37104717$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkEtLAzEUhYNU7EP_gAvJ0s3UPCaT6VKKLyi4UXA3ZJI700gmU5O00H9vxAqu7uWc7x4Od44mfvSA0DUlS0oFu9ttjzHAYUkI4XzJCGNnaJYNVlBGyeTfPkXzGD8zJ0QlLtCUS0pKSeUMfWzgAEH11vf4J8-ObuyPWHmDVUi2s9oqh61P4JztwWvAacQGnM1nWJmDytIAPsUM4S0ol7ZYqwCX6LxTLsLVaS7Q--PD2_q52Lw-vazvN4UuOU1FW-pOr2RZ1xUBkKzUXNdGr1TdEQrE6M4YLtpSmqptBTO6hIozQTMtKgOCLdDtb-4ujF97iKkZbNS5rfIw7mPDaiJXjFScZPTmhO7bAUyzC3ZQ4dj8fYN9AwpXZmw |
| CitedBy_id | crossref_primary_10_1038_s41598_023_36798_7 crossref_primary_10_3389_fdgth_2024_1459640 crossref_primary_10_3389_fonc_2023_1289050 crossref_primary_10_1097_DCC_0000000000000695 crossref_primary_10_1152_physiol_00048_2024 crossref_primary_10_7759_cureus_76867 crossref_primary_10_1371_journal_pone_0300786 crossref_primary_10_1007_s10742_025_00351_y crossref_primary_10_1152_ajpheart_00766_2023 crossref_primary_10_3390_jpm14040354 crossref_primary_10_7717_peerj_cs_2916 crossref_primary_10_1161_CIR_0000000000001213 |
| ContentType | Journal Article |
| DBID | CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1152/physrev.00033.2022 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Medicine Anatomy & Physiology |
| EISSN | 1522-1210 |
| ExternalDocumentID | 37104717 |
| Genre | Research Support, U.S. Gov't, Non-P.H.S Review Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NHLBI NIH HHS grantid: R01 HL163680 – fundername: NHLBI NIH HHS grantid: F30 HL156478 – fundername: NIMH NIH HHS grantid: U01 MH098953 – fundername: NIA NIH HHS grantid: P30 AG059307 – fundername: NHLBI NIH HHS grantid: R01 HL146690 – fundername: NHLBI NIH HHS grantid: R01 HL126527 – fundername: NHLBI NIH HHS grantid: R01 HL130020 |
| GroupedDBID | --- -DZ -~X .55 123 18M 2WC 4.4 53G 5VS 79B 85S AAFWJ AAYJJ ABCQX ABHWK ABJNI ABKWE ABOCM ABPPZ ACGFO ACGFS ACGOD ACNCT ACPRK ADBBV ADFNX ADIYS AENEX AETEA AGHSJ ALMA_UNASSIGNED_HOLDINGS BAWUL BKKCC BTFSW CGR CS3 CUY CVF DIK DU5 E3Z EBS ECM EIF EMOBN F5P GX1 H13 H~9 IAO IH2 IHR IOF ITBOX KQ8 L7B N9A NHB NPM OK1 P2P PQQKQ RAP RHI RPL RPRKH RWL RXW TAE TN5 TR2 W8F WH7 WOQ X7M XSW YBH YNT YSK ZCA 7X8 |
| ID | FETCH-LOGICAL-c431t-b4cfc9748860ee724c3c8dc9a8f01e0dcfdd35b47d6bb52dc4e6325160e56de52 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 14 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001034269500002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1522-1210 |
| IngestDate | Fri Jul 11 11:54:46 EDT 2025 Thu Apr 03 07:09:43 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | physiology medicine artificial intelligence health care |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c431t-b4cfc9748860ee724c3c8dc9a8f01e0dcfdd35b47d6bb52dc4e6325160e56de52 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 |
| ORCID | 0000-0003-0784-7987 0000-0002-6068-8041 0000-0001-8379-3373 |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/10390055 |
| PMID | 37104717 |
| PQID | 2807920630 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2807920630 pubmed_primary_37104717 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-10-01 |
| PublicationDateYYYYMMDD | 2023-10-01 |
| PublicationDate_xml | – month: 10 year: 2023 text: 2023-10-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Physiological reviews |
| PublicationTitleAlternate | Physiol Rev |
| PublicationYear | 2023 |
| SSID | ssj0005565 |
| Score | 2.559479 |
| SecondaryResourceType | review_article |
| Snippet | Artificial intelligence in health care has experienced remarkable innovation and progress in the last decade. Significant advancements can be attributed to the... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 2423 |
| SubjectTerms | Artificial Intelligence Delivery of Health Care Humans |
| Title | Leveraging physiology and artificial intelligence to deliver advancements in health care |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/37104717 https://www.proquest.com/docview/2807920630 |
| Volume | 103 |
| WOSCitedRecordID | wos001034269500002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEB7Uinjx0fqoL1YQb6Gbx2Y3Jyli8WBLDwq5hWZ3AwVNqq2C_96ZTWq9CIKXnCYhbCY7j_3m-wCugiDRvgkTTxHvbDThxlMYJz2MzTrP_Vj7wjixCTkaqTRNxk3Dbd7AKpd7otuoTaWpR94j1pYkIIaom9mrR6pRdLraSGisQyvEVIYgXTJdsYUL4aQkMUSRgIfPl0MzIuhR34BEXqgmCLFMJPHc31JMF2oGu_99yT3YaZJM1q-9Yh_WbNmGTr_EAvvlk10zB_t0_fQ2bA2b0_UOpA8WHdvJFrHZtwmblIaRg9VcE2z6g8STLSpm7DNhO1iDJnAjc2jE6glLRtCyA3ga3D3e3nuN8oKnMaFYeHmkC42VhlIxt1YGkQ61MjqZqIL7lhtdGBOKPJImznMRGB3ZOMRMCa1FbKwIDmGjrEp7DEwJWdBDQ99ismaSpJBc5hgEI2O5jXkXLpdLmaFn03HFpLTV-zxbLWYXjurvkc1qCo4slEQx4cuTP9x9CtukEV8j8M6gVeB_bc9hU38spvO3C-cyeB2Nh1-Fo8wU |
| linkProvider | ProQuest |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Leveraging+physiology+and+artificial+intelligence+to+deliver+advancements+in+health+care&rft.jtitle=Physiological+reviews&rft.au=Zhang%2C+Angela&rft.au=Wu%2C+Zhenqin&rft.au=Wu%2C+Eric&rft.au=Wu%2C+Matthew&rft.date=2023-10-01&rft.issn=1522-1210&rft.eissn=1522-1210&rft.volume=103&rft.issue=4&rft.spage=2423&rft_id=info:doi/10.1152%2Fphysrev.00033.2022&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1522-1210&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1522-1210&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1522-1210&client=summon |