<?xml version="1.0" encoding="utf-8"?>
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  <doc>
    <id>2952</id>
    <completedYear>2025</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue/>
    <volume>2025</volume>
    <type>article</type>
    <publisherName>AccScience Publishing (ASP) DESIGN+</publisherName>
    <publisherPlace>Singapur</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Balancing innovation and trust: Assessing artificial intelligence’s role in medical history taking and physician perspectives on patient care</title>
    <abstract language="eng">This study explores the potential of artificial intelligence (AI) in medical history taking (anamnesis) and assesses its acceptance using technology acceptance models. Through nine expert interviews with physicians from diverse medical backgrounds, the study aims to understand concerns and anticipated benefits of AI in the doctor–patient relationship. To demonstrate AI’s applications, digital anamnesis surveys were conducted with two actual patients, and the resulting data were interpreted by AI and reviewed by physicians. Findings indicate that physicians view AI as potentially beneficial, expecting that AI can facilitate improvements in care quality, efficiency, and time savings. Despite initial concerns about AI’s ability to address individual patient needs and its impact on the doctor–patient relationship, there is significant interest in integrating AI tools into daily practice. Key issues include patient constitution, the effort-to-benefit ratio, and potential risks to patient trust. The study identifies six areas for further research: Economic impact and cost-benefit analysis, patient acceptance and trust, stress reduction and job satisfaction, effects on doctor–patient relationships, development of verification mechanisms, and ethical and legal considerations. These findings underscore the complexities of AI integration in health care, emphasizing the need to address concerns about patient individuality, data privacy, and interpersonal relationships while harnessing AI’s potential.</abstract>
    <parentTitle language="eng">DESGIN+</parentTitle>
    <identifier type="doi">10.36922/dp.7675</identifier>
    <identifier type="urn">urn:nbn:de:bvb:861-opus4-29528</identifier>
    <enrichment key="PeerReviewed">Ja</enrichment>
    <enrichment key="RS_Correlation">Ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Felix Höpfl</author>
    <author>Marina Schellhorn</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AI-enhanced anamnesis systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AI technology acceptance</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AI quality assumptions</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Idana AI in medical history taking</value>
    </subject>
    <collection role="ddc" number="610">Medizin und Gesundheit</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Gesundheits- und Sozialwissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-rosenheim/files/2952/manuscript_dp04649.pdf</file>
  </doc>
</export-example>
