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Cited by 34Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareTechnology and Human Factors in Education and Health

Artificial intelligence-enhanced patient evaluation: bridging art and science

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Oikonomou, Evangelos K, Khera, Rohan·2024·European Heart Journal
DOI 10.1093/eurheartj/ehae415PMC PMC11400875
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Abstract

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic’s view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

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  • Gatekeeper use requires more than discrimination: reliable calibration, very high sensitivity and negative predictive value in the intended population, prospective pathway-level validation, decision-curve evidence at the selected operating threshold, and explicit mechanisms for clinician override and follow-up.

    “Gatekeeper use requires more than discrimination: reliable calibration, very high sensitivity and negative predictive value in the intended population, prospective pathway-level validation, decision-curve evidence at the selected operating threshold, and explicit mechanisms for clinician override and follow-up.36,39,40”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [39]

    Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • What remains unproven is whether a negative or low-risk AI-ECG output can safely support echocardiography deferral.

    “What remains unproven is not detection capability, but whether a negative or low-risk AI-ECG output can safely support echocardiography deferral.3,36,39,40”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [39]

    The ‘Artificial intelligence-guided electrocardiography’ section describes AI-ECG detection performance, and ‘Defining high-performance and high-value artificial intelligence strategies’ calls for prospective studies of real-world effectiveness. Neither passage addresses whether a negative or low-risk AI-ECG result can safely justify deferring echocardiography. Searches for deferral, negative results, and prospective validation did not locate that specific point. (LLM, conf=0.73)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Oikonomou, E. K., & Khera, R.
Affiliations
  • Yale University (US)
  • Yale New Haven Hospital (US)
Corresponding author
  • Oikonomou, Evangelos K
  • Khera, Rohan
ORCID
Venue
European Heart Journal
Publisher
Oxford University Press (OUP)
Published
2024-07-08
Volume
45
Issue
35
Pages
3204-3218
ISSN
  • 0195-668X (ISSN-L)
  • 1522-9645
DOI
10.1093/eurheartj/ehae415
PMC
PMC11400875
OpenAlex
W4400433606
Cited by
34
References
26
Citations by year
2026: 10 · 2025: 18 · 2024: 6
FWCI
0.78
Type
journal article
Language
en
Open access
Licence
academic.oup.com/pages/standard-publication-reuse-rights (vor)
Locations
  • European Heart Journal (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (1F32HL170592-01, R01HL167858, K23HL153775)
  • Doris Duke Charitable Foundation (2022060)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
Keywords
clinical implementation of AI, patient assessment, medical history, physical examination, wearable sensors, longitudinal patient monitoring, personalized medicine, clinical workflow integration, AI ethics and regulation, digital health
MeSH terms
Digital Health, Artificial Intelligence, Humans, Physical Examination, Precision Medicine

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