---
res:
  bibo_abstract:
  - 'Despite their great promise, artificial intelligence (AI) systems have yet to
    become ubiquitous in the daily practice of medicine largely due to several crucial
    unmet needs of healthcare practitioners. These include lack of explanations in
    clinically meaningful terms, handling the presence of unknown medical conditions,
    and transparency regarding the system’s limitations, both in terms of statistical
    performance as well as recognizing situations for which the system’s predictions
    are irrelevant. We articulate these unmet clinical needs as machine-learning (ML)
    problems and systematically address them with cutting-edge ML techniques. We focus
    on electrocardiogram (ECG) analysis as an example domain in which AI has great
    potential and tackle two challenging tasks: the detection of a heterogeneous mix
    of known and unknown arrhythmias from ECG and the identification of underlying
    cardio-pathology from segments annotated as normal sinus rhythm recorded in patients
    with an intermittent arrhythmia. We validate our methods by simulating a screening
    for arrhythmias in a large-scale population while adhering to statistical significance
    requirements. Specifically, our system 1) visualizes the relative importance of
    each part of an ECG segment for the final model decision; 2) upholds specified
    statistical constraints on its out-of-sample performance and provides uncertainty
    estimation for its predictions; 3) handles inputs containing unknown rhythm types;
    and 4) handles data from unseen patients while also flagging cases in which the
    model’s outputs are not usable for a specific patient. This work represents a
    significant step toward overcoming the limitations currently impeding the integration
    of AI into clinical practice in cardiology and medicine in general.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Yonatan
      foaf_name: Elul, Yonatan
      foaf_surname: Elul
  - foaf_Person:
      foaf_givenName: Aviv A.
      foaf_name: Rosenberg, Aviv A.
      foaf_surname: Rosenberg
  - foaf_Person:
      foaf_givenName: Assaf
      foaf_name: Schuster, Assaf
      foaf_surname: Schuster
  - foaf_Person:
      foaf_givenName: Alexander
      foaf_name: Bronstein, Alexander
      foaf_surname: Bronstein
      foaf_workInfoHomepage: http://www.librecat.org/personId=58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
    orcid: 0000-0001-9699-8730
  - foaf_Person:
      foaf_givenName: Yael
      foaf_name: Yaniv, Yael
      foaf_surname: Yaniv
  bibo_doi: 10.1073/pnas.2020620118
  bibo_issue: '24'
  bibo_volume: 118
  dct_date: 2021^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0027-8424
  - http://id.crossref.org/issn/1091-6490
  dct_language: eng
  dct_publisher: National Academy of Sciences@
  dct_title: Meeting the unmet needs of clinicians from AI systems showcased for cardiology
    with deep-learning–based ECG analysis@
  fabio_hasPubmedId: '34099565'
...