Clinical Frameworks and Meaningful Human Oversight of AI

PREPRINT — Not peer reviewed. Version 2.1. An artificial intelligence (AI) system can give a correct answer while missing something important about the patient. Whether physicians working with AI outperform AI alone is therefore a different question from whether human oversight can protect patients. This article asks whether staff can check AI work when AI has selected the evidence they see. It preserves three propositions: frameworks should guide attention without limiting it; accuracy and attentional safety are different; and findings outside the current explanation must be able to change care. Nursing assessment, hands-on care, and the patient–clinician relationship belong in this discussion. Four practical requirements organize the discussion: access to information beyond the AI answer, the skills and opportunity to recognize a problem, a clear route to action, and enough time and support. Institutions should assess whether teams find important omissions and respond appropriately, while also measuring errors introduced by human intervention, delays, unnecessary tests, and workload. Recording an opinion before AI advice appears does not establish independence if AI already selected the evidence. Changes in version 2.1: Preserves the three framework propositions and broad clinical audience; distinguishes the timing of AI advice from AI selection of the evidence available to clinicians; adds brief discussions of Henderson and Erturk and acknowledges related oversight work by van de Sande and colleagues; strengthens the clinical prose and evaluation agenda. Version 2.0 is available at https://doi.org/10.5281/zenodo.22780659. Related work: This article extends the author's related unreviewed preprint, Artificial intelligence-induced attentional displacement in clinical diagnosis (https://doi.org/10.5281/zenodo.22727468). That manuscript focuses on AI-mediated selection and presentation of information. The present article addresses clinical frameworks generally and the conditions under which assigned human oversight can function. AI assistance: OpenAI ChatGPT and Codex assisted with source retrieval, organization of the author’s prior material, drafting and revision of prose, reference checking, and document preparation. Codex assisted with this revision on September 17, 2026. The central propositions and earlier argument originated with the author. No empirical results were generated. The author is responsible for reviewing the final text and its sources before dissemination. All clinical scenarios are hypothetical.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-17
DOI
https://doi.org/10.5281/zenodo.22818434
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
preprint
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preprint

Clinical Frameworks and Meaningful Human Oversight of AI

Stanley Clark Newhall
Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare and Education
preprint

Clinical Frameworks and Meaningful Human Oversight of AI

Stanley Clark Newhall
preprint en

Abstract

PREPRINT — Not peer reviewed. Version 2.1. An artificial intelligence (AI) system can give a correct answer while missing something important about the patient. Whether physicians working with AI outperform AI alone is therefore a different question from whether human oversight can protect patients. This article asks whether staff can check AI work when AI has selected the evidence they see. It preserves three propositions: frameworks should guide attention without limiting it; accuracy and attentional safety are different; and findings outside the current explanation must be able to change care. Nursing assessment, hands-on care, and the patient–clinician relationship belong in this discussion. Four practical requirements organize the discussion: access to information beyond the AI answer, the skills and opportunity to recognize a problem, a clear route to action, and enough time and support. Institutions should assess whether teams find important omissions and respond appropriately, while also measuring errors introduced by human intervention, delays, unnecessary tests, and workload. Recording an opinion before AI advice appears does not establish independence if AI already selected the evidence. Changes in version 2.1: Preserves the three framework propositions and broad clinical audience; distinguishes the timing of AI advice from AI selection of the evidence available to clinicians; adds brief discussions of Henderson and Erturk and acknowledges related oversight work by van de Sande and colleagues; strengthens the clinical prose and evaluation agenda. Version 2.0 is available at https://doi.org/10.5281/zenodo.22780659. Related work: This article extends the author's related unreviewed preprint, Artificial intelligence-induced attentional displacement in clinical diagnosis (https://doi.org/10.5281/zenodo.22727468). That manuscript focuses on AI-mediated selection and presentation of information. The present article addresses clinical frameworks generally and the conditions under which assigned human oversight can function. AI assistance: OpenAI ChatGPT and Codex assisted with source retrieval, organization of the author’s prior material, drafting and revision of prose, reference checking, and document preparation. Codex assisted with this revision on September 17, 2026. The central propositions and earlier argument originated with the author. No empirical results were generated. The author is responsible for reviewing the final text and its sources before dissemination. All clinical scenarios are hypothetical.

Zenodo (CERN European Organization for Nuclear Research)
Oldham Council (GB)
Quality Education
Artificial Intelligence in Healthcare and Education
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