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Related Concept Videos

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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Related Experiment Video

Updated: Mar 14, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
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Strategic Governance of Artificial Intelligence-Enabled Clinical Algorithm Development: Formative Evaluation of the

Sang Hyun Ahn1,2,3, Junhewk Kim4

  • 1MoDoc AI Inc., Needham, MA, United States.

JMIR Formative Research
|March 12, 2026
PubMed
Summary

The Semiautomatic Clinical Algorithm Development (S-ACAD) framework balances AI speed with expert oversight for safe clinical content. This human-in-the-loop approach shows promise for efficient, safe pediatric emergency guidance development.

Keywords:
AI-enabled uncertaintyclinical algorithm developmentdecision-making frameworksdigital health managementhealth care leadershiphuman-AI collaborationhuman-in-the-looplearning health systemsorganizational implementationstrategic governance

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Healthcare leaders grapple with balancing AI-driven content development speed against traditional methods' safety.
  • AI automation offers efficiency but risks clinical content inaccuracies (hallucinations).
  • Robust governance is crucial for integrating AI in healthcare while ensuring patient safety.

Purpose of the Study:

  • To describe the development and evaluate the Semiautomatic Clinical Algorithm Development (S-ACAD) framework.
  • To assess the feasibility of a human-in-the-loop model for pediatric emergency guidance.
  • To balance operational efficiency with rigorous safety standards in AI-assisted clinical content creation.

Main Methods:

  • A prospective, single-day proof-of-concept case study on pediatric febrile seizures.
  • A physician expert utilized a 4-phase workflow: AI data collection, AI synthesis, AI sparring, and clinical validation.
  • Benchmarking against a Fully Autonomous Clinical Algorithm Development (F-ACAD) system.

Main Results:

  • The S-ACAD framework produced a parent-actionable algorithm in ~245 minutes, rated highly for clinical validity by independent specialists.
  • 19 human expert interventions were recorded, primarily for clinical judgment and safety review.
  • The F-ACAD system completed in ~68 minutes but flagged 17 issues, including clarity and standard-of-care concerns.

Conclusions:

  • The S-ACAD framework demonstrates potential for active governance in AI-assisted clinical content development.
  • This human-in-the-loop model may reduce turnaround times while maintaining safety safeguards.
  • Further validation across diverse experts, topics, and settings is required for generalizability.