ARTIFICIAL INTELLIGENCE IN HEALTHCARE: DIAGNOSTIC SUPPORT AND ADMINISTRATIVE AUTOMATION

Julia Żak; Monika  Stępińska; Marta Omiecińska; Alicja  Maciejewska; Maja Kaczor; Weronika Trynkiewicz; Zuzanna Rybka; Emilia Lenkiewicz; Karolina Dąbrowska; Jakub Winiarczyk

doi:10.31435/ijitss.2(50).2026.5250

Authors

Julia Żak Czerniakowski Hospital, Warsaw, Poland https://orcid.org/0009-0008-3383-8025
Monika Stępińska University Clinical Center of the Medical University of Warsaw, Warsaw, Poland https://orcid.org/0009-0008-0347-2704
Marta Omiecińska Międzyleski Specialist Hospital of Warsaw, Warsaw, Poland https://orcid.org/0009-0002-3134-8141
Alicja Maciejewska Independent Public Health Care Complex in Pruszków, Pruszków, Poland https://orcid.org/0009-0009-6903-2434
Maja Kaczor National Medical Institute of the Ministry of the Interior and Administration, Warsaw, Poland https://orcid.org/0009-0001-8218-5429
Weronika Trynkiewicz Ludwik Rydygier Specialist Hospital, Kraków, Poland https://orcid.org/0009-0008-2267-7166
Zuzanna Rybka University Clinical Center, Medical University of Warsaw, Warsaw, Poland https://orcid.org/0009-0001-4056-8800
Emilia Lenkiewicz National Medical Institute of the Ministry of the Interior and Administration, Warsaw, Poland https://orcid.org/0009-0001-4822-8157
Karolina Dąbrowska Wojewódzki Szpital w Łomży, Łomża, Poland https://orcid.org/0009-0002-9053-2002
Jakub Winiarczyk Independent Public Health Care Complex in Pruszków, Pruszków, Poland https://orcid.org/0009-0008-8163-5580

DOI:

https://doi.org/10.31435/ijitss.2(50).2026.5250

Keywords:

Artificial Intelligence, Clinical Workflow, Generative AI, Machine Learning, Physician Burnout, Explainable AI (XAI), Implementation Science

Abstract

Background: Artificial intelligence (AI) is rapidly moving from theoretical research into daily hospital operations. While AI promises to improve diagnostic speed and reduce administrative burnout, its real-world success depends on how well it integrates into complex clinical workflows. This review evaluates the practical impact of both discriminative and generative AI on modern healthcare delivery.

Methods: We conducted a targeted literature search using PubMed and Semantic Scholar (via Consensus AI) for research published between January 2018 and March 2026. We structured this work as a critical narrative review to focus specifically on the practical challenges and successes of AI in real-world hospital settings. After screening for clinical relevance and the presence of deployment data, we selected 21 peer-reviewed articles for final inclusion.

Results: The evidence shows that discriminative AI significantly improves speed in acute care, particularly in sepsis forecasting and stroke triage. Newer models are also showing success in evaluating surgical risks from ECGs and tracking traumatic brain injuries. However, these tools often face challenges like "alert fatigue" and data bias. On the administrative side, generative AI—such as ambient clinical scribes—is successfully reducing documentation time and improving billing accuracy. We found that the success of these tools depends on "Human-in-the-Loop" models and mathematical explainability frameworks, like SHAP values, which help doctors understand and trust AI decisions.

Conclusion: AI is becoming a necessary requirement for modern medicine, not just an optional upgrade. By offloading high-volume data tasks to machines, AI can help return the physician’s focus to direct patient care. Future success depends on using protocols that ensure algorithmic transparency and prevent automation bias, ensuring the doctor remains the final authority in clinical decisions.

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