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This research aims to identify and analyze the reasons for failed implementations of machine learning in medical diagnosis within Ukrainian healthcare.

February 14, 20260 citationsOpen Access

Medical ML Failed Implementations: What Went Wrong

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OIOleh IvchenkoOdessa National Polytechnic University DGDmytro Grybeniuk

Key Points

This research aims to identify and analyze the reasons for failed implementations of machine learning in medical diagnosis within Ukrainian healthcare.
Review of failed machine learning projects in Ukrainian healthcare
Qualitative analysis of implementation barriers
Interviews with stakeholders involved in medical ML projects
Identified common barriers to implementation, including lack of training and infrastructure
Observed insufficient collaboration between IT specialists and healthcare professionals
Noted regulatory issues hindering adoption of machine learning technologies

Abstract

Part of the Medical ML Research Series: Machine Learning for Medical Diagnosis in Ukrainian Healthcare. Published on Stabilarity Research Hub.

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Medical ML Failed Implementations: What Went Wrong

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