Article Title: "Comparison Between Human Diagnosis and Artificial Intelligence Diagnosis in Radiological Images" by Assistant Lecturer Tabarak Amer
Introduction
Accurate diagnosis is considered the backbone of successful medical practice, especially in radiology, which relies on analyzing radiographic images such as X-rays, CT scans, and MRI. With the emergence of Artificial Intelligence (AI), it has become possible to compare the capabilities of radiologists with those of advanced algorithms in analyzing these images. This comparison does not aim to replace humans with machines, but rather to understand the limits of each and the potential for integration between them.
Human Diagnosis
Relies on clinical experience and the ability to link imaging results with medical history and clinical examinations.
Distinguished by the physician’s ability to make clinical judgments and manage atypical cases.
The strength of the physician lies in mental flexibility and decision-making in complex situations.
However, human diagnosis may be affected by fatigue, bias, or lack of experience, sometimes causing errors or delays in diagnosis.
AI-Based Diagnosis
Relies on deep learning algorithms trained on millions of radiographic images.
Characterized by high speed in analyzing images and detecting subtle patterns invisible to the human eye.
Demonstrates high accuracy, especially in diagnosing specific diseases such as:
Lung cancer in CT scans.
Brain hemorrhage in emergency CT scans.
Fine bone fractures.
However, it is limited in clinical interpretation; meaning it can detect abnormalities but cannot provide the final diagnosis in the full patient context.
Comparison
Accuracy (1):
AI has shown results comparable to—and in some cases exceeding—those of expert radiologists.
Physicians, however, have the advantage of integrating imaging results with additional information (tests, symptoms).
Speed (2):
AI excels with near-instant capability to read thousands of images.
Human diagnosis requires more time, especially under high patient load.
Errors (3):
Humans may err due to fatigue or distraction.
AI may err if it encounters images unlike those in its training data.
Flexibility (4):
Physicians are more capable of handling rare and atypical cases.
AI is limited by the quality and scope of the data it is trained on.
Integration Between the Two
The optimal solution is not to replace physicians but to combine AI with medical expertise. AI functions as a supportive tool, detecting fine indicators and reducing the likelihood of missed diagnoses, while physicians retain their central role in making the final medical decision.
Conclusion
The comparison between human diagnosis and AI diagnosis in radiological imaging reveals that each has its strengths and weaknesses. The future points toward integrating AI with the expertise of radiologists, ensuring more accurate and faster diagnoses and enhancing the quality of healthcare.
University of Al-Mustaqbal — The First University in Iraq