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Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

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Related Experiment Video

Updated: Jun 20, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Evaluating Large Language Model (LLM) Performance on Established Breast Classification Systems.

Syed Ali Haider¹, Sophia M Pressman¹, Sahar Borna¹

¹Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA.

Diagnostics (Basel, Switzerland)

|July 27, 2024

Summary

This summary is machine-generated.

Gemini demonstrated superior accuracy (98%) over ChatGPT-4 (71%) in classifying complex breast conditions using medical AI. This advancement promises improved diagnostic support for plastic surgeons and better patient outcomes.

Keywords:

artificial intelligence breast breast ptosis capsular contracture ectopic breast tissue gender-affirming mastectomy gynecomastia large language models machine learning plastic surgery

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Area of Science:

Medical Artificial Intelligence
Plastic Surgery
Diagnostic Imaging

Background:

Advanced Large Language Models (LLMs) are emerging tools in medical diagnostics.
Accurate classification of breast conditions is crucial for effective plastic surgery treatment planning.
Existing LLMs require evaluation for their ability to interpret complex medical classification systems.

Purpose of the Study:

To assess the diagnostic classification accuracy of advanced LLMs (ChatGPT-4 and Gemini) for various breast conditions.
To compare the performance of Gemini and ChatGPT-4 across five established breast-related classification systems.
To determine the potential of LLMs in aiding plastic surgeons' diagnostic decision-making.

Main Methods:

Fifty clinical scenarios involving breast conditions were developed.
The classification accuracy of ChatGPT-4 and Gemini was evaluated against five specific breast classification systems.
LLM responses were scored (0-2) for correctness, and descriptive statistics were used for comparison.

Main Results:

Gemini achieved a 98% overall accuracy, significantly outperforming ChatGPT-4's 71% accuracy.
Both LLMs performed well on the Baker and UTSW classifications.
Gemini showed higher accuracy than ChatGPT-4 in Fischer, Kajava, and Regnault classifications.

Conclusions:

Gemini demonstrates superior performance in classifying complex breast conditions compared to ChatGPT-4.
LLMs show significant potential for enhancing diagnostic support in plastic surgery.
Further development and integration of LLMs could improve diagnostic accuracy and patient outcomes in plastic surgery.