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. 2024 Dec 13;10(12):322.
doi: 10.3390/jimaging10120322.

MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging

Sibtain Syed  1 , Rehan Ahmed  1 , Arshad Iqbal  2 , Naveed Ahmad  3 , Mohammed Ali Alshara  3
Affiliations

MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging

Sibtain Syed et al. J Imaging. .

Abstract

With technological advancements, remarkable progress has been made with the convergence of health sciences and Artificial Intelligence (AI). Modern health systems are proposed to ease patient diagnostics. However, the challenge is to provide AI-based precautions to patients and doctors for more accurate risk assessment. The proposed healthcare system aims to integrate patients, doctors, laboratories, pharmacies, and administrative personnel use cases and their primary functions onto a single platform. The proposed framework can also process microscopic images, CT scans, X-rays, and MRI to classify malignancy and give doctors a set of AI precautions for patient risk assessment. The proposed framework incorporates various DCNN models for identifying different forms of tumors and fractures in the human body i.e., brain, bones, lungs, kidneys, and skin, and generating precautions with the help of the Fined-Tuned Large Language Model (LLM) i.e., Generative Pretrained Transformer 4 (GPT-4). With enough training data, DCNN can learn highly representative, data-driven, hierarchical image features. The GPT-4 model is selected for generating precautions due to its explanation, reasoning, memory, and accuracy on prior medical assessments and research studies. Classification models are evaluated by classification report (i.e., Recall, Precision, F1 Score, Support, Accuracy, and Macro and Weighted Average) and confusion matrix and have shown robust performance compared to the conventional schemes.

Keywords: Large Language Model (LLM); convolutional neural networks; disease recognition; healthcare application; image processing; malignancy classification.

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Conflict of interest statement

The authors have affirmed no conflicts of interest in this research study.

Figures

Figure 1
Figure 1
Graphical scheme of the system architecture.
Figure 2
Figure 2
Graphical scheme of use cases in the proposed framework.
Figure 3
Figure 3
Graphical illustration of proposed AI bones fracture detection model.
Figure 4
Figure 4
Graphical illustration of the proposed AI lung cancer detection model.
Figure 5
Figure 5
Graphical illustration of the proposed AI brain tumor detection model.
Figure 6
Figure 6
Graphical illustration of the proposed AI skin cancer detection model.
Figure 7
Figure 7
Graphical illustration of the proposed AI kidney malignancy detection model.
Figure 8
Figure 8
Graphical illustration of the proposed GPT-4 model system integration.
Figure 9
Figure 9
Graphical illustration of the confusion matrix for Bone Fracture recognition; Lung Tumor recognition; Brain Tumor detection; Skin Lesion identification; and Renal Malignancy recognition AI model.
Figure 10
Figure 10
Graphical illustration of accuracy graph for Bone Fracture recognition; Lung Tumor recognition; Brain Tumor detection; Skin Lesion identification; and Renal Malignancy recognition AI model.
See this image and copyright information in PMC

References

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