October 12, 2025Open Access

GenAI Agents for Early Disease Diagnosis: A Review of Architectures, Applications, and Policy Directions

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SJSatyadhar Joshi

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Overview

Review highlights how AI agents improve diagnostic accuracy and operational efficiency in healthcare, suggesting significant advancements in medical imaging and multimodal data processing.

Key Points

GenAI agents enhance diagnostic accuracy, achieving improvements of 15-25% in AUC-ROC across various studies.
Utilizing advanced machine learning architectures, these AI systems integrate multimodal data for comprehensive disease diagnosis and treatment recommendations.
Challenges include ensuring data privacy compliance with regulations like HIPAA and GDPR, alongside the need for model interpretability using techniques such as SHAP and LIME.
Future research aims to develop federated learning approaches for secure model training, addressing regulatory hurdles while advancing medical superintelligence capabilities.

Cite This Study

Satyadhar Joshi (2025) studied this question.

synapsesocial.com/papers/68ebffcfdef9fcb308ff24c8 https://doi.org/10.20944/preprints202510.0547.v1

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October 12, 2025Open Access

GenAI Agents for Early Disease Diagnosis: A Review of Architectures, Applications, and Policy Directions

View Full Paper

Ask AI

Bookmark

Authors

SJSatyadhar Joshi

Discussion

Member takes

Overview

Review highlights how AI agents improve diagnostic accuracy and operational efficiency in healthcare, suggesting significant advancements in medical imaging and multimodal data processing.

Key Points

GenAI agents enhance diagnostic accuracy, achieving improvements of 15-25% in AUC-ROC across various studies.
Utilizing advanced machine learning architectures, these AI systems integrate multimodal data for comprehensive disease diagnosis and treatment recommendations.
Challenges include ensuring data privacy compliance with regulations like HIPAA and GDPR, alongside the need for model interpretability using techniques such as SHAP and LIME.
Future research aims to develop federated learning approaches for secure model training, addressing regulatory hurdles while advancing medical superintelligence capabilities.

Cite This Study

Satyadhar Joshi (2025) studied this question.

synapsesocial.com/papers/68ebffcfdef9fcb308ff24c8 https://doi.org/10.20944/preprints202510.0547.v1

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