A Survey on Advancing Medical Diagnostics and Healthcare with Generative AI: Techniques, Applications, and Future Prospects

Abstract

Generative artificial intelligence (AI) is reshaping modern healthcare by augmenting diagnostic accuracy, accelerating imaging workflows, and enabling data-driven clinical decisions. This paper surveys the recent evolution and clinical integration of generative models—such as GANs, diffusion architectures, and transformer-based systems—in medical diagnostics, with a focus on radiology. Across 219 peer-reviewed studies and 47 clinical implementations, these models demonstrate improvements in image clarity, artifact reduction, and rare disease detection, while reducing documentation burden and enhancing operational efficiency. The review also examines challenges including adversarial vulnerabilities, hallucination risks, and computational constraints, alongside policy recommendations for safe deployment. Quantitative analyses reveal that these tools can reduce diagnostic errors by up to 41% and contribute to projected cost savings of $362 billion annually by 2030. Emphasis is placed on ethical frameworks, federated data strategies, and emerging trends such as quantum generative modeling and neuro-symbolic systems, establishing generative AI as a pivotal enabler of precision medicine.

Introduction

Problem Overview:
Diagnostic errors cause more annual deaths than breast cancer or car accidents in the U.S., with over 5 million cases annually, largely due to medical imaging misinterpretation. These errors cost Medicare over $42 billion per year. Human limitations, data scarcity (especially for rare diseases), and systemic inefficiencies (e.g., long biopsy delays, clinician burnout) exacerbate the crisis.

Solution: Generative AI

Generative AI is transforming diagnostics through:

• Synthetic data generation (to address data scarcity)

• Multimodal integration (e.g., imaging + EHR)

• Adaptive learning (continuous improvement)

FDA-approved tools like NVIDIA Clara and Med-PaLM 2 are already in clinical use, enhancing diagnostic speed and accuracy.

Technical Foundations

• Federated Learning (FL): Enables secure, decentralized model training across hospitals (e.g., FLARE project achieved 94.2% accuracy in embolism detection from 6.3M images).

• Evaluation Metrics:

  • FID ≤ 9.0 for image realism

  • SSIM ≥ 0.92 for structure preservation

  • NRMSE ≤ 0.05 for dose accuracy

• Clinical Testing: Turing tests show radiologists often can’t distinguish AI-generated images; GANs increased rare disease detection by 18%.

Clinical Applications

A. Diagnostic Imaging

• Enhanced Resolution: Diffusion models improve CT/MRI while reducing radiation (e.g., 33% exposure reduction at Mass General).

• Artifact Removal: GANs and DDPMs reconstruct degraded scans, improving lesion detection and scan efficiency.

B. Disease Detection & Risk Stratification

• AI predicts disease progression and links imaging to genetic profiles (e.g., BRCA1).

• AI-generated molecules show 87% validation success in rare disease drug design.

C. Synthetic Data

• Used for training, validation, and trials.

• Boosts performance in underrepresented populations (e.g., skin cancer detection in Asian patients).

D. Drug Discovery

• Accelerates molecular design and drug repurposing (e.g., baricitinib identified early for COVID-19).

E. Operational Workflows

• Clinical Documentation: GPT-4 tools reduced documentation time by 29% at Mayo Clinic.

• Scheduling: AI optimized OR use, reducing downtime by 19%.

• Patient Communication: Multilingual AI-generated instructions improve understanding and adherence.

• Billing/Compliance: Real-time coding systems cut admin costs by 21%.

Implementation Challenges

• Security Risks: Vulnerable to adversarial attacks (e.g., minor image changes cause diagnostic errors).

  • Defenses: Randomized smoothing, blockchain verification, and Fourier filtering.

• Regulatory Gaps: Lack of global standards for generative AI in medicine.

• Ethical Concerns: Privacy, accountability, and model transparency remain under debate.

Conclusion

This review highlights how generative AI is transforming medical imaging and diagnostics through substantial gains in accuracy, efficiency, and clinical value. Across 219 studies and 47 clinical deployments, generative models improved disease detection by 17–41% and reduced false positives by over a third. Operational efficiency gains include reduced documentation time and better operating room utilization. Beyond diagnostics, generative chemistry is accelerating drug discovery pipelines, with Phase II candidates already in progress. However, deployment is not without barriers—hallucination risks, adversarial vulnerabilities, and regulatory inconsistencies across regions remain pressing issues. Despite these challenges, the economic outlook is strong, with projected annual savings of up to $362 billion by 2030, largely driven by error reduction and preventive care enhancements. To responsibly scale these technologies, the paper proposes a five-pronged policy framework emphasizing standard validation protocols, federated data infrastructures, clinician-AI training, ethical governance, and financial incentives. Recommended actions include establishing international standards on hallucination thresholds, implementing blockchain-backed data privacy, and supporting dynamic patient consent mechanisms. Additionally, investment in upskilling clinicians and incentivizing participation in AI trials are crucial for workforce transition. These initiatives must work in concert to foster adoption, ensure safety, and preserve equity across diverse healthcare environments. Critical research frontiers include developing longitudinal AI models for disease progression, improving multimodal reasoning across images, genomics, and clinical text, and optimizing large models for edge deployment using biocomputing and photonic processors. Ultimately, generative AI represents not just a technical tool, but a paradigm shift in medicine. Its successful integration requires cross-disciplinary collaboration, robust governance, and a shared commitment to inclusive innovation. With thoughtful stewardship, these technologies can drive a more intelligent, resilient, and patient-centered future in global healthcare.

References

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Copyright

Copyright © 2025 Ms. Yukta Pandit, Ms. Huda Siddiqui, Ms. Sweety Singh, Prof. Monali Rajput. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.