New possibilities for medical support systems utilizing artificial intelligence (AI) and data platforms.

Area of Science:

• Health Informatics
• Artificial Intelligence in Medicine
• Medical Data Management

Background:

• Japan is developing centralized platforms for medical, health, and caregiving data.
• Advancements in general-purpose AI models (e.g., Segment Anything, Chat GPT) show potential for healthcare applications.
• Current general-purpose AI models lack sufficient medical data training, limiting direct healthcare use.

Purpose of the Study:

• To explore the potential of centralized data platforms to overcome limitations of general-purpose AI in healthcare.
• To investigate the development of universally applicable AI models for medical imaging and AI assistants for clinical support.
• To assess the role of AI assistants in improving doctor-patient interactions and healthcare quality.

Main Methods:

• Leveraging advancements in artificial intelligence (AI), including general-purpose models.
• Utilizing centralized data platforms to aggregate and manage diverse medical and health data.
• Developing AI models trained on extensive, medically-relevant datasets.

Main Results:

• Centralized data platforms can enable the development of universally applicable AI models for medical data processing.
• AI assistants can be created to support doctors in symptom analysis and disease detection.
• AI can act as an intermediary, simplifying consultations and enhancing patient understanding.

Conclusions:

• Centralized data platforms are crucial for advancing AI in Japanese healthcare.
• AI assistants can function as 'sub-doctors,' improving diagnostic accuracy and efficiency.
• AI integration promises to reduce physician workload and enhance elderly care through early detection and prevention.