


01. Data Integrity & Accessibility
Fragmented, inconsistent, or incomplete healthcare data currently hinders effective AI model training
02. Seamless Integration & Standardisation
Lack of standardised data formats and protocols complicates integration with existing systems and data exchange
03. Strategic Investment & Infrastructure
Significant upfront costs for hardware, software, and integration, alongside limitations in existing IT infrastructure, are real concerns
04. Robust Data Privacy & Security
Crucial need for robust measures to protect sensitive patient information from breaches
05. Regulatory Clarity & Ethical Guidelines
Absence of specific AI in healthcare legislation creates ambiguity regarding accountability and ethical use
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