6 Challenges of AI Adoption in Healthcare

AI is transforming healthcare by refining diagnostics, customising treatment plans, and optimising administrative workflows. For example, advanced AI systems can now detect early signs of cognitive decline years before symptoms manifest by analysing sleep-related brain wave activity captured through EEG technology.
AI adoption in healthcare comes with notable difficulties. A study revealed that AI-driven mortality prediction models frequently miss around 66% of severe injuries that could prove fatal. Furthermore, ethical dilemmas arise regarding AI’s authority in critical medical decisions, especially in diagnosis and treatment. To fully utilise AI in healthcare, these issues must be resolved.
1. Data privacy and security
AI systems require extensive access to patient data, raising significant privacy and security concerns. Healthcare data breaches are among the most costly, averaging $7.13 million per incident in 2020. Maintaining patient trust and safeguarding sensitive data need adherence to laws like HIPAA and GDPR.
2. Accuracy and reliability
The efficacy …
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