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As the Rush Toward AI in Healthcare Continues, Explainability is Crucial
Artificial intelligence is seeing a massive amount of interest in healthcare, with scores of hospitals and health systems already have deployed the technology – more often than not on the administrative side – to great success.But success with AI in the healthcare setting – especially on the clinical side – can't happen without addressing the growing concerns around models' transparency and explainability. In a field where decisions can mean life or death, being able to understand and trust AI decisions isn't just a technical need – it's an ethical must.Neeraj Mainkar is vice president of software engineering and advanced technology at Proprio, which develops immersive tools for surgeons. He has considerable expertise in applying algorithms in healthcare. Healthcare IT News spoke with him to discuss explainability, and the need for patient safety and trust, error identification, regulatory compliance and ethical standards in AI.
Medigy Insights
Responsible AI means operating within ethical boundaries. The push for advanced explainability in AI enhances trust and reliability, ensuring AI decisions are transparent, justifiable and ultimately beneficial to patient care. Ethical standards guide the responsible disclosure of information, protecting user privacy, upholding regulatory requirements like HIPAA and encouraging public trust in AI systems.As AI continues to evolve, the emphasis on explainability will continue to be a critical aspect of regulatory frameworks, ensuring these advanced technologies are used responsibly and effectively in healthcare.
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