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AI systems can verify medication adherence and discontinue conflicting medications, enhancing care. Patients familiar with AI may adapt and trust similar technologies in healthcare.
Fremont, CA: The increasing reliance on informal information sources, particularly the internet, in healthcare, has led to a surge in generative artificial intelligence (AI) adoption. AI systems create accessible content, making it more user-friendly. However, it is not a universal solution and must be deployed discerningly to avoid adverse outcomes. Generative AI performs best in environments with high repetition and low risk, allowing healthcare providers and patients to gradually understand its capabilities and establish trust in its utility.
Potential Health Functions for Generative AI
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With this context, we can evaluate the suitability of generative AI within various healthcare activities.
Routine Information Gathering
Generative AI can improve healthcare by enhancing information collection, reporting, and data analysis. It can engage patients in understandable language, resolve uncertainties, and summarize data for providers. AI systems can verify medication adherence and discontinue conflicting medications, enhancing care. Patients familiar with AI may adapt and trust similar technologies in healthcare.
Diagnosis
AI can improve diagnostic procedures, especially for conditions with extensive data. However, accurate diagnoses and bias mitigation remain challenges, especially for less common diseases. Healthcare providers should exercise caution in deploying AI until they can train it on extensive medical datasets. Designing workflows where AI supports physicians in the diagnostic process is crucial to mitigate risks.
Treatment
AI's potential in diagnostics faces challenges like accountability, liability, patient trust issues, and technological limitations. Healthcare providers are responsible for their treatments, and AI's ability to replicate complex tasks beyond simple medication management is unlikely. Additionally, AI's strengths in high-repetition, low-risk tasks make integration into medical treatment processes unlikely in the near future.
Post-Treatment Monitoring and Follow-Up
AI deployment in healthcare is promising for ensuring patient adherence to post-treatment advice and monitoring behavior outside clinical settings. Wearable technology, smart devices, and sensors offer real-time monitoring, personalized recommendations, and interventions, enabling medical providers to proactively address health deterioration and provide immediate medical attention.
Population Health Management
For predictive analytics, medical providers can improve patient population management using extensive datasets from EHRs and HIEs. AI can identify at-risk patients, assess hospital readmission likelihood, and create personalized care plans. However, the effectiveness depends on diverse datasets, including wearable technologies and smart devices.
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