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Can we fully rely on AI when it comes to clinical decision-making? 🤖🩺

In this interview, Marina Chernik, PhD, Senior Healthcare IT & AI Consultant at ScienceSoft, discusses how healthcare organizations can reduce the risks of AI hallucinations by building strong verification frameworks and safety controls.

Learn what to look for when evaluating clinical AI systems before they go live and how to ensure that unreliable or fabricated outputs don’t reach clinicians.

Watch the full interview here 👇
https://www.youtube.com/watch?v=x6PH5PDMf3o

How to Fight Clinical AI Hallucinations | Interview with Marina Chernik Marina Chernik, PhD, Senior Healthcare IT & AI Consultant at Scienc...

ScienceSoft helped a leading contact center SaaS provider enhance its platform with AI-powered transcription, summarization, sentiment analysis, and contact reason detection: https://www.scnsoft.com/case-studies/multi-model-ai-pipeline-for-customer-analytics-in-a-contact-center-saas

📈 The insurance industry stays genuinely committed to AI scaling, with 99% of insurers already having GenAI initiatives underway, and 90% planning to raise AI investments in the coming years.

Yet, not every insurance task benefits equally from AI, and too many design approaches have failed to support scalable, controllable AI use in real workflows.

In ScienceSoft’s recent guide, we explore:
🔹 Key opportunities AI unlocks for insurance organizations
🔹 Latest technology developments and adoption trends
🔹 Practical AI applications across insurance functions
🔹 Realistic architectures for insurance AI solutions

🚀 Check it out to better plan your AI-first insurance transformation journey.

Read: https://www.scnsoft.com/insurance/artificial-intelligence

🩺 Patient identification errors can affect care quality, operational efficiency, and data reliability across healthcare organizations.

In ScienceSoft’s webinar, Tanya Filippova and Alex Bekker take a closer look at how US healthcare providers can improve patient matching and address fragmented patient data.

They discuss what typically causes inaccuracies in patient records, which technologies help improve data matching, and how AI and ML can support healthcare data workflows. The webinar also features a real-life example of a care provider that improved patient record accuracy from 85% to 98%.

🔗 Watch the webinar recording: https://www.youtube.com/watch?v=vBetivt0IvE

Solving the Patient Identification Problem | Up to 98% Patient Record Accuracy In this webinar, Tanya Filippova and Alex Bekker discuss how US healthcare providers can improve patient identification accuracy.They explore the causes of f...