Data Security and Privacy for
Clinical Research Organizations

Learn how to secure your clinical data in AI-driven research and traditional clinical trials with resources from privacy experts.

At Private AI, we help companies harness the power of sensitive patient data for trials and research, while maintaining stringent privacy standards and regulatory compliance.

Download our guide on balancing data security with innovation, watch on demand webinars about advanced de-identification of PII and safeguarding patient privacy, and learn how Private AI can help turn secure your sensitive data without sacrificing your dataset.

Webinar: Watch it on Demand

Learn how advanced de-identification techniques can protect patient privacy while unlocking AI’s full potential in healthcare innovation.

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Instant Download: RWE Guide

Unlocking the Power of Real-World Evidence (RWE)

Unlock the full potential of real-world evidence in clinical research while ensuring data privacy and compliance.

Webinar: Instant Access

Discover strategies for safeguarding protected health information (PHI) while ensuring the utility and relevance of healthcare data

Discover Private AI:

Featured: 2024 Whitepaper

Learn more about Private AI’s use cases and methodology across complex, real-world applications and compliance, and see a detailed comparison with key competitors.

Read also…

The Evolving Landscape of Health Data Protection Laws in the United States

Read more

WHO’s AI Ethics and Governance Guidance for Large Multi-Modal Models operating in the Health Sector – Data Protection Considerations

Read more

Safeguarding health data used in Machine Learning

Benefits of AI in Healthcare and Data Sources

Read more

Healthcare Data Breach

The Costs of a Data Breach in the Healthcare Sector and its Privacy Compliance Implications

Read more

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Language Packs

Expand the categories below to see which languages are included within each language pack.
Note: English capabilities are automatically included within the Enterprise pricing tier. 

French
Spanish
Portuguese

Arabic
Hebrew
Persian (Farsi)
Swahili

French
German
Italian
Portuguese
Russian
Spanish
Ukrainian
Belarusian
Bulgarian
Catalan
Croatian
Czech
Danish
Dutch
Estonian
Finnish
Greek
Hungarian
Icelandic
Latvian
Lithuanian
Luxembourgish
Polish
Romanian
Slovak
Slovenian
Swedish
Turkish

Hindi
Korean
Tagalog
Bengali
Burmese
Indonesian
Khmer
Japanese
Malay
Moldovan
Norwegian (Bokmål)
Punjabi
Tamil
Thai
Vietnamese
Mandarin (simplified)

Arabic
Belarusian
Bengali
Bulgarian
Burmese
Catalan
Croatian
Czech
Danish
Dutch
Estonian
Finnish
French
German
Greek
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Italian
Japanese
Khmer
Korean
Latvian
Lithuanian
Luxembourgish
Malay
Mandarin (simplified)
Moldovan
Norwegian (Bokmål)
Persian (Farsi)
Polish
Portuguese
Punjabi
Romanian
Russian
Slovak
Slovenian
Spanish
Swahili
Swedish
Tagalog
Tamil
Thai
Turkish
Ukrainian
Vietnamese

Rappel

Testé sur un ensemble de données composé de données conversationnelles désordonnées contenant des informations de santé sensibles. Téléchargez notre livre blanc pour plus de détails, ainsi que nos performances en termes d’exactitude et de score F1, ou contactez-nous pour obtenir une copie du code d’évaluation.

99.5%+ Accuracy

Number quoted is the number of PII words missed as a fraction of total number of words. Computed on a 268 thousand word internal test dataset, comprising data from over 50 different sources, including web scrapes, emails and ASR transcripts.

Please contact us for a copy of the code used to compute these metrics, try it yourself here, or download our whitepaper.