Try Carebot for free

Book a call

After you submit your inquiry, we will contact you as soon as possible to arrange a call at a convenient time. The entire process is non-binding. We want you to be confident that Carebot works exactly for your needs.

Try Carebot for free

Book a call

After you submit your inquiry, we will contact you as soon as possible to arrange a call at a convenient time. The entire process is non-binding. We want you to be confident that Carebot works exactly for your needs.

Try Carebot for free

Book a call

After you submit your inquiry, we will contact you as soon as possible to arrange a call at a convenient time. The entire process is non-binding. We want you to be confident that Carebot works exactly for your needs.

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How the introductory meeting works

Our application specialist will meet with you to demonstrate how the system works and what concrete benefits it brings to your practice. Based on your needs, they will then propose an optimal implementation for your hospital, covering both technical and legal aspects, with the aim of keeping the overall burden on your team to a minimum.

Where Carebot already helps

Our solution is currently used in hundreds of hospitals, from regional departments to university clinics. MDR Class IIa certification, the CE mark, and validated clinical studies confirm that the AI operates safely and reliably.

Already used by hundreds of institutions

Already used by hundreds of institutions

Already used by hundreds of institutions

0 million

images evaluated per year¹

+0 hour

for the radiologist²

up to 0%

accuracy of detection³

0 million

images evaluated per year¹

+0 hour

for the radiologist²

up to 0%

accuracy of detection³

0 million

images evaluated per year¹

+0 hour

for the radiologist²

up to 0%

accuracy of detection³

Why do doctors choose Carebot?

Detects more findings

It acts as a doctor's third eye and helps detect findings that could easily be overlooked.

Saves time of doctors

It automatically recognizes images without findings and clearly marks urgent cases, which contributes to more efficient handling of critical situations without burdening the team.

Reduces the risk of errors

It maintains consistent quality of care even during staffing shortages or when onboarding new colleagues.

No disruption to workflow

It is easy to implement into existing systems and does not disrupt established workflows. Doctors make decisions as before, only with more accurate and faster data.

Why do doctors choose Carebot?

Detects more findings

It acts as a doctor's third eye and helps detect findings that could easily be overlooked.

Saves time of doctors

It automatically recognizes images without findings and clearly marks urgent cases, which contributes to more efficient handling of critical situations without burdening the team.

Reduces the risk of errors

It maintains consistent quality of care even during staffing shortages or when onboarding new colleagues.

No disruption to workflow

It is easy to implement into existing systems and does not disrupt established workflows. Doctors make decisions as before, only with more accurate and faster data.

Why do doctors choose Carebot?

Detects more findings

It acts as a doctor's third eye and helps detect findings that could easily be overlooked.

Saves time of doctors

It automatically recognizes images without findings and clearly marks urgent cases, which contributes to more efficient handling of critical situations without burdening the team.

Reduces the risk of errors

It maintains consistent quality of care even during staffing shortages or when onboarding new colleagues.

No disruption to workflow

It is easy to implement into existing systems and does not disrupt established workflows. Doctors make decisions as before, only with more accurate and faster data.

¹ Internal data of Carebot s.r.o.

² Yacoub, B., et al. (2022). Impact of artificial intelligence assistance on chest CT interpretation times: a prospective randomized study. American Journal of Roentgenology.

³ Kvak, D., et al. (2023). Can deep learning reliably recognize abnormality patterns on chest X-rays? A multi-reader study examining one month of AI implementation in everyday radiology clinical practice. arXiv.