In your experience, what is the biggest barrier to AI adoption in healthcare?

Machine Learning (ML)Artificial Intelligence (AI)

Cost & ROI concerns25%

Integration with existing workflows (EHR, clinical processes)25%

Regulatory & compliance issues50%

Physician/staff resistance to AI tools25%

Lack of technical expertise for implementation25%

8 PARTICIPANTS

189 views2 Upvotes1 Comment

Sort by:

CTO in Media5 months ago

We use AI/ML and GenAI in our daily work and as core components in our actual product.

AI is still in its early stages, and hallucination issues in GenAI are still prevalent. I would personally be very hesitant to use any AI model in a health setting that has not been proven to be exceptionally good at the specific task.

Content you might like

Data Analytics-as-a-Service (DAaaS) is a cloud-based delivery model for data analytics & AI. What do you see as the most important enabler for you to adopt DAaaS for your business?

Ease of getting my data into the DAaaS platform11%

Tools that make it easy to create use cases with the DAaaS platform41%

A pre-existing library of dashboards and report templates to help me quickly get up-and-running31%

The ability to try out the DAaaS platform for free before buying7%

Services from the DAaaS vendor (consulting, support, training)4%

Confidence that my data is safe in the cloud3%

View Results

What "tells" have you noticed for AI-generated text (i.e., typical words/phrases, or other patterns indicative of GenAI)?

What’s the number-1 factor you consider when deciding whether a new tool is worth purchasing?

Budget allocation12%

Potential process improvements71%

Onboarding & training bandwidth7%

Security & compliance7%

Reviewing prior purchase overlap1%

View Results

Which pitfalls—model bias, false positives/negatives, data quality, regulatory constraints—often impede AI-based security tools, and how can they be mitigated in a financial-services context?