Three Takeaways- What Does it Take for AI-powered Clinical Decision Support Systems to Become Part of Everyday Clinical Practice?

On the 17th of June, the second Awareness Workshop was hosted by the technical partners KPMG Israel, MOR, RU, Yonalink, gathering healthcare professionals, researchers and academia to join the discussion on AI in healthcare. The discussion was surrounded by mainly three topics: Trust, Data, and Policy & Sustainability.

Building Trust Through Transparency

Technology alone is not enough to drive adoption. Participants agreed that trust is one of the most significant barriers to implementing AI-powered clinical decision support systems.

For clinicians to confidently rely on AI recommendations, they need to understand how the models are developed, what data they are trained on, and why they produce specific recommendations. Transparency and explainability are therefore essential, not only for healthcare professionals but also for patients, whose confidence in AI-supported care will play an important role in successful implementation.

Better Data for Better Decisions

The discussion also highlighted that the quality of AI depends on the quality of the data behind it.

Participants emphasised the importance of comprehensive, high-quality datasets that reflect the complexity of real patients. Beyond medical images and clinical records, AI systems should consider factors such as comorbidities, medications, psychosocial information, and digital pathology data. Clear standards for data collection and interpretation are equally important to ensure reliable, clinically relevant results.

Putting People at the Centre

Looking beyond the technology, participants agreed that long-term success requires strong governance and policies that support sustainable adoption.

A recurring message throughout the discussion was that patients should be active partners in the development of AI solutions, rather than simply end users. Involving patients from the earliest stages helps ensure that new technologies address real healthcare needs while building trust, inclusiveness, and long-term acceptance.

Conclusion

As AI continues to reshape healthcare, discussions like these demonstrate that successful innovation depends not only on technological excellence, but also on collaboration, transparency, and a shared commitment to developing solutions that truly benefit patients and healthcare professionals alike. Join the next Awareness Workshop on the 16th of September, more information here: https://www.ai4lungs.eu/post/save-the-date-join-the-ai4lungs-awareness-workshop-on-september-16-2026 

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