Deep Neural Networks at the Heart of Hospital Care
No child should suffer twice. In medication safety, prevention is the most powerful prescription. To stop adverse drug reactions (ADRs) before they start, we must break the stagnation in EHR‑based AI adoption and accelerate predictive modeling where it matters most.
Why EHR‑Based AI Lags Behind
Deep learning has transformed computer vision and natural language processing — and it’s already reshaping medical imaging and electronic health record (EHR) systems.
The regulatory scoreboard tells the story:
- 13 deep neural network (DNN)–based Software as a Medical Device (SaMD) products have FDA or CE‑Mark approval for medical imaging.
- Only 4 have achieved the same for EHR‑based applications.
Why the gap? Medical imaging AI enjoys clear advantages:
- Well‑structured pixel data
- Established validation protocols
- Defined regulatory pathways
- Focused clinical use cases across radiology subspecialties
EHR‑based AI faces a tougher climb:
- Heterogeneous data (structured codes, labs, free text)
- Higher interpretability and bias concerns
- Complex integration into clinical workflows
As a result, approved EHR‑based DNNs are rare — and mostly confined to niches like ICU deterioration prediction or digital triage.
ADR Prediction: An Even Bigger Gap
When it comes to using DNNs for adverse drug reaction prediction, the field is even more limited.
A recent survey of published inpatient ADR prediction studies screened 4,775 papers. After exclusions, only 13 studies remained — and just 3 used deep neural networks.
Of those:
- One targeted drug‑induced long‑QT syndrome
- One focused on acute kidney injury
- One compared CNN and RNN methods for multiple life‑threatening conditions (e.g., cardiac arrest, sepsis)
The warning signs were clear:
- 8 of 13 studies had a high risk of biased validation
- The remaining 5 lacked enough information to assess bias
And the worse — none included pediatric patients under 18 years old.
This exclusion perpetuates a daily inequity in healthcare — children remain underrepresented in the very innovations meant to protect them.
Our Solution: Synoptic Intelligence for Pediatric Medication Safety
We’re introducing Synoptic Intelligence — a state‑of‑the‑art deep learning system designed to break the bottleneck in EHR‑based AI and safeguard children from harmful, unintended consequences of medical treatment.
Our ADR Prediction engine is designed to account for the distinct pharmacokinetic, immunological, and developmental characteristics of children, and to integrate seamlessly into daily workflows at pediatric units.
We’re now collecting real‑world data for large‑scale validation and will share progress in the coming months.
Stay tuned — or join us — to accelerate this equitable innovation.
