The Missing Pieces in AI for Healthcare

Artificial Intelligence (AI) is revolutionizing healthcare, offering solutions for diagnostics, workflow automation, and decision support. At ViVE 2025, industry leaders focused on advancing AI maturity in healthcare, emphasizing ambient AI solutions, automation, and AI-driven administrative efficiencies (Forbes). However, while the discussion highlighted significant advancements, there are several critical aspects necessary for the responsible and effective deployment of AI in healthcare.

In this article, we explore the missing elements that must be addressed to ensure AI’s long-term success in improving patient care and clinical workflows.

The Gaps in Healthcare AI Implementation

1. Data Quality and Bias Mitigation

One of the most overlooked yet fundamental challenges in AI development is the quality of training data. Many AI models rely on historical medical records, which can contain biases based on demographic, socioeconomic, and geographic factors. If AI systems are trained on incomplete or unrepresentative datasets, they risk perpetuating health disparities rather than mitigating them.

What’s Needed:

• Standardized, diverse, and high-quality datasets to prevent algorithmic bias

• Greater transparency in AI training processes

• Continuous monitoring and auditing of AI models to detect biases over time

2. Interoperability and Integration Challenges

ViVE 2025 highlighted AI-driven efficiencies, but integrating these solutions into existing electronic health records (EHRs) and hospital workflows remains a significant hurdle. Many healthcare organizations struggle with fragmented data systems, making it difficult for AI to deliver seamless, real-time insights.

What’s Needed:

• Industry-wide interoperability standards

• AI models that can communicate across different healthcare IT infrastructures

• Plug-and-play AI solutions that reduce implementation complexity

3. Explainability and Trust in AI-Driven Decisions

Healthcare professionals are unlikely to trust AI recommendations if they cannot understand how they were generated. While ambient AI and automation were emphasized, more attention has to be given to the explainability of AI-driven clinical decisions. AI models must offer clear, evidence-based reasoning to gain the trust of providers and patients alike.

What’s Needed:

• Transparent AI models that provide clear citations and reasoning for recommendations (Read about Open Evidence)

• Improved user interfaces that allow clinicians to verify AI-generated insights

• Regulations requiring AI systems to be auditable and accountable

4. Ethical Considerations and Regulatory Oversight

The rapid adoption of AI in healthcare raises ethical concerns, particularly around data privacy, informed consent, and liability. While the industry is moving quickly to deploy AI solutions, the regulatory landscape has not kept pace, leaving many unanswered questions about accountability when AI-driven errors occur.

What’s Needed:

• Clearer guidelines on AI governance and accountability

• Ethical frameworks for AI development and deployment

• Stronger policies on patient data protection and consent

5. The Human-AI Collaboration Factor

AI should enhance, not replace, human expertise. Most of the discussions today are primarily centered on automation, but there was little mention of how AI can better support rather than supplant healthcare professionals. AI should be designed to work alongside clinicians, augmenting their capabilities rather than attempting to fully automate decision-making.

What’s Needed:

• AI tools that prioritize clinical collaboration over automation

• Training programs to help healthcare workers effectively use AI

• Feedback mechanisms to ensure AI evolves based on real-world clinical experiences

Conclusion: A More Holistic Approach to AI in Healthcare

Critical advancements in AI are being made almost daily, but sometimes the technology can outpace the hands on experience revealing gaps in the conversation. For AI to truly transform healthcare, stakeholders must prioritize data quality, interoperability, transparency, ethical considerations, and human-AI collaboration. Addressing these missing elements will be crucial in ensuring that AI solutions not only improve efficiency but also enhance patient outcomes in a safe, equitable, and trustworthy manner.

At Ascend Innovation Partners, we help startups and enterprises develop solutions that prioritize ethical, transparent, and clinically meaningful applications. If you're looking to navigate the AI healthcare landscape, let's connect.

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