Tag: retrieval-augmented generation

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Events

Open Innovator Virtual Session: Responsible AI Integration in Healthcare

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Events

Open Innovator Virtual Session: Responsible AI Integration in Healthcare

The recent Open Innovator Virtual Session brought together healthcare technology leaders to address a critical question: How can artificial intelligence enhance patient care without compromising the human elements essential to healthcare? Moderated by Suzette Ferreira, the panel featured Michael Dabis, Dr. Chandana Samaranayake, Dr. Ang Yee, and Charles Barton, who collectively emphasized that AI in healthcare is not a plug-and-play solution but a carefully orchestrated process requiring trust, transparency, and unwavering commitment to patient safety.

The Core Message: AI as Support, Not Replacement

The speakers unanimously agreed that AI’s greatest value lies in augmenting human expertise rather than replacing it. In healthcare, where every decision carries profound consequences for human lives, technology must earn trust from both clinicians and patients. Unlike consumer applications where failures cause inconvenience, clinical AI mistakes can result in misdiagnosis, inappropriate treatment, or preventable harm.

Current Reality Check:

  • 63% of healthcare professionals are optimistic about AI
  • 48% of patients do NOT share this optimism – revealing a significant trust gap
  • The fundamental challenge remains unchanged: clinicians are overwhelmed with data and need it transformed into meaningful, actionable intelligence

The TACK Framework: Building Trust in AI Systems

Dr. Chandana Samaranayake introduced the TACK framework as essential for gaining clinician trust:

  • Transparency: Clinicians must understand what data AI uses and how it reaches conclusions. Black-box algorithms are fundamentally incompatible with clinical practice where providers bear legal and ethical responsibility.
  • Accountability: Clear lines of responsibility must be established for AI-assisted decisions, with frameworks for evaluating outcomes and addressing errors.
  • Confidence: AI systems must demonstrate consistent reliability through rigorous validation across diverse patient populations and clinical scenarios.
  • Control: Healthcare professionals must retain ultimate authority over clinical decisions, with the ability to override AI recommendations at any time.

Why AI Systems Fail: Real-World Lessons

The Workflow Integration Problem

Michael Dabis highlighted that the biggest misconception is treating AI as a simple product rather than a complex integration process. Several real-world failures illustrate this:

  • Sepsis prediction systems: Technically brilliant systems that nurses loved during trials but deactivated on night shifts because they required manual data entry, creating more work than they eliminated
  • Alert fatigue: Systems generating too many notifications that overwhelm clinicians and obscure genuinely important insights
  • Radiology AI errors: Speech recognition confusing “ilium” (pelvis bone) with “ileum” (small intestine), leading AI to generate convincing but dangerously wrong reports about intestinal metastasis instead of pelvic metastasis

The Consulting Disaster

Dr. Chandana shared a cautionary tale: A major consulting firm had to refund the Australian government after their AI-generated healthcare report cited publications that didn’t exist. In healthcare, such mistakes don’t just waste money—they can cost lives.

Four Critical Implementation Requirements

1. Workflow Integration

AI must fit INTO clinical workflows, not on top of them. This requires:

  • Co-designing with clinicians from day one
  • Observing how healthcare professionals actually work
  • Ensuring systems add value without creating additional burdens

2. Data Governance

Clean, traceable, validated data is non-negotiable:

  • Source transparency so clinicians know data age and origin
  • Interoperability for holistic patient views
  • Adherence to the principle: garbage in, garbage out

3. Continuous Feedback Loops

  • AI must learn from clinical overrides and corrections
  • Ongoing validation required (supported by FDA’s PCCP guidance)
  • Mechanisms for users to report issues and suggest improvements

4. Cross-Functional Alignment

  • Team agreement on requirements, risk management, and validation criteria
  • Intensive training during deployment, not just online courses
  • Change management principles applied throughout

Patient Safety and Ethical Considerations

Dr. Gary Ang emphasized accountability as going beyond responsibility—it means owning both the solution and the problem. Key concerns include:

Skill Degradation Risk: Over-reliance on AI may erode clinical abilities. Doctors using AI for endoscopy might lose the capacity to detect issues independently when systems fail.

Avoiding Echo Chambers: AI systems must help patients make informed decisions without manipulating behavior or validating delusions, unlike social media algorithms.

Patient-Centered Approach: The patient must always remain at the center, with AI protecting safety rather than prioritizing operational efficiency.

Future Directions: Holistic and Preventive Care

Charles Barton outlined a vision for AI that extends beyond reactive treatment:

The Current Problem: Healthcare data is siloed—no single clinician has end-to-end patient health information spanning sleep, nutrition, physical activity, mental health, and diagnostics.

The Opportunity: 25% of health problems, particularly musculoskeletal and cardiovascular issues affecting 25% of the world’s population, can be prevented through healthy lifestyle interventions supported by AI.

Future Applications:

  • Patient education about procedures, medications, and screening decisions
  • Daily health monitoring instead of reactive treatment
  • Predictive and prescriptive recommendations validated through continuous monitoring
  • Early identification of disease risk years before symptoms appear

Scaling Challenges and Geographic Considerations

Unlike traditional medical devices with predictable inputs and outputs, AI systems are undeterministic and require different scaling approaches:

  • Start with limited, low-risk use cases
  • Expand gradually with continuous validation
  • Recognize that demographics and healthcare issues vary by region—global launches aren’t feasible
  • Prepare organizations for managing AI’s operational complexity

Key Takeaways

For Healthcare Organizations:

  • Treat AI as a process requiring ongoing commitment, not a one-time product purchase
  • Invest in hands-on training and workforce preparation
  • Build data foundations with interoperability in mind
  • Establish clear governance frameworks for accountability and patient safety

For Technology Developers:

  • Spend time in clinical environments understanding actual workflows
  • Design for transparency with explainable AI outputs
  • Enable easy override mechanisms for clinicians
  • Test across diverse populations to avoid amplifying health inequities

For Clinicians:

  • Engage actively in AI development and implementation
  • Maintain clinical reasoning skills alongside AI tools
  • Approach AI suggestions with appropriate professional skepticism
  • Advocate for patient interests above operational efficiency

Conclusion

The Open Innovator Virtual Session made clear that successfully integrating AI into healthcare requires more than technological sophistication. It demands deep respect for clinical workflows, unwavering commitment to patient safety, and genuine collaboration between technologists and healthcare professionals.

The consensus was unequivocal: Fix the foundation first, then build the intelligent layer. Organizations not ready to manage the operational discipline required for AI development and deployment are not ready to deploy AI. The technology is advancing rapidly, but the fundamental principles—earning trust, ensuring safety, and supporting rather than replacing human judgment—remain unchanged.

As healthcare continues its digital transformation, success will depend on preserving what makes healthcare fundamentally human: empathy, intuition, and the sacred responsibility clinicians bear for patient wellbeing. AI that serves these values deserves investment; AI that distracts from them, regardless of sophistication, must be reconsidered.

The future of healthcare will be shaped not by technology alone, but by how wisely we integrate that technology into the profoundly human work of healing and caring for one another.

Reach out to us at open-innovator@quotients.com or drop us a line to delve into the transformative potential of groundbreaking technologies and participate in our events. We’d love to explore the possibilities with you.

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Applied Innovation

Strategies to Reduce Hallucinations in Large Language Models

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Applied Innovation

Strategies to Reduce Hallucinations in Large Language Models

Large language models (LLMs) such as GPT-3 and GPT-4 have emerged as powerful tools in the rapidly expanding field of artificial intelligence, capable of producing human-like prose, answering questions, and assisting with a range of tasks. However, these models face a basic challenge: the ability to “hallucinate,” or produce information that seems coherent and compelling but is factually incorrect or entirely created.

Understanding LLM hallucinations

LLM hallucinations occur when AI models provide outputs that look grammatically correct and logical but deviate from factual accuracy. This phenomenon can be attributed to a number of factors, including training data shortages, the model’s inability to access real-time information, and linguistic difficulties.

These hallucinations can have far-reaching implications, especially when LLMs are used in critical areas such as healthcare, finance, or journalism. Misinformation generated by these models may lead to poor decision-making, a loss of faith in AI systems, and perhaps harmful consequences in sensitive areas.

Reducing Hallucinations

Recognising the importance of the situation, researchers and AI practitioners have created a number of strategies to decrease hallucinations in LLMs. These strategies aim to improve model accuracy, base replies on factual information, and overall dependability.

1. Retrieval-Augmented Generation (RAG)

One of the most promising techniques is Retrieval-Augmented Generation (RAG). This approach blends the generative capabilities of LLMs with information retrieval systems. RAG aids in ensuring that responses are based on reliable data by letting the model to access and incorporate critical information from external knowledge bases throughout the generating process.

For example, when asked about recent occurrences, a RAG-enhanced model may gather current knowledge from reputable sources, significantly reducing the likelihood of delivering outdated or incorrect information. This approach is particularly useful for domain-specific applications requiring great accuracy.

2. Fine-Tuning with High-Quality Datasets

Another important strategy is to fine-tune LLMs with carefully selected, high-quality datasets. This process provides the model with accurate, relevant, and domain-specific data, allowing it to build a more nuanced understanding of certain issues.

A model built for medical purposes, for example, might be improved by consulting peer-reviewed medical literature and clinical suggestions. This specialised training enables the model to offer more accurate and contextually relevant replies in its own domain, reducing the possibility of hallucinations.

3. Advanced Prompting Techniques

The method in which questions are posed to LLMs has a significant impact on the quality of their responses. Advanced prompting tactics, such as chain-of-thought prompting, encourage the model to explain its reasoning step by step. This strategy not only improves the model’s problem-solving abilities, but it also makes it easier to spot logical flaws or hallucinations throughout the development process. Other techniques, like as few-shot and zero-shot learning, can help models understand the context and intent of queries, leading to more accurate and relevant responses.

4. Reinforcement Learning from Human Feedback

Human monitoring via reinforcement learning is another successful way to combating hallucinations. In this method, human reviewers evaluate the model’s outputs, providing feedback that helps the AI to learn from its mistakes and improve over time.

This iterative process allows for continuous improvements to the model’s performance, bringing it closer to human expectations and factual accuracy. It is particularly useful for spotting minor errors or contextual misunderstandings that would be difficult to discover with automated approaches alone.

5. Topic Extraction and Automated Alert Systems

Using topic extraction algorithms and automated alert systems can give further protection against hallucinations. These systems examine LLM outputs in real time to detect any content that deviates from agreed norms or contains potentially sensitive or incorrect information.

Setting up these automated inspections enables businesses to detect and cure potential hallucinations before they cause harm. This is especially critical in high-risk applications where the consequences of deception might be severe.

6. Contextual Prompt Engineering

Carefully developed prompts with clear instructions and rich contextual information can assist LLMs in producing more consistent and coherent responses. Contextual prompt engineering can significantly minimise the chance of hallucinations by reducing ambiguity and focussing the model’s attention to relevant query components.

This strategy requires an in-depth understanding of both the model’s capabilities and the specific use case, allowing prompt designers to supply inputs that provide the most accurate and meaningful outcomes.

7. Data Augmentation

Improving the training data with more context or examples that fall inside the model’s context window can provide a stronger foundation for comprehension. This method allows the model to get a better grasp of a variety of topics, leading in more accurate and contextually appropriate responses.

8. Iterative Querying

In some circumstances, an AI agent may manage interactions between the LLM and a knowledge base throughout several rounds. This method comprises refining queries and responses in stages, allowing the model to focus on more accurate answers by using more context and information gathered along the process.

Challenges and Future Directions

While these approaches have shown promise in reducing hallucinations, eliminating them remains a significant challenge. The ability of LLMs to generate new text based on patterns in their training data predisposes them to occasional flights of fancy.

Furthermore, implementing these ideas in real-world applications poses distinct challenges. The field’s ongoing difficulties include reconciling the need for accuracy with computer efficiency, maintaining model performance across several domains, and ensuring ethical use of AI systems.

Looking ahead, scholars are looking at new avenues of AI development that might help tackle the hallucination problem. Advances in causal reasoning, knowledge representation, and model interpretability may contribute to the creation of more reliable and trustworthy artificial intelligence systems.

Takeaway:

As LLMs become more important in many parts of our lives, overcoming the issue of hallucinations is key. Combining tactics like as RAG, fine-tuning, smart prompting, and human involvement may significantly improve the accuracy and trustworthiness of these powerful AI technologies.However, there is no optimum answer. Users of LLMs should always treat their findings with caution, especially in high-risk situations. As we work to refine these models and find new approaches to battle hallucinations, the goal remains clear: to maximise AI’s vast potential while ensuring that its outputs are as accurate, reliable, and helpful as possible.

Contact us at open-innovator@quotients.com to schedule a consultation and explore the transformative potential of this innovative technology.

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