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Is AI Ready to Replace Your Doctor?

Have you ever wondered what if doctors could harness the power of many experts, all at once? Imagine every heartbeat, every lab result, and every medication being processed in seconds—faster than any human could ever dream of. No, this isn’t science fiction; it’s the new reality of Artificial Intelligence (AI) and Large Language Models (LLMs) in healthcare. The rise of AI in medicine and medical artificial intelligence is transforming the landscape of patient care and research.

Think of AI as the invisible co-pilot in a doctor’s journey—an entity that never sleeps, forgets nothing, and spots patterns that would take years for a human mind to recognize. It’s like giving healthcare professionals superpowers, enabling them to stay ahead of the curve in ways we never thought possible. But the real magic? Smart alert mechanisms jump into action when things are about to go wrong, providing warnings that save lives and make sure the right decisions happen in real-time. This is where AI for medical diagnosis truly shines, enhancing the capabilities of healthcare professionals.

AI and LLMs are changing the way healthcare works—and we’re at the forefront. Here’s how.

AI Pathology: Microscope with Superpowers

What if your microscope could not only analyze slides but also interpret them? That’s exactly what we did for Pathomiq. Our AI-powered pathology tool doesn’t just scan whole slides—it identifies disease progression and predicts patient responses with unmatched precision. By integrating LLMs, we created a system that not only analyzes images but also generates comprehensive, easy-to-understand diagnostic reports.

For Pathomiq, we trained AI models to detect malignancy patterns with 99% accuracy, and the LLMs translated the results into meaningful insights for doctors, which benefitted them with Faster diagnostics, better accuracy, and simpler communication between specialists.

Medical Image Analysis: X-Rays, But Make It Smart

X-rays, MRIs, and other medical imaging can be a treasure trove of data, but they often need an intelligent eye to make sense of it all. Abbvie came to us with this challenge. Our AI models analyze medical images to pinpoint abnormalities, demonstrating the power of AI medical diagnosis.

AI takes care of the image recognition, while LLMs convert findings into plain language summaries. For Abbvie, this resulted in faster image processing and more accurate interpretations. Clearer insights, faster decisions, and a smart system that even non-experts can understand.

AI Health Advisors

Imagine a health advisor that predicts your next treatment before you even need it. Our AI health advisor uses predictive analytics to identify patients likely to undergo surgery, showcasing how AI forecasts patient outcomes. This is similar to the Nura AI health screening concept, where early predictions combined with actionable, easy-to-read insights mean better health outcomes and proactive care.

Intelligent Document Parsing

Medical documents are notorious for their jargon-heavy content. But what if AI and LLMs could automatically extract the relevant information? That’s exactly what we did with our intelligent document parsing tool. Whether research papers or patient reports, our system extracts key data and presents it in a clear, concise format.

AI handles document parsing for faster decision-making. As there wouldn’t be any more sifting through endless documents—It streamlines the process and saves time.

Drug Discovery: Abbvie’s Fast-Track to Innovation

When Abbvie sought to enhance its drug discovery process, we stepped in with an AI-powered platform that redefines speed and accuracy. We developed a research tool that lists genes with their weighted interconnectivity from research papers, providing a visualization framework to display genes and proteins along with their interconnections. Our AI tools handle complex text parsing across various document formats and perform frequency determination and spectral clustering to identify gene pairs, their locations, and contextual details.

Our AI extracts and visualizes gene data, parses text, and determines the frequency and clustering of gene interactions. This approach accelerates drug discovery, cuts costs, and offers a clearer path from genetic research to real-world drug development.

Clinical Trials: Pathomiq’s AI-Powered Cancer Detection

Clinical trials are all about accuracy and speed, especially in cancer detection. For Pathomiq, we built AI models that analyze digital slides to identify early-stage malignancies. Our AI stepped in to explain the findings and suggest the next steps, streamlining the process for researchers and doctors.

AI detects cancer patterns in digital pathology slides and provides context-rich explanations that make trial results easier to understand. Early cancer detection paired with simplified trial documentation means faster, more accurate results.

Conclusion: AI & LLM—The Future of Healthcare, Today

At Mantra Labs, we’re not just integrating AI and LLMs into healthcare; we’re pioneering a revolution. It is said that AI has the potential to reduce diagnostic errors by up to 30% and streamline drug discovery processes by cutting research times in half. It has revolutionized healthcare by delivering faster diagnostics, improving the accuracy of medical imaging, and optimizing processes like pathology and clinical trials. Yet, even with these advancements, the human touch remains essential. Healthcare professionals bring the empathy, intuition, and ethical judgment that AI, for all its precision, cannot replace. While AI enhances decision-making and efficiency, it’s the collaboration between human insight and machine intelligence that ensures the best outcomes. The future of healthcare is not just about smarter technology, but about how human expertise and AI together can provide faster, more precise, and compassionate care.

Further Reading:

Doctor Who? AI takes center stage in American Healthcare

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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