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Doctor Who? AI Takes Center Stage in American Healthcare

You’re watching an episode of Grey’s Anatomy, and Dr. Meredith Grey isn’t just relying on her surgical skills and medical knowledge but also consulting an AI system that provides real-time diagnostics and treatment recommendations. It might sound like science fiction, but this is rapidly becoming a reality in the healthcare landscape of the USA.

The Dawn of AI in Healthcare

You walk into a hospital where a highly sophisticated AI does your initial screening. Your symptoms are analyzed, and a preliminary diagnosis is ready before you even see a doctor. This is not a far-off future; it’s happening now. For instance, AI-driven tools like IBM’s Watson Health are already assisting doctors by sifting through vast amounts of medical data to identify the most effective treatments for cancer patients.

Transforming Patient Care with AI

AI’s integration into healthcare is enriching patient care in ways we never thought possible. Here are some specific advancements:

AI-Powered Radiology

Advanced AI systems like Google’s DeepMind Health are employing deep learning to diagnose eye diseases from retinal scans with a high degree of accuracy. These AI systems can identify conditions such as diabetic retinopathy and age-related macular degeneration, often before symptoms become severe. For CXOs and CSOs, integrating such AI systems can lead to earlier intervention, reduced costs from late-stage treatments, and better patient outcomes.

Predictive Analytics in Hospitals

Predictive analytics is revolutionizing hospital care by forecasting patient deterioration, readmission risks, and even potential outbreaks of hospital-acquired infections. For example, a system developed by Johns Hopkins uses AI to predict septic shock hours before it happens, giving doctors crucial time to intervene. This predictive capability can significantly reduce mortality rates and improve hospital efficiency, making it a critical investment for healthcare executives aiming to enhance patient safety and operational performance.

Natural Language Processing (NLP) in Medical Records

AI-driven NLP tools are transforming the way physicians interact with medical records. Companies like Nuance have developed AI assistants that can transcribe and analyze physician-patient conversations, ensuring that critical information is accurately captured and reducing the administrative burden on healthcare providers. For healthcare leaders, this means less time on documentation and more time on patient care, improving both provider satisfaction and patient experiences.

AI in Personalized Medicine

Startups like Tempus are using AI to analyze clinical and molecular data at scale, helping oncologists create personalized cancer treatment plans. By examining the genetic mutations in a patient’s tumor, AI can suggest targeted therapies that are more likely to be effective. This precision approach not only improves treatment outcomes but also optimizes resource allocation and treatment costs, offering a compelling value proposition for chief strategy officers focused on innovation and patient-centered care.

The Numbers Speak for Themselves

AI’s impact on healthcare is not just theoretical; compelling data back it:

  • Increased Early Detection: According to the American Cancer Society, AI in mammography has increased early detection rates by 20-30%.
  • Operational Efficiency: Healthcare providers utilizing AI have reported a 15-20% increase in efficiency, allowing them to treat more patients with the same resources.
  • Cost Savings: The McKinsey Global Institute estimates that AI could save the healthcare industry up to $100 billion annually through improved efficiencies in clinical and operational processes.

Quick Facts and Resources

AI in healthcare is expected to grow at a CAGR of 38.5% from 2024 to 2030, according to Grand View Research. Additionally, a study published in The Lancet found that an AI system outperformed radiologists in diagnosing pneumonia from chest X-rays.

Real-World Impact: 

PathomIQ, a leading computational pathology company in the USA, uses an AI-powered cancer detection and grading platform that uses deep learning to identify patterns of prostate cancer in whole slide images (WSIs), reducing pathologists’ workload by requiring a review of only 5% of data. This automation through predictive annotations and high-speed processing demonstrates AI’s transformative potential in cancer detection, grading, and personalized therapy design.

Explore how AI solutions can transform your healthcare practice by checking out our case studies.

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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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