Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(3)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(33)

Technology Modernization(9)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(152)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(8)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

Can AI Be Your Superhero in Disease Detection?

For decades, disease detection relied on physical exams and limited diagnostic tools. While these remain essential, advancements in AI are ushering in a new era of healthcare. Imagine a tireless medical detective scrutinizing vast amounts of data to identify potential threats before they become serious. This is the power of AI-powered screening tools, poised to revolutionize healthcare. AI promises a future where early detection becomes faster, more accurate, and accessible to all. Let’s delve into how AI is transforming preventive care…

Here’s how AI is redefining the way we approach preventive care:

  • Eagle Eyes for Early Detection: A 2023 study on the National Library Of Medicine highlights that AI algorithms can analyze medical images like X-rays and mammograms at a staggering 10 times the speed of humans while maintaining high accuracy. This translates to earlier diagnoses, improved treatment outcomes, and potentially saved lives.
  • Beyond the Human Scope: AI can sift through vast medical data, including patient history, lab results, and genetic information. This allows for a more comprehensive analysis and identifying subtle patterns that might escape the human eye. Studies suggest AI can even outperform doctors in some screening tasks.
  • Democratizing Healthcare: A major hurdle in preventive care is accessibility. AI-powered screening tools can be deployed in remote areas or used by primary care physicians, reducing the burden on specialists. This is particularly significant for diseases like diabetic retinopathy, where early detection is crucial but access to ophthalmologists might be limited.

Helping to Automate Cancer Detection

PathomIQ, a computational pathology company, partnered with Mantralabs to tackle the challenge of automating prostate cancer detection from complex whole slide images. We built an AI solution using a deep learning architecture to identify five distinct cancer patterns. This frees up pathologists’ time by automating analysis, potentially leading to faster and more efficient diagnoses. Here are the key outcomes of the AI implementation:

  • Automated Prostate Cancer Pattern Detection: The platform successfully learned to identify five distinct prostate cancer patterns: stroma (normal cells), benign (early stage), and Gleason Patterns 3, 4, and 5 (increasing severity).
  • Reduced Workload for Pathologists: The AI system achieved high accuracy, allowing pathologists to focus on reviewing only a small percentage (less than 5%) of the data for annotations. This frees up their time for more complex tasks.
  • Improved Efficiency: The platform utilizes high-speed processing and streamlines the workflow through automation, potentially leading to faster analysis and diagnosis.

This collaboration between PathomIQ and Mantralabs represents a significant advancement in the fight against cancer. This AI solution can potentially improve diagnostic efficiency and probably save lives by automating prostate cancer detection and reducing pathologist workload.

Challenges and the Road Ahead

While the potential of AI in healthcare screening is undeniable, there are challenges to address:

  • Data Bias: AI algorithms are only as good as the data they’re trained on. Biases in medical data can lead to inaccurate diagnoses for certain demographics. Mitigating bias requires diverse datasets and ongoing monitoring.
  • Human Expertise Remains Crucial: AI shouldn’t replace doctors, but rather be a powerful tool that assists them. The final call on diagnosis and treatment should always come from a qualified medical professional.
  • Regulation and Transparency: As AI becomes more integrated into healthcare, robust regulations and clear communication are essential to ensure patient trust and ethical use.

The Future of AI-powered Screening

The future of healthcare screening is undoubtedly intertwined with AI. As technology advances and these challenges are addressed, we can expect a new era of preventive care:

  • Personalized Screening: AI can tailor screening programs to individual risk factors, making them more efficient and effective.
  • Real-time Monitoring: Wearable devices with AI integration could continuously monitor health vitals, allowing for early intervention and preventing complications.

AI holds immense promise for revolutionizing healthcare screening. By leveraging its strengths and addressing the challenges, we can move towards a future where preventive care is faster, more accurate, and accessible to all.

How Mantra Labs Can Help

Mantralabs is investing heavily in the research and development of cutting-edge AI solutions for the healthcare industry. We understand the challenges of implementing AI in screening programs, and we have the expertise to help companies overcome them. We can help you:

  • Develop AI-powered screening tools tailored to your specific needs.
  • Mitigate bias in your AI models to ensure fair and accurate diagnoses.
  • Integrate AI seamlessly into your existing healthcare workflows.

Connect with us today to learn how we can help you revolutionize healthcare screening with AI.

Cancel

Knowledge thats worth delivered in your inbox

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

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot