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How Smart Technology in the USA is Improving Efficiency in Healthcare

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Smart technology in healthcare marks is set to make a significant improvement in patient care. In the US, advancements in digital health technologies such as AI, the Internet of Things (IoT), telehealth, and wearable devices are already revolutionizing how healthcare providers approach diagnosis, treatment, and patient management. 

According to recent studies, medical errors are the third leading cause of death in the United States causing up to 251,000 deaths annually. Thankfully, these digital health trends will diminish human errors, enhance the quality of care, streamline operations, reduce costs, and improve access to healthcare services across the population. 

As the healthcare industry embraces these exciting innovations, understanding the role and impact of smart technology is essential in every area, from audiology and eye care to dental and diagnostics. 

The Role of Smart Technology in Healthcare

Hospitals worldwide are suffering from staff shortages and lack of treatment access. Advanced digital tools and systems will tackle these issues by leveraging data, connectivity, and automation to improve healthcare delivery and, ultimately, patient outcomes. These technologies have various applications, from electronic health records and telemedicine to wearables and AI-powered diagnostic tools. Thanks to the power of these innovations we’re about to see a more equitable society with personalized, efficient, and effective healthcare for all. 

Telehealth and telemedicine

The rise of telehealth and telemedicine has been one of the most significant shifts in healthcare delivery, especially during the pandemic. Patients can receive medical consultations and follow-up care remotely, significantly expanding access to healthcare services, particularly for individuals in rural or underserved areas.

Wearable health monitoring devices

Wearables such as fitness trackers, glucose monitors, and smartwatches enable continuous monitoring of health metrics in real-time. These devices are typically worn by people with chronic diseases and studies show that 30% of hospital visits by patients with common conditions are unnecessary. Luckily, ongoing monitoring can alert healthcare providers to potential health issues before they become serious, allowing for proactive management of chronic conditions and overall health without overwhelming hospitals.

Artificial intelligence and machine learning

AI in healthcare offers exceptional precision in diagnostics, treatment plan development, and even in predicting patient outcomes. The benefits of AI in healthcare include analyzing vast amounts of data to identify patterns and insights that would be difficult for humans to discern, leading to more accurate diagnoses and personalized treatment plans.

Blockchain in healthcare

Blockchain technology enhances the security, privacy, and efficiency of health data exchange. Providing secure records of transactions, blockchain can facilitate a transparent and reliable exchange of medical information among healthcare providers, payers, and patients.

Electronic health records 

Fast and accurate data access has transformed how patient records are stored, accessed, and used across the healthcare system. By digitizing these records, healthcare providers no longer have to waste time sourcing and authenticating patient information, facilitating better-informed decision-making and more coordinated care.

Benefits of Smart Technology in Healthcare

Thanks to smart technology, the world is becoming more advanced in many areas – including the healthcare industry. Patients and healthcare workers alike can enjoy a multitude of benefits, including efficient and effective medical services. With improved patient care and optimized everyday operations, these benefits lead to significant cost savings and increased satisfaction overall.

Fast, efficient operations

AI in healthcare provides better administrative and clinical processes, reducing the time and resources typically required for routine tasks. Electronic health records, for example, have eliminated paper documentation, making patient data easily accessible and shareable among healthcare professionals. The automation of scheduling, billing, and inventory management also reduces administrative strain, giving medical staff more time to focus more on patient care. Another way healthcare providers can minimize costs and maximize efficiency is by monitoring overall equipment effectiveness. OEE assesses the effectiveness of medical equipment and technologies, ensuring they are available, reliable, and performing at their optimal capacity. By monitoring and improving OEE, healthcare facilities can reduce equipment downtime, enhance patient care, decrease delays in diagnosis or treatment, and ensure a higher return on investment in medical technology.

Improved patient care and satisfaction

Access to healthcare is something that the world has struggled with for decades. But thanks to telehealth services and wearable health monitoring devices, providers can now give personalized patient care outside of traditional clinical settings. Quality of care, increased patient engagement and satisfaction are some benefits of AI in healthcare, providing patients with greater control over their health and well-being. While trained human healthcare workers offer excellent services, smart technology can greatly enhance them. AI-driven diagnostics and treatment produce high-degree precision and personalization, leading to better health outcomes. 

Reduced healthcare costs

One of the barriers to receiving correct, quality healthcare is finances. Luckily, smart technology is significantly reducing healthcare costs in many ways. For instance, telehealth decreases the need for in-person visits, saving money for healthcare providers and patients. Predictive analytics and real-time health monitoring can prevent costly emergency interventions by identifying potential health issues before they escalate. Plus, AI and machine learning optimize resource allocation, ensuring that healthcare services are delivered more cost-effectively.

Improved accuracy in diagnostics 

Traditionally, coming to an official diagnosis can take a long time due to inspecting data and cross-examinations. Sometimes, this can lead to inaccuracies or late diagnosis with little room for treatment. Fortunately, AI in healthcare and machine learning algorithms can easily analyze large amounts of medical data with incredible accuracy and speed, outperforming the usual methods we use to identify diseases and predict outcomes. For instance, AI algorithms can more accurately interpret imaging results, leading to earlier and more precise diagnoses of conditions like cancer, cardiovascular diseases, and neurological disorders. Using this accurate data, healthcare providers can plan personalized treatments and provide better overall patient care. 

Challenges and Considerations

While smart technology in healthcare brings many advantages, it also presents some challenges. Privacy and security concerns are critical, as the increased digitization of health information raises the risk of data breaches. Ensuring the equitable distribution of these technologies is crucial to avoid widening the health disparity gap. But, there’s a need for comprehensive training for healthcare professionals to adapt to these new technologies effectively. Overcoming these barriers requires thoughtful policy-making, strong security measures, and ongoing education.

Optimizing Healthcare

As digital trends suggest, smart technology will boost efficiency in the USA by improving every area of healthcare, from diagnostics to disease prevention. Despite challenges, the potential benefits are vast and exciting. To learn more about smart technology in healthcare and other industries, speak to our professional team at Mantra Labs for more information today. 

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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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