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HealthTech 101: How are Healthcare Technologies Reinventing Patient Care

4 minutes, 58 seconds read

Technological advancements and innovations are disrupting the healthcare industry. Smart health monitoring systems, apps, wearables and handheld devices are already in use. The prevailing Covid-19 pandemic has created an urgency to adopt digital. Healthcare technologies will cover many more conditions than before. Dr. John Halamka, President, Mayo Clinic Platform expects more than 60% of healthcare services to go virtual

This revolution in healthcare is not discretionary. This is the need of time. Currently, segregating meaningful data collected through various sources like medical records, wearables, apps, etc. is a challenge. But very soon, HealthTech will evolve across the globe. With Cloud, AI and advanced data analytics, patients and healthcare institutions will be able to access and utilize the right information in a fraction of seconds.

Let’s delve deeper into the new healthcare technologies that will disrupt patient care.

1. Telehealth

Telehealth corresponds to the accessibility of health services and information over the internet and telecommunication. Telehealth care allows remote or long-distance patient care through clinician contact, consultations, reminders, monitoring, and remote admissions. Simply put, telehealth care is the virtualization of most of the physical interactions between doctors and patients. 

Today, HealthTech underpins telehealth, as it enables robotic surgeries through remote access, physical therapy via remote monitoring instruments, home monitoring and live feeds, and video telephony. 

Recent advancements in AI and cloud-based technologies are enhancing remote healthcare experiences for patients. Solutions like chatbots, voice interfaces, and augmented reality are making digital experiences more intuitive for users.

Advancements in TeleHealth

2. Interoperability

To deliver informed and better care, healthcare organizations need to access patient health information over a distributed network. However, due to prevailing privacy regulations and lack of standardization in healthcare institutions, necessary information is still not available when required. That’s why interoperability has become a crucial aspect of HealthTech. 

Interoperability is the ability to exchange, interpret, use, and annotate patients’ health information including medical reports, images (X-rays, CT Scans, Radiographs, etc.) and treatment information through secure communication channels.

Health data standardization is necessary to ensure interoperability. So far, many different standards development organizations (SDOs) create, update, and maintain health data standards. For example, the Interoperability Standards Advisory (ISA) is one of the institutions that define interoperability standards and implementation specifications for the industry to fulfill specific clinical health IT interoperability needs. DICOM (Digital Imaging and Communications in Medicine) is one of the methods of medical image sharing. Using the DICOM system, health management professionals, physicians, and radiologists access medical images in a secure distributed environment.

[Related: Medical Image Management: DICOM Images Sharing Process]

However, to create an ecosystem of connected healthcare services, information needs to be available on the cloud and in a uniform format. There are three levels of interoperability:

  1. Foundational: Here, one system can share information with the other. The receiving system cannot interpret the information but can acknowledge the receipt.
  2. Structural: Here, the receiving system can interpret and use the information but cannot modify it.
  3. Semantic: Here, both the sender and receiver can interpret, use, and annotate the information. Semantic interoperability is the most desirable system in today’s time.

Interoperability across healthcare service providers can also reduce the time and cost of lab tests. For instance, many health checkups are valid for about a year. In case of emergencies, instead of advising patients tests, medical professionals can access previous test information and start procedures — reducing the overall treatment time.

3. Biomedical Computing

Biomedical computing is the application of computer science in medicine. It involves medical data management, medical imaging systems, developing advanced user interfaces for medical professionals, remote monitoring systems, medical diagnosis, scientific visualizations, and other computer-aided medical solutions.

The advanced application of biomedical computing involves using machine learning models for cancer detection and grading, predictive biomarkers and accelerating drug discovery processes. For example, Seg3D, a volume segmentation & processing tool allows segmentation, contouring to plan complex surgeries.

Seg3D - biomedical computing software

4. Health Forecasting

The right information is important for delivering care, products, and services to people in need. Today, many devices generate health data — home assistants, fitness bands, health and sleep trackers, diabetes monitors, and other ailment specific apps. However, predicting a condition and preparing for it requires reliable data and appropriate analytical tools. 

Extreme events test the efficiency of a healthcare system. Not all traditional techniques (e.g. analytics models that rely on historical data) can be applied to forecasting future conditions. The HealthTech systems call for probabilistic health forecasting methods to prepare institutions with information, finance, resources, drugs, equipment, and staff to serve any unforeseen event with the least possible lag.

The Future of Healthcare Technologies

Technologies like Augmented Reality, Virtual Reality, AI, Machine Learning will play a crucial role in transforming patient experience as well as augmenting skills and education of future doctors. For example, Cleveland Clinic at Case Western Reserve University is already using AR to train human anatomy and surgery through 3D human models.

HealthTech in India will soon control patient care over traditional OPD services. Although critical medical surgeries will still require the dexterity of medical professionals, patient support and routine consultations will be accomplished through telehealth services. This will also make health services available in remote areas where setting up and managing a full-fledged hospital facility is not feasible. 

To know about how healthcare industry is bringing hospitals to a customer’s doorstep, watch our webinar on Digital Health Beyond COVID-19.


Mantra Labs has been helping diagnostic and healthcare organizations like Manipal Hospitals, Suraksha Diagnostics in developing holistic patient management systems. We’ve also helped healthcare technology firms like PathomIQ in developing machine learning models for AI-based cancer detection segmentation and classification.

For your specific requirement, please feel free to write to us at hello@mantralabsglobal.com


Common FAQs

What is HealthTech?

HealthTech or Healthcare Technology is the application of knowledge and skills to solve a health problem and improve quality of life. It involves devices, medicines, vaccines, procedures and systems. WHO.

What is Telehealth?

Telehealth is making healthcare services and information available to the public through the internet and telecommunications. It involves online or video consultations, remote monitoring, reminders to take medicine, remote mental health therapy, patient support, SOS alerts and more.

What is interoperability in healthcare?

Interoperability corresponds to healthcare systems working together irrespective of geographical location. For example, medical images sharing via DICOM; guided permission to share patient data across clinics, labs, hospitals, and pharmacies.

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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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