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What will be the state of the healthcare industry post pandemic?

4 minutes, 9 seconds read

The COVID-19 has proven to be havoc in this tech-savvy world. The community of Healthcare and Development has become the epicentre of the World’s attention for the motives of fighting against the disease; providing social services in this pandemic situation and promoting humanity and livelihood above all. 

However, on the flip side of the coin, we are witnessing challenges like never before. With the outbreak of this catastrophic pandemic, medical accessibility and safety have become our primary concern, bringing about a paradigm change in the state of the healthcare industry throughout the world.

As goes the old adage, “Necessity is the Mother of Invention”; the healthcare sector, post COVID-19 pandemic; is about to undergo metamorphosis with a plethora of new ideas. Getting accustomed to the lockdown phase, people are more and more acquainted with the use of technology. From 8 to 80 almost everyone has resorted to the digital platform and shall continue to retain the habit post-pandemic. Like other brick and mortar bodies, a huge part of healthcare shall have to move online, too.

AI-powered customer support

The idea of telecommunication in the field of healthcare will see a sudden spike in usage. The number of telehealth consults has risen exponentially during this pandemic and it will multiply manifolds post COVID-19. During this outbreak, with an increase in queries and lack of live agents, AI-powered customer support can be used as the first line of communication. Unlike old IVR’s, AI-enabled customer support shall understand the patient’s needs and converse with them as a live agent. 

Vozy’s Lili, is a conversational AI platform for healthcare organizations that alleviates pressure caused due to high call volume. Apart from providing customer assistance, it maintains a complete patient flow and helps monitor the health conditions post-treatment.

AI in customer support

Healthcare professionals are also opting for chatbots for checking symptoms to access symptoms, understand the conditions and accordingly suggest remedies or schedule appointments. 

Automation for contactless patient management

While we pull up our socks for a strategic battle, we can promote our major workforce and healthcare by optimizing and digitizing it, sans promoting widespread of this contagious phenomenon.

Data management of patient’s documents not only consumes a lot of bandwidth of medical staff but might also increase the phobia of the spread of coronavirus through touch, post-pandemic.

“End-user organizations adopt RPA technology as a quick and easy fix to automate manual tasks,” said Cathy Tornbohm, vice president at Gartner.

Healthcare applications, like Practo, can not only automate healthcare data management but also provide expert suggested healthcare tips. It connects with the nearest doctors and helps you choose on the basis of feedback, fees and doctor’s profile. It provides affordable healthcare packages, free healthcare tips and many more.

Automation for contactless patient management - Practo

With the implementation of automation in healthcare, it will not only reduce redundancy time but also provide an unbiased and transparent workflow. 

[Also read – Are wellness and diagnostic apps transforming ‘Patient Experience’]

Remote monitoring

AI in healthcare is going to be the next big revolution. Preserving human life by implementing robotic operations would be the next big step in the medicine industry. Basic hygiene will become the most important factor and the scarcity of equipment which we are facing will alarm us to prepare in an exponential and not in a linear way.

In radiology, medical professionals examine medical images such as an X-Ray, ECG or a radiogram to diagnose the illness and suggest a solution. With telemedicine being very popular in present times, workstations can be created where radiologists worldwide can consult each other. With the help of AI and machine learning, solutions can be suggested to the medical practitioner. 

Neucleus.io is one such web-based work station that provides access to medical images with diagnostic workstation performance. 

Medical Images Management - healthcare industry

Training neural networks with the results of past attempts can rule out the need to test every combination in drug creation. It can also guide the treatment discovery process and help in telemedicine through drug selection.

To maintain social distancing and contactless patient monitoring, Robot doctors of Canada are already performing real-time ultrasound and helping doctors treat patients remotely.  

A different future for the healthcare industry

Post pandemic, more of the typical traditional process requiring human functioning will be replaced by machines, to work more swiftly, providing better results. Thermal sensors will be incorporated in our everyday use gadgets like Mobile phones to allow a thermal scanning process so as to differentiate between normal and ill people on the basis of parameters like body temperature, sweat, facial symptoms, etc. 

Digital transformation will be prevalent everywhere post this catastrophe and machines, technologies and AI will become the tools in reshaping the structure of the healthcare industry. If such a situation knocks our door again, we will be all set to sail through the storm.

Check out the webinar on ‘Digital Health Beyond COVID-19: Bringing the Hospital to the Customer’ on our YouTube channel

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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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