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The Role of Generative AI in Healthcare

Artificial intelligence (AI) is transforming the healthcare industry in various ways, from improving diagnosis and treatment to enhancing patient experience and reducing costs. One of the most promising and innovative branches of AI is generative AI. 

Generative AI uses deep learning models, such as generative adversarial networks (GANs) or large language models (LLMs), to learn from extensive data and produce realistic and diverse outputs.

According to a report by Market.us, the global Gen-AI in healthcare market size was valued at USD 1.2 billion in 2022 and is expected to reach USD 8.9 billion by 2032, growing at a CAGR of 22.7% during the forecast period. 

Given the broad focus, this emerging technology has enormous potential to revolutionize healthcare in unprecedented ways, but it also poses some challenges and risks that need to be addressed.

What are the applications of generative AI in healthcare?

Generative AI has many potential applications in healthcare, such as:

• Data augmentation: Firms can create synthetic data that can augment the existing data and improve the performance and accuracy of other AI models. For example, creating synthetic medical images that can help train diagnostic or predictive models with more data and diversity. 

American healthcare company, CloudMedX is a computing platform that improves patient outcomes using predictive analytics. It uses AI to collect data and build holistic pictures of individuals and communities. Its single, unified data platform has operational, clinical, and financial functions, meaning healthcare providers can find everything they need in one place. 

The company’s predictive healthcare models can predict disease progression and determine the likelihoods that patients may have complications by processing medical data and providing risk assessment scores. 

• Data privacy: Using generative AI, healthcare companies can create anonymized data to protect patients’ and providers’ privacy and security. For example, synthetic patient records can be used for research or analysis without revealing actual patients’ identities or sensitive information.

• Data generation: We can create new data or content that can provide insights or solutions for healthcare problems. For example, USA-based startup Persado uses generative AI to create personalized and persuasive content for healthcare communication and engagement. Their digital solutions, Persad PerScribed and Persado Motivation AI Platform have helped healthcare companies, insurers, and retail clinics conduct effective campaigns. 

• Data enhancement: Generative AI can enhance the existing data or content by adding more details or quality. For example, the tech can help respond to patient queries better. Google DeepMind has developed MedPaLM, a large language model (LLM) trained on medical datasets that can respond to healthcare queries. 

Nuance Communications, a technology provider of advanced conversational AI for ambient clinical documentation and decision support through voice biometrics; and specialized ambient sensing hardware, leverages Open AI’s Chat GPT to enhance customer responses and manage administrative tasks. 

Data synthesis: Generative AI can synthesize different data or content types to create a comprehensive and coherent output. AI-based firm Zebra Medical Vision has developed more than 11 algorithms to help medical professionals detect diseases better. Their HealthMammo tool is trained on over 350,000 mammogram reports and detects cancer with a 92% success rate compared to 87% among radiologists.

What are the challenges and risks of generative AI in healthcare?

Generative AI is still an evolving technology that faces some challenges and risks, such as:

• Quality and reliability: Generative AI may produce inaccurate or unrealistic outputs that may mislead or harm users. For example, it may generate false medical information that may affect diagnosis or treatment decisions or generate fake medical images that may violate ethical standards.

• Regulation and governance: There may be a lack of clear rules or guidelines for its development and use in healthcare. For example, there may be questions about accountability, transparency, explainability, fairness, and safety in healthcare settings.

• Ethics and trust: Given the lack of human touch, generative AI may pose ethical and social issues that may affect the trust and acceptance of users. The digital products using it creates may generate harmful or offensive content that affects public health in a worst-case scenario.

Conclusion

Generative AI is a rapidly evolving ecosystem of tools that holds enormous promise for healthcare. It can address some healthcare challenges, such as pandemics, chronic diseases, staff shortages, and administrative burdens. However, the technology also comes with its own challenges and risks that must be carefully considered and managed. Therefore, it is essential to develop trustworthy and responsible generative AI systems that can benefit healthcare without compromising its quality and integrity.

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NPS in Insurance Claims: What Insurance Leaders Are Doing Differently

Claims are the moment of truth. Are you turning them into moments of loyalty?

In insurance, your app interface might win you downloads. Your pricing might drive conversions.
But it’s the claims experience that decides whether a customer stays—or leaves for good.

According to a survey by NPS Prism, promoters are 2.3 times more likely to renew their insurance policies than passives or detractors—highlighting the strong link between customer advocacy and retention.

NPS in insurance industry is a strong predictor of customer retention. Many insurers are now prioritizing NPS to improve their claims experience.

So, what are today’s high-NPS insurers doing differently? Spoiler: it’s not just about faster payouts.

We’ve worked with claims teams that had best-in-class automation—but still had low NPS. Why? Because the process felt like a black box.
Customers didn’t know where their claim stood. They weren’t sure what to do next. And when money was at stake, silence created anxiety and dissatisfaction.

Great customer experience (CX) in claims isn’t just about speed—it’s about giving customers a sense of control through clear communication and clarity.

The Traditional Claims Journey

  • Forms → Uploads → Phone calls → Waiting
  • No real-time updates
  • No guidance after claim initiation
  • Paper documents and email ping-pong

The result? Frustrated customers and overwhelmed call centers.

The CX Gap: It’s Not Just Speed—It’s Transparency

Customers don’t always expect instant decisions. What they want:

  • To know what’s happening with their claim
  • To understand what’s expected of them
  • To feel heard and supported during the process

How NPS Leaders Are Winning Loyalty with CX-Driven Claims and High NPS

Image Source: NPS Prism

1. Real-Time Status Updates

Transparency to the customer via mobile app, email, or WhatsApp—keeping them in the loop with clear milestones. 

2. Proactive Nudges

Auto-reminders, such as “upload your medical bill” or “submit police report,” help close matters much faster and avoid back-and-forth.

3. AI-Powered Document Uploads

Single-click scans with OCR + AI pull data instantly—no typing, no errors.

4. In-the-Moment Feedback Loops

Simple post-resolution surveys collect sentiment and alert on issues in real time.

For e.g., Lemonade uses emotional AI to detect customer sentiment during the claims process, enabling empathetic responses that boost satisfaction and trust.

Smart Nudges from Real-Time Journey Tracking

For a leading insurance firm, we mapped the entire in-app user journey—from buying or renewing a policy to initiating a claim or checking discounts. This helped identify exactly where users dropped off. Based on real-time activity, we triggered personalized notifications and offers—driving better engagement and claim completion rates.

Tech Enablement

  • Claims Orchestration Layer: Incorporates legacy systems, third-party tools, and front-end apps for a unified experience.
  • AI & ML Models: For document validation, fraud detection, and claim routing, sentiment analysis is used. Businesses utilizing emotional AI report a 25% increase in customer satisfaction and a 30% decrease in complaints, resulting in more personalized and empathetic interactions.
  • Self-Service Portals: Customers can check their status, update documents, and track payouts—all without making a phone call.

Business Impact

What do insurers gain from investing in CX?

A faster claim is good. But a fair, clear, and human one wins loyalty.

And companies that consistently track and act on CX metrics are better positioned to retain customers and build long-term loyalty.

At Mantra Labs, we help insurers build end-to-end, tech-enabled claims journeys that delight customers and drive operational efficiency.
From intelligent document processing to AI-led nudges, we design for empathy at scale.

Want a faster and more transparent claims experience?

Let’s design it together.
Talk to our insurance transformation team today.

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