#FactCheck - Viral Photos Falsely Linked to Iranian President Ebrahim Raisi's Helicopter Crash

Introduction

The Indian healthcare sector has been transforming remarkably. This is mainly due to the development of emerging technologies such as AI and IoT. The rapid adoption of technology in healthcare delivery such as AI and IoT integration along with telemedicine, digital health solutions, and Electronic Medical Records (EMR) have enhanced the efficacy of hospitals, driving growth. The integration of AI and IoT devices in healthcare can improve patient care, health record management, and telemedicine and reshape the medical landscape as we know it. However, their implementation must be safe, with robust security and ethical safeguards in place.

The Transformative Power of AI and IoT in Revolutionising Healthcare

IoT devices for healthcare such as smartwatches, wearable patches, and ingestive sensors are equipped with sensors. These devices take physiological parameters in real-time, including heart rate, blood pressure, glucose level, etc. This can be forwarded automatically from these wearables to healthcare providers and EHR systems. Real-time patient health data enable doctors to monitor progress and intervene when needed. 

The sheer volume of data generated by IoT healthcare devices opens avenues for applying AI. AI and ML algorithms can analyse patient data for patterns that further provide diagnostic clues and predict adverse events before they occur. A combination of AI and IoT opens avenues for proactive and personalised medicine tailored to specific patient profiles. This amalgamation can be a bridge between healthcare accessibility and quality. And, especially in rural and underserved areas, it can help receive timely and effective medical consultations, significantly improving healthcare outcomes. Moreover, the integration of AI-powered chatbots and virtual health assistants is enhancing patient engagement by providing instant medical advice and appointment scheduling. 

CyberPeace Takeaway, the Challenges and the Way Forward 

Some of the main challenges associated with integrating AI and IoT in healthcare include cybersecurity due to data privacy concerns, lack of interoperability, and skill gaps in implementation. Addressing these requires enhanced measures or specific policies, such as:

• Promoting collaborations among governments, regulators, industry, and academia to foster a healthcare innovation ecosystem such as public-private partnerships and funding opportunities to drive collaborative advancements in the sector. Additionally, engaging in capacity-building programs to upskill professionals.
• Infrastructural development, including startup support for scalable AI and IoT solutions. Engaging in creating healthcare-specific cybersecurity enhancements to protect sensitive data. According to a 2024 report by Check Point Software Technologies, the Indian healthcare sector has experienced an average of 6,935 cyberattacks per week, compared to 1,821 attacks per organisation globally in 2024. 

Conclusion 

The Deloitte survey highlights that on average hospitals spend 8–10% of their IT budget on cybersecurity techniques, such as hiring professionals and acquiring tools to minimise cyber-attacks to the maximum extent. Additionally, this spending is likely to increase to 12-15 % in the next two years moving towards proactive measures for cybersecurity. 

The policy frameworks and initiatives are also carried out by the government. One of the Indian government's ways of driving innovation in AI and IoT in healthcare is through initiatives under the National Digital Health Mission (NDHM), the National Health Policy and the Digital India Initiative.

Though the challenges presented by data privacy and cyber security persist, the strong policies, public-private collaborations, capacity-building initiatives and the evolving startup ecosystem carry AI and IoT’s potential forward from the thoughtful merging of innovative health technologies, delivery models, and analytics. If the integration complexities are creatively tackled, these could profoundly improve patient outcomes while bending the healthcare cost curve. 

References

• https://www.ndtv.com/business-news/indian-healthcare-sector-faced-6-935-cyberattacks-per-week-in-last-6-months-report-5989240 
• https://www.businesstoday.in/technology/news/story/meity-nasscom-coe-collaborates-with-start-ups-to-enhance-healthcare-with-ai-iot-458739-2024-12-27 
• https://www2.deloitte.com/content/dam/Deloitte/in/Documents/risk/in-ra-deloitte-dsci-hospital-report-noexp.pdf ‍
• https://medium.com/@shibilahammad/the-transformative-potential-of-iot-and-ai-in-healthcare-78a8c7b4eca1

As AI language models become more powerful, they are also becoming more prone to errors. One increasingly prominent issue is AI hallucinations, instances where models generate outputs that are factually incorrect, nonsensical, or entirely fabricated, yet present them with complete confidence. Recently, ChatGPT released two new models—o3 and o4-mini, which differ from earlier versions as they focus more on step-by-step reasoning rather than simple text prediction. With the growing reliance on chatbots and generative models for everything from news summaries to legal advice, this phenomenon poses a serious threat to public trust, information accuracy, and decision-making.

What Are AI Hallucinations?

AI hallucinations occur when a model invents facts, misattributes quotes, or cites nonexistent sources. This is not a bug but a side effect of how Large Language Models (LLMs) work, and it is only the probability that can be reduced, not their occurrence altogether. Trained on vast internet data, these models predict what word is likely to come next in a sequence. They have no true understanding of the world or facts, they simulate reasoning based on statistical patterns in text. What is alarming is that the newer and more advanced models are producing more hallucinations, not fewer. seemingly counterintuitive. This has been prevalent reasoning-based models, which generate answers step-by-step in a chain-of-thought style. While this can improve performance on complex tasks, it also opens more room for errors at each step, especially when no factual retrieval or grounding is involved.

As per reports shared on TechCrunch, it mentioned that when users asked AI models for short answers, hallucinations increased by up to 30%. And a study published in eWeek found that ChatGPT hallucinated in 40% of tests involving domain-specific queries, such as medical and legal questions. This was not, however, limited to this particular Large Language Model, but also similar ones like DeepSeek. Even more concerning are hallucinations in multimodal models like those used for deepfakes. Forbes reports that some of these models produce synthetic media that not only look real but are also capable of contributing to fabricated narratives, raising the stakes for the spread of misinformation during elections, crises, and other instances.

It is also notable that AI models are continually improving with each version, focusing on reducing hallucinations and enhancing accuracy. New features, such as providing source links and citations, are being implemented to increase transparency and reliability in responses.

The Misinformation Dilemma

The rise of AI-generated hallucinations exacerbates the already severe problem of online misinformation. Hallucinated content can quickly spread across social platforms, get scraped into training datasets, and re-emerge in new generations of models, creating a dangerous feedback loop. However, it helps that the developers are already aware of such instances and are actively charting out ways in which we can reduce the probability of this error. Some of them are:

1. Retrieval-Augmented Generation (RAG): Instead of relying purely on a model’s internal knowledge, RAG allows the model to “look up” information from external databases or trusted sources during the generation process. This can significantly reduce hallucination rates by anchoring responses in verifiable data.
2. Use of smaller, more specialised language models: Lightweight models fine-tuned on specific domains, such as medical records or legal texts. They tend to hallucinate less because their scope is limited and better curated.

Furthermore, transparency mechanisms such as source citation, model disclaimers, and user feedback loops can help mitigate the impact of hallucinations. For instance, when a model generates a response, linking back to its source allows users to verify the claims made.

Conclusion

AI hallucinations are an intrinsic part of how generative models function today, and such a side-effect would continue to occur until foundational changes are made in how models are trained and deployed. For the time being, developers, companies, and users must approach AI-generated content with caution. LLMs are, fundamentally, word predictors, brilliant but fallible. Recognising their limitations is the first step in navigating the misinformation dilemma they pose.

References

• https://www.eweek.com/news/ai-hallucinations-increase/
• https://www.resilience.org/stories/2025-05-11/better-ai-has-more-hallucinations/ 
• https://www.ekathimerini.com/nytimes/1269076/ai-is-getting-more-powerful-but-its-hallucinations-are-getting-worse/ 
• https://techcrunch.com/2025/05/08/asking-chatbots-for-short-answers-can-increase-hallucinations-study-finds/ 
• https://en.as.com/latest_news/is-chatgpt-having-robot-dreams-ai-is-hallucinating-and-producing-incorrect-information-and-experts-dont-know-why-n/
• https://www.newscientist.com/article/2479545-ai-hallucinations-are-getting-worse-and-theyre-here-to-stay/
• https://www.forbes.com/sites/conormurray/2025/05/06/why-ai-hallucinations-are-worse-than-ever/ 
• https://towardsdatascience.com/how-i-deal-with-hallucinations-at-an-ai-startup-9fc4121295cc/ 
• https://www.informationweek.com/machine-learning-ai/getting-a-handle-on-ai-hallucinations 

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There has been a struggle to create legal frameworks that can define where free speech ends and harmful misinformation begins, specifically in democratic societies where the right to free expression is a fundamental value.  Platforms like YouTube, Wikipedia, and Facebook have gained a huge consumer base by focusing on hosting user-generated content. This content includes anything a visitor puts on a website or social media pages. 

The legal and ethical landscape surrounding misinformation is dependent on creating a fine balance between freedom of speech and expression while protecting public interests, such as truthfulness and social stability. This blog is focused on examining the legal risks of misinformation, specifically user-generated content, and the accountability of platforms in moderating and addressing it.

The Rise of Misinformation and Platform Dynamics

Misinformation content is amplified by using algorithmic recommendations and social sharing mechanisms. The intent of spreading false information is closely interwoven with the assessment of user data to identify target groups necessary to place targeted political advertising. The disseminators of fake news have benefited from social networks to reach more people, and from the technology that enables faster distribution and can make it more difficult to distinguish fake from hard news.

Multiple challenges emerge that are unique to social media platforms regulating misinformation while balancing freedom of speech and expression and user engagement. The scale at which content is created and published, the different regulatory standards, and moderating misinformation without infringing on freedom of expression complicate moderation policies and practices.

The impacts of misinformation on social, political, and economic consequences, influencing public opinion, electoral outcomes, and market behaviours underscore the urgent  need for effective regulation, as the consequences of inaction can be profound and far-reaching.

Legal Frameworks and Evolving Accountability Standards

Safe harbour principles allow for the functioning of a free, open and borderless internet. This principle is embodied under the US Communications Decency Act and the Information Technology Act in Sections 230 and 79 respectively.  They play a pivotal role in facilitating the growth and development of the Internet. The legal framework governing misinformation around the world is still in nascent stages. Section 230 of the CDA protects platforms from legal liability relating to harmful content posted on their sites by third parties. It further allows platforms to police their sites for harmful content and protects them from liability if they choose not to.

By granting exemptions to intermediaries, these safe harbour provisions help nurture an online environment that fosters free speech and enables users to freely express themselves without arbitrary intrusions.

A shift in regulations has been observed in recent times. An example is the enactment of the Digital Services Act of 2022 in the European Union.  The Act requires companies having at least 45 million monthly users to create systems to control the spread of misinformation, hate speech and terrorist propaganda, among other things. If not followed through, they risk penalties of up to 6% of the global annual revenue or even a ban in EU countries.

Challenges and Risks for Platforms

There are multiple challenges and risks faced by platforms that surround user-generated misinformation. 

• Moderating user-generated misinformation is a big challenge, primarily because of the quantity of data in question and the speed at which it is generated. It further leads to legal liabilities, operational costs and reputational risks.   
• Platforms can face potential backlash, both in instances of over-moderation or under-moderation. It can be considered as censorship, often overburdening. It can also be considered as insufficient governance in cases where the level of moderation is not protecting the privacy rights of users. 
• Another challenge is more in the technical realm, including the  limitations of AI and algorithmic moderation in detecting nuanced misinformation. It holds out to the need for human oversight to sift through the misinformation that is created by AI-generated content.  

Policy Approaches: Tackling Misinformation through Accountability and Future Outlook

Regulatory approaches to misinformation each present distinct strengths and weaknesses. Government-led regulation establishes clear standards but may risk censorship, while self-regulation offers flexibility yet often lacks accountability. The Indian framework, including the IT Act and the Digital Personal Data Protection Act of 2023, aims to enhance data-sharing oversight and strengthen accountability. Establishing clear definitions of misinformation and fostering collaborative oversight involving government and independent bodies can balance platform autonomy with transparency. Additionally, promoting international collaborations and innovative AI moderation solutions is essential for effectively addressing misinformation, especially given its cross-border nature and the evolving expectations of users in today’s digital landscape.

Conclusion

A balance between protecting free speech and safeguarding public interest is needed to navigate the legal risks of user-generated misinformation poses. As digital platforms like YouTube, Facebook, and Wikipedia continue to host vast amounts of user content, accountability measures are essential to mitigate the harms of misinformation. Establishing clear definitions and collaborative oversight can enhance transparency and build public trust. Furthermore, embracing innovative moderation technologies and fostering international partnerships will be vital in addressing this cross-border challenge. As we advance, the commitment to creating a responsible digital environment must remain a priority to ensure the integrity of information in our increasingly interconnected world.

References

• https://www.thehindu.com/opinion/op-ed/should-digital-platform-owners-be-held-liable-for-user-generated-content/article68609693.ece
• https://www.thehindu.com/opinion/op-ed/should-digital-platform-owners-be-held-liable-for-user-generated-content/article68609693.ece 
• https://hbr.org/2021/08/its-time-to-update-section-230 
• https://www.cnbctv18.com/information-technology/deepfakes-digital-india-act-safe-harbour-protection-information-technology-act-sajan-poovayya-19255261.htm 

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