Tag: AI

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MedTech

The EU AI Act vs MDR/IVDR: Europe’s MedTech Regulatory Collision

The EU wants to be the global leader in AI and in medical technology. Unfortunately, its own flagship regulations are on a collision course. Europe’s innovators are stuck in the middle.

EU AI Act MDR IDVR MedTech regulations in Europe

One year after the EU AI Act entered into force, the continent’s medtech trade body MedTech Europe is calling for a four-year delay in its application to medical technologies. Why? Because a single AI-driven device could now be regulated twice: once under the Medical Devices Regulation (MDR) or In Vitro Diagnostics Regulation (IVDR), and again as a “high-risk” system under the AI Act.

That means two sets of conformity assessments, two quality management systems, two mountains of technical documentation and double the post-market reporting burden. For lean startups, this isn’t just red tape. It’s a growth chokehold.

The dual burden problem

At first glance, the two regimes seem complementary. Both care about safety, risk management, and continuous monitoring. In reality, their definitions, processes, and paperwork don’t line up.

An AI-powered imaging tool could be a relatively low-risk Class IIa device under MDR, yet automatically high-risk under the AI Act. That triggers an entirely separate certification pathway, with no guarantee that an MDR-designated Notified Body can handle AI Act conformity checks.

Capacity is already stretched thin. There are too few Notified Bodies for MDR/IVDR demand. Adding AI Act assessments without clear rules risks a regulatory gridlock where ready-to-market products sit idle.

Collateral damage: clinical research

The AI Act could even hit innovation before products launch. MedTech Europe warns that clinical investigations required to generate evidence for MDR/IVDR approval aren’t explicitly exempt from AI Act obligations.

In practice, that could delay or derail trials as companies wrestle with a second compliance framework mid-development. Given that several EU Member States have already missed the AI Act’s August 2025 deadlines for national authorities and sanctions, readiness is questionable at every level.

Why this matters for investment

For founders, this uncertainty means longer timelines, higher legal and compliance costs, and greater risk. All red flags for investors. For VCs, it erodes capital efficiency and predictability, making US or Asian markets look more attractive for AI-medtech plays.

My position: safety first, but smarter regulation

Patient safety is non-negotiable. The AI Act’s principles of transparency, accountability, and risk management are sound. But doubling up on processes that don’t align will drain resources from where they matter most: clinical validation and safe deployment.

Instead of overlapping statutory frameworks, Europe should consider an industry-led code of practice for AI in healthcare, modelled on the ABPI Code of Practice in UK pharma marketing. Such a code could set high, enforceable standards for AI safety and ethics, developed and policed by industry bodies, with regulators holding a backstop role.

This approach would keep standards high, compliance practical, and innovation alive without making startups navigate two regulatory mazes at once.

Founder and investor playbook

Founders:

  • Map your AI features against both MDR/IVDR and AI Act risk classifications now.
  • Build a “dual compliance” roadmap showing how you’ll meet both sets of requirements in parallel.
  • Use this as a de-risking narrative in investor pitches; proactivity here is a credibility boost.

Investors:

  • Add a “regulatory burden scorecard” to due diligence.
  • Prioritise teams with in-house regulatory expertise and realistic dual-compliance budgets.
  • Recognise that a slightly less flashy product with a watertight compliance plan may outperform in this environment.

Takeaway:
The EU’s ambition to lead in AI and medtech is laudable. But until the AI Act and MDR/IVDR are harmonised, ideally through an industry-led, safety-focused code, Europe’s most innovative startups will spend more time in the compliance maze than in the clinic.

Let’s keep this conversation going
If you’re a founder, investor, or policymaker navigating the AI Act–MDR/IVDR clash, I’d like to hear your perspective. Are you already planning for dual compliance, or do you see a better path forward?
Follow Disrupting Healthcare for more EU medtech analysis and practical strategies for turning regulatory headwinds into competitive advantage.

This content has been enhanced with GenAI tools.

Categories
MedTech Pharma Marketing

AI in Omnichannel Pharma Marketing: Machine Learning Applications & Strategies 2025

FAQ #10: How Can AI and Machine Learning Enhance Omnichannel Marketing?

AI and machine learning represent transformative technologies that enable sophisticated personalization, predictive analytics, and automation capabilities essential for effective omnichannel marketing at scale. These technologies address the complexity and data volume challenges inherent in integrated multi-channel campaigns.1

AI-Powered Personalization at Scale

Dynamic Content Customization leverages machine learning to deliver highly relevant experiences:2

  • Real-time content adaptation based on customer behavior and preferences
  • Predictive content recommendations anticipating information needs
  • Automated A/B testing optimizing messaging across different segments
  • Cross-channel consistency maintaining personalization across touchpoints

Advanced Segmentation Capabilities:

  • Behavioral clustering identifying previously unknown customer patterns
  • Predictive modeling forecasting customer lifecycle stages and needs3
  • Look-alike modeling expanding successful segment targeting
  • Dynamic segmentation updating customer profiles in real-time

Predictive Analytics for Customer Journey Optimization

Customer Behavior Forecasting enables proactive engagement strategies:4

  • Next-best-action recommendations optimizing customer interactions
  • Churn prediction models identifying at-risk relationships
  • Engagement timing optimization determining optimal contact frequencies
  • Channel preference prediction selecting most effective communication methods

Journey Intelligence Applications:

  • Path analysis identifying most effective customer journey sequences
  • Conversion prediction forecasting likelihood of desired outcomes
  • Intervention optimization determining when and how to influence journeys
  • Attribution modeling understanding cross-channel impact on outcomes

Natural Language Processing and Content Generation

Automated Content Creation streamlines omnichannel content development:5

  • Dynamic content generation creating channel-specific variations from core materials
  • Language adaptation for different audiences and technical levels
  • Sentiment analysis optimizing content tone and approach
  • Compliance checking ensuring regulatory adherence across content variations

Conversational AI Applications:

  • Chatbot integration providing 24/7 customer support across channels6
  • Voice assistant compatibility enabling voice-based interactions
  • Natural language queries improving search and content discovery
  • Automated response generation for customer service and support

Marketing Automation and Orchestration

Intelligent Campaign Management automates complex omnichannel workflows:7

  • Trigger-based automation responding to customer actions across channels
  • Multi-channel sequencing coordinating message timing and content
  • Dynamic creative optimization adapting visuals and messaging in real-time
  • Performance-based optimization automatically adjusting campaigns based on results

Resource Allocation Optimization:

  • Budget optimization across channels using performance predictions
  • Channel mix optimization determining optimal resource allocation
  • Timing optimization scheduling interactions for maximum effectiveness
  • Capacity planning predicting resource needs for campaign management

Real-Time Analytics and Decision Making

Performance Monitoring and Optimization:8

  • Real-time dashboard updates tracking campaign performance across channels
  • Anomaly detection identifying unusual patterns requiring attention
  • Performance prediction forecasting campaign outcomes
  • Automated optimization making real-time campaign adjustments

Customer Experience Analytics:

  • Journey mapping visualizing customer paths across touchpoints
  • Experience scoring quantifying customer satisfaction at each interaction
  • Friction identification highlighting areas for experience improvement
  • Personalization effectiveness measuring impact of AI-driven customization

Healthcare-Specific AI Applications

Medical Content Intelligence:9

  • Clinical data analysis informing content strategy and messaging
  • Drug interaction checking ensuring safe information delivery
  • Symptom pattern recognition supporting patient education initiatives
  • Treatment outcome prediction personalizing patient journey content

Regulatory Compliance Automation:10

  • Content compliance scoring using AI to assess regulatory adherence
  • Automated approval workflows streamlining review processes
  • Regulatory update monitoring tracking changes across jurisdictions
  • Risk assessment automation identifying potential compliance issues

Implementation Strategies and Best Practices

AI Integration Roadmap:

  • Pilot program development starting with specific use cases and expanding
  • Data infrastructure preparation ensuring quality data for AI model training
  • Cross-functional collaboration integrating AI across marketing, sales, and medical teams
  • Performance measurement establishing metrics for AI-driven improvements

Technology Selection Criteria:

  • Healthcare industry specialization ensuring domain expertise and compliance
  • Integration capabilities with existing marketing technology stacks
  • Scalability requirements supporting growth and expanding use cases
  • Security and privacy protections meeting healthcare regulations

ROI and Performance Impact

Measurable Benefits of AI implementation in omnichannel marketing:9

  • Conversion rate improvements through better personalization and targeting
  • Cost reduction via automation of manual processes and optimization
  • Customer satisfaction increases from more relevant and timely interactions
  • Operational efficiency gains through intelligent workflow automation

Case Study Example: Pfizer’s ‘Charlie’ AI tool streamlines content creation and regulatory compliance, demonstrating practical applications of AI in pharmaceutical marketing operations.5

Future AI Developments

Emerging Technologies shaping omnichannel marketing evolution:11

  • Generative AI for creative content development and adaptation
  • Computer vision for visual content analysis and optimization
  • Advanced NLP for multilingual content creation and cultural adaptation
  • Federated learning enabling AI training while maintaining privacy

Strategic Considerations:

  • Ethical AI development ensuring transparent and responsible implementations
  • Human-AI collaboration balancing automation with human expertise and oversight
  • Continuous learning systems that improve performance over time
  • Privacy-preserving AI technologies protecting sensitive healthcare data

AI and machine learning multiply omnichannel marketing effectiveness by enabling sophisticated personalization, predictive optimization, and automation capabilities that would be impossible to achieve manually at scale.

This is a part of The Complete Guide to Omnichannel Marketing in Pharma and Medtech series.

This content has been enhanced with GenAI tools.

Read other series:

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MedTech Pharma Marketing

Navigating MLR with AI

What is MLR and why does it matter in pharma and MedTech marketing?

MLR stands for Medical, Legal and Regulatory review. It is a mandatory, cross-functional process that ensures that all promotional, educational and informational materials are:

  • accurate and balanced
  • scientifically substantiated
  • compliant with global and local regulations

In highly regulated industries like pharmaceuticals and MedTech, MLR approval is a critical checkpoint before any HCP-facing or public content goes live. Failure to follow MLR requirements can result in:

  • Regulatory penalties
  • Product delistings
  • Legal liabilities
  • Damaged reputation

As content volume increases with the rise of omnichannel and AI-generated materials traditional MLR workflows can become a bottleneck unless modernized.

How can AI improve MLR workflows?

AI supports MLR processes in three major ways:

1. Content Pre-Screening

AI tools can scan drafts to:

  • Flag non-compliant language
  • Identify missing references or claims
  • Highlight risky phrasing (e.g., off-label implications)

This allows compliance teams to focus human review where it’s most needed, accelerating the overall review cycle.

2. Co-Pilots for MLR Teams

AI-powered assistants can:

  • Suggest alternative compliant language
  • Autofill references from approved claim libraries
  • Surface similar approved content as templates

These tools significantly reduce manual effort, especially during content revisions.

3. Approval of Modular Content

The modular content model breaks content into pre-approved blocks. AI can then:

  • Assemble high-volume personalized content on demand
  • Guarantee compliance by combining only validated modules
  • Eliminate hallucination risk (a common issue with generative AI)

This method not only accelerates content production but also reduces MLR review time by up to 60%.

How does AI integrate into the MLR approval chain?

Phase AI Role Benefit

PhaseAI RoleBenefit
Pre-MLRDrafting, tone adjustment, auto-translationFaster initial content creation
MLR ReviewPre-screening, red-flagging issues, checking against literature referencesPrioritizes human review efficiently
Post-MLR DeploymentMatching with HCP profiles, tagging usageTracks content reuse & optimization

The AI’s involvement must be transparent, traceable, and auditable, ensuring compliance with internal SOPs and external guidelines.

What are the risks and limitations of AI in MLR?

While AI accelerates MLR processes, there are important caveats:

  • Hallucinations: Generative AI may invent plausible-sounding but false claims. Limiting AI to assembly of approved blocks solves this.
  • Regulatory uncertainty: Global health authorities (e.g., FDA, EMA) have not yet fully defined how AI-assisted content creation fits within promotional regulations.
  • Tool validation: Any AI tool used must undergo proper validation, risk assessment, and documentation — especially in GxP environments.

Best practices for AI-driven MLR transformation

  • Separate AI use by stage
    • Use generative AI only before MLR to assist with content ideation or rephrasing.
    • Use only pre-approved content for AI assembly after MLR.
  •  Invest in modular content strategy
    • Pre-approve reusable content blocks to scale faster without repeated review cycles.
  •  Enable “compliance by design”
    • Integrate claim libraries, brand guardrails, and reference checkers directly into content tools.
  • Improve the supply chain, not just speed
    • Faster content creation is meaningless without scalable MLR capacity.
  •  Partner with experienced vendors
    • Choose platforms and agencies that specialize in life sciences compliance and can demonstrate audit readiness.

Summary

Modern MLR is no longer just a gatekeeper. MLR process is a strategic enabler of fast, scalable, and compliant pharma marketing. When powered responsibly by AI, it can cut approval cycles from weeks to days, support omnichannel campaigns, and free up expert reviewers for higher-value tasks.

The future of compliant content in life sciences is modular, AI-assisted, and human-supervised — and it’s already underway.

This text has been enganced with GenAI tools.

Categories
MedTech Pharma Marketing

Top Use Cases of AI in Pharma & MedTech

What are the main forms of generative AI being leveraged in pharma marketing?

Generative AI can be categorized into three general uses:

  • Content Generators: These produce texts, images, and video rapidly, and can also refine content by changing tone, simplifying information, or fine-tuning messages for specific audience segments.
  • Answer Engines: These are new forms of ‘search’ that generate direct answers (text, images, video) rather than just providing links. They can be used to extract insights from market research reports or summarize clinical studies.
  • Agents (Large Action Models – LAMs): These forms of AI act to achieve specific goals without specific instructions or human oversight. For example, an AI agent could review keynote Q&As and social media reactions to assess the medical community’s response to a drug launch. These are considered the most controversial due to their autonomous nature.

What are the primary use cases for AI in pharma and MedTech marketing?

There are four broad categories of AI use cases in pharma and MedTech marketing:

  1. Strategic Insights Generation: AI can analyze vast amounts of both structured (e.g., CRM data) and unstructured data (e.g., government policy documents, articles on HCP preferences) to generate actionable insights and trends. This can improve understanding by up to 30%. However, caution is advised as AI can produce plausible but incorrect results (“AI hallucination”).
  2. Marketing Content Generation: AI is crucial for omnichannel strategies that require high content volume for personalization. It can rapidly generate various content formats (texts, images, video, audio) and reduce content creation costs by 30-50%.
  3. MLR Acceleration (Medical, Legal, Regulatory Review): AI can modernize labor-intensive MLR processes, which typically take 21 to 56 days for approval. AI tools can pre-screen materials, flagging problematic content for detailed review and fast-tracking compliant assets. AI co-pilots can also assist MLR staff with referencing claims and rephrasing options, potentially accelerating content approvals two to three times.
  4. Field Force Enablement: AI benefits customer-facing staff like sales teams and Medical Science Liaisons (MSLs) by improving training, account planning, and content delivery. AI can provide immediate access to drug information, offer coaching for specific situations, and integrate with CRM to help reps prioritize accounts and personalize HCP engagements based on “next best” message or format recommendations.
Categories
Pharma Marketing

Understanding AI in Pharma Marketing

What is Artificial Intelligence (AI) in a pharma commercial context?

In a pharma commercial context, AI primarily refers to generative AI, which differs from traditional “classical AI” or machine learning.

  • Classical AI / Machine Learning models perform specific tasks, like analyzing data for predictions or identifying patient groups, using very specific datasets.
  • Generative AI works with a foundational, large language model (LLM) trained on vast amounts of data, enabling it to be applied to many different problems. Its key characteristics include:
    •   Basic competences applicable to various problems.
    •   Ability to learn from structured and unstructured data.
    •   Generation of multiple outputs such as texts, images, speech, video, and designs.
    •     An intuitive, conversational user interface.

In pharma, generative AI is used for activities like writing marketing materials, generating webinars, building image and video libraries, spotting segmentation opportunities, and speeding up marketing processes. It is projected to grow faster in healthcare than any other industry, potentially generating between $60 billion and $110 billion a year in economic value for the pharma and MedTech industries, with most value going to commercial operations.

Categories
Digital Health

Artificial Intelligence and Machine Learning (AI/ML) in Medical Devices

Do you know that FDA already approved 521 Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices?  

PAPNET. The failure of the pioneering AI/ML-enabled test.

Five hundred apps may not surprise you in January 2023, given the noise around Open AI and its GPTChat. However, the first such device, PAPNET Testing System, was approved over 28 years ago. In 1995, the year of Johnny Mnemonic and Ghost in the Shell movies!

Fig.1 Neural net-based (PAPNET, Neuromedical Systems, Suffern, NY) display of squamous cells (Papanicolaou stain) from a balloon smear showing effects of radiotherapy. Marked cell enlargement and vacuolization of cytoplasm are easily recognized.
Source: Koss, Leopold & Morgenstern, Nora & Tahir-Kheli, Naveed & Suhrland, Mark & Schreiber, Katie & Greenebaum, Ellen. (1998). Evaluation of Esophageal Cytology Using a Neural Net–Based Interactive Scanning System (the PAPNET System): Its Possible Role in Screening for Esophageal and Gastric Carcinoma. American journal of clinical pathology. 109. 549-57. 10.1093/ajcp/109.5.549.

PAPNET was using a neural network to analyze and interpret cytology from Pap smears. While this early system generated a lot of interest and Google Scholar lists 217 peer-reviewed articles on PAPNET results, the business side of it was not that great. The cost-effectiveness of the system in comparison to manual screening by cytotechnician was not there. Neuromedical Systems Inc, the company behind PAPNET went bust in 1999, and now its intellectual property is a part of Becton, Dickinson and Company portfolio.

List of FDA-approved Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices

If you look at the list of approved AI/ML-enabled medical devices, you will notice that the vast majority (392 medical devices, 75% of the whole) are for Radiology. Cardiovascular (57,11%), Hematology (15, 3%), and Neurology (14, 3%) are the remaining three significant categories.

Fig. 2: Split of approved AI/ML-enabled medical devices by Specialty Panel.
Source: FDA.gov, graphics by disrupting.healthcare

There are only 15 companies that have more than five AI/ML-enabled medical devices approved. Five of those companies are actually subsidiaries of GE, which in total owns 42 AI/ML-enabled medical devices. Then there is Siemens with 27 devices, Canon with 15, Aidoc Medical with 13, and Zebra Medical Vision with 9 devices. Philips, which also submitted its devices via different subsidiaries has in total 10 approved AI-enabled devices

Table 1. Companies with over 5 approved AI/ML-enabled medical devices
CompanyAI/ML-enabled medical devices
GE Medical Systems42
Siemens Healthineers27
Canon17
Aidoc Medical13
Philips Healthcare10
Zebra Medical Vision9
Quantib BV6
Arterys Inc.5
Clarius Mobile Health Corp.5
HeartFlow, Inc.5
RaySearch Laboratories AB5
Viz.ai, Inc.5
Source: FDA.gov, disrupting.healthcare

The future of AI-enabled and data-driven MedTech

The market for Artificial Intelligence / Machine Learning – enabled Medical Devices seems to be poised for growth. At a recent HLTH 2022 conference in Las Vegas, Michelle Wu, CEO of NyquistData, a company offering an AI-supported intelligence platform dedicated to MedTech companies discusses the advantages of using AI to unlock the potential of unstructured data from medical devices.

Leveraging Artificial Intelligence, Data to Improve Medical Devices
Source: Xtelligent Healthcare Media

Cleerly. An example of an AI-enabled medical device for a heart-attack-free future.

A good example of upcoming Artificial Intelligence / Machine Learning – enabled Medical Device may be Cleerly. The startup has raised $279 million from investors including Fidelity, T. Rowe Price, Novartis and Peter Thiel.

Founded by cardiologist James Min, former professor at Weill Cornell Medical College and director of the Dalio Institute of Cardiovascular Imaging at New York-Presbyterian, Cleerly uses AI to improve diagnostics cutting down on the time it takes to flag patients at risk.

Its proprietary AI algorithms analyze CCTA images to generate a 3D model of patients’ coronary arteries, identify their lumen (the cavity or channel within a tube or tubular organ such as a blood vessel) and vessel walls, locate and quantify stenoses, as well as identify, quantify and categorize plaque.

Source: Cleerly
Categories
Digital Health

Deprexis DiGA approved! 11th Digital Therapeutics (DTx) reimbursed in Germany

Deprexis. A Digital Therapeutics (DTx) for depression
Deprexis. A Digital Therapeutics (DTx) for depression. Source: Gaia Group AG

Deprexis – Digital therapeutics (DTx) for depression has received DiGA fast-track approval for DTx prescription and reimbursement in Germany.

The innovative DiGA process allows for fast-track approval of digital therapeutics and is the first such program in the world. It was created by the 2019 Digital Healthcare Act and allows apps to be prescribed by doctors while costs will be reimbursed through German statutory health insurance. 

The federal regulator, BfArM, manages DiGA. To get through DiGA, there are certain conditions:

  • Safety and Suitability for Use confirmed by CE certification as a medical product in the lowest-risk classes
  • Data Protection Conformity to data protection legislation (EU-wide GDPR and German Federal Data Protection Act (BDSG)) 
  • Information security Assessment is based on the recommendations of the German Federal Office for Information Security (BSI) and specific parts of  IT-Grundschutz (ITbasic data protection) catalogs designated for healthcare apps. 
  • Interoperability Related to German central IT standards directory available via online platform vesta, managed by gematik
  • Availability of preliminary data on the health benefits provided. Data must show that patient-relevant endpoints, in particular morbidity, mortality, or quality of life, are positively influenced.

Check out the full guide for DiGA here.

Results of DTx DiGA assessment as of June 2022. Source: BfArM.

At the time of writing this, there were 59 applications for DiGA listing, 40 for provisional listing, and 19 for the final listing. So far BfArM has approved 11 applications and rejected one. 25 applications have been withdrawn. In theory, the full approval process should take three months.

Deprexis, the 11th DiGA-approved application is interesting on its own. The manufacturer of the app is GAIA Group, an offspring of Airbus which builds its products on a proprietary AI platform called brocaⓇ.

Deprexis, Digital Therapeutics (DTx) for depression. Source: GAIA Group AG


The focus here is clearly not on UX, but on medical benefits. Deprexis may not have the nicest UX, but is a proper DTx providing a three-month-long highly individualized Cognitive Behavioral Therapy support program for patients with depression. Application is able to perform a dialogue with the patient, learning from the input on the way. It contains 10 content modules and is available online via desktop and mobile app interface. 

Deprexis is backed by clinical data from at least nine studies, one of which had a sample of 3,800 patients, which does not sound much in pharmaceuticals, but it is a lot in DTx. While in Germany it just received reimbursement, in the US the price for treatment is $400 one-time payment, or $540 in three monthly installments of $135 each.

Categories
Digital Health

Big Data, AI and Coronavirus COVID-19 (NCov-2019)

Coronavirus COVID-19 (NCov-2019) has tested some of the digital health capabilities such as AI-based predicitive models and real-time big data visualization. As a positive side effect, it has also allowed public to learn about epidemiology via video games.

 

AI-based predictive models caught COVID-19 faster than us

According to the news reports, two AI-based and one human volunteer-based warning systems were first to alert humanity about the threat coming from Wuhan.

HealthMap Project Website

The first to react was the automated HealthMap system at Boston Children’s Hospital, which scans online news and social media reports for signals of spreading disease. Its warning was very quick and accurate (pneumonia cases in Wuhan) – raised at 11:12PM local time on December 30, but it did not assign significance high enough to the message.

The second report came from a human. Marjorie Pollack from the Program for Monitoring Emerging Diseases (ProMed)  has based on similar social media reports received about 4 hours before the HealthMap warning. ProMed team’s analysis was more detailed than the first warning from AI but came about half an hour later.

BlueDot Explorer (screenshot from the website)

The third and most publicized report came from another AI-based model called BlueDot. BlueDot first became aware of the pneumonia cases in Wuhan on December 31st, and in addition to notifying their clients and government stakeholders directly, they publicly released their findings in the Journal of Travel Medicine on January 14th. While it was not truly the fastest, it is worth hearing how Kamran Khan, a Canadian MD, and founder of BlueDot explains the process behind.

Big Data Visualization to track Coronavirus COVID-19 (NCov-2019)

Dashboards showing the number of infected people, geographical spread and trends of the disease are useful to HCPs but also journalists and the public. This use case, although unfortunate, shows how important it is to be able to see and not only read data.

coronavirus covid19 dashboard
Screenshot of the COVID-19 dashboard by Johns Hopkins CSSE

The first and most known dashboard came from the team at Johns Hopkins University. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It has been accompanied by an article in The Lancet.

Currently, there are multiple dashboards showing diverse aspects of the epidemic of COVID-19. The list of dashboards with working links can be found at ESRI website.

Coronavirus virally spreads a game for people want to know

Due to the virus business slows down. Except for Ndemic Creations, a studio that in 2012 developed Plague Inc. A game that simulates a viral epidemic.

Plague Inc. is a game, but it is based on science and realistically shows the spread of viral infections amongst the human population. In the game, the player is supposed to infect all humans before the cure is available. It is so successful in teaching about epidemiology, that it has been endorsed by the CDC. During the COVID-19 outbreak, it has reached the top of charts on the Apple Store. According to its developers, similar peaks in popularity have accompanied the Ebola outbreak in Africa in 2014-2016.