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Generative AI and Precision Medicine—The Future Is Not What It Used to Be

Generative AI is a new and rapidly emerging form of artificial intelligence that has the potential to revolutionize precision medicine by improving diagnosis,…



By Tom Lawry

Generative AI is a new and rapidly emerging form of artificial intelligence that has the potential to revolutionize precision medicine by improving diagnosis, treatment, and drug discovery. It’s comprised of Large Language Models and other intelligent systems that replicate a human’s ability to create text, images, music, video, computer code, and more.

So, naturally, when Damian Doherty, Editor-in-Chief of Inside Precision Medicine, approached me about writing an article on Generative AI, the first thing I did was ask the latest version of ChatGPT to provide a 2,800-word manuscript on the opportunities and issues of its application to precision medicine.

The content it generated was relevant, logically organized, and backed up with factual information. Sentence structures were precise and delivered in an easy-to-understand format. There was a formulaic beginning, middle, and end, with the correct provisos stated for being wrong.

The result was quite good, but in the end, it was a little too GPT-ish. There were many things my human brain wanted to know that it didn’t cover or guide me towards.

In some ways, this exercise mirrors the deeper discussions and explorations that are just getting underway to both understand our new and evolving AI capabilities and define a logical pathway to help clinicians and researchers make the practice of medicine more precise.

I’ve had the benefit of working with the application of AI in health and medicine for over a decade. Here are my very human thoughts on what should be considered as we approach this opportunity.

An AI taxonomy

Generative AI is a relatively new form of AI that has been released into the wild. As such, there are very few experts. This means that we are all early in the journey of understanding what it is and how we apply it to do good.

The chart below provides a simple taxonomy to help differentiate generative AI from other forms of Predictive Analytics.

While there is a great deal of hype over generative AI, there is a growing body of evidence on the things it can do well with humans in the loop:

  • Write clinical notes in standard formats such as SOAP (subjective, objective assessment and plan)
  • Assign medical codes such as CPT and ICD-10
  • Generate plausible and evidence-based hypotheses
  • Interpret complex laboratory results

Going forward generative AI will provide benefits in many areas including:

Drug Discovery and Development: Assistance in the discovery of new drugs and their development by predicting molecular structures, simulating drug interactions, and identifying potential drug candidates more quickly and accurately. AI can identify existing drugs that could be repurposed for new therapeutic uses, potentially speeding up the drug development process and reducing costs.

Personalized Treatment Plans: Analyze large-scale patient data, including genetic information, medical records, and imaging data, to guide physicians in the creation of personalized treatment plans tailored to an individual’s unique genetic makeup and health profile.

Disease Diagnosis: Assistance in the early and accurate diagnosis of diseases by analyzing medical images, genomic data, and clinical records, helping healthcare professionals make more informed decisions.

Medicine has been here before—change is hard

Since medicine came out of the shadows and into the light as a data-driven, scientific discipline we’ve always aspired to be better. The reality is that change is hard. It requires us to think and act differently.

When cholera was raging through London in the 1850’s Dr. John Snow was initially rebuffed when he challenged the medical establishment by gathering and presenting data demonstrating that the root cause of cholera was polluted water rather than the prevailing view that it was caused by bad air. From this came the early stages of epidemiology.

In the 1970’s the introduction of endoscopy into surgical practice was met with resistance in the surgical community which saw little use for “key-hole” surgery as the prevailing view and practice was that large problems required large incisions. Today, the laparoscopic revolution is seen as one of the biggest breakthroughs in contemporary medical history.

Generative AI and Large Language Models are part of medicine’s next frontier. They are already challenging current practices across the spectrum of research, clinical trials, medical and nursing school curricula, and the front-line practice of medicine. It’s not a matter of whether it will affect what you do but rather how and when.

With the right dialogue and guidance from a diverse set of stakeholders, we will create a path forward that leverages the benefits of our evolving creations to improve health and medical practices while ensuring that appropriate guardrails are put in place to monitor and guide its use.

It’s not about going slow. It’s about getting things right

In some ways, the challenge of generative AI today is less about increased AI capabilities and more about the velocity of change it is driving.

Generative AI came screaming into mainstream consciousness in the fall of 2022. ChatGPT, a generative AI product from OpenAI, racked up 100 million users in two months. In the history of humans, there has never been a product that has seen such rapid adoption. Shortly after ChatGPT reached this milestone the next version of GPT was released with greatly increased capabilities.

Time to 100 million users

From the practice of medicine to the development of new drugs, generative AI’s “speed of progress” is not following the normal path that economists refer to as linear growth. This is where something new is created that adds incremental value, which then creates a small gap between the time of its creation and when it starts being used. As adoption occurs there is another small gap between uptake and the time it takes for policymakers to develop necessary guard rails to both guide its use and safeguard users from risks. Linear growth is steady and predictable and what clinical and operational systems are set up to manage.

Generative AI is upending linear growth. It’s taking a different trajectory that economists call exponential growth. This is where something increases faster as it gets bigger. Most of our systems are not designed to accommodate this dramatic escalation in change. Exponential growth doesn’t last and eventually, the pace of change returns to linear growth. But when it’s happening it feels like the world is inside a tornado.

Linear Growth

Earlier this year, the European Parliament approved landmark rules for AI, known as the EU AI Act. While the regulation is far from becoming law, it aims to bring generative AI tools under greater restrictions. This includes generative AI developers being required to submit these systems for review before releasing them commercially.

The rapid change driven by generative AI has some calling for measures to slow or even suspend AI development to evaluate its impact on humans and society. A petition from the Future of Life Institute was put forward and signed by leaders including Elon Musk calling for a six-month moratorium on AI development.

Exponential GrowthWhile there is uncertainty in what we are creating and how it should be applied, it is unlikely that any mandates will slow the pace of AI innovation.

Instead of attempting to slow progress, let us expedite the education and dialogue among policymakers, medical and research leaders, and frontline practitioners to chart a course for progress in applying our new intelligent capabilities. These groups are also most relevant to ensuring that a necessary set of laws, regulations, and protocols are in place to safeguard those both providing and receiving health and medical services.

In keeping with this “gas and guardrails” approach to AI innovation there are two important areas to consider.

The creation of enforceable responsible AI principles

Let’s recognize and support the overall good that can come from AI innovation. At the same time, we must be mindful of how our ever-expanding AI capabilities can replicate and even amplify human biases and risks that work against the goal of improving the health and well-being of all citizens.

Prioritizing fairness and inclusion in AI systems is a socio-technical challenge. The speed of progress is spawning a new set of issues for governments and regulators. It’s also challenging us with new ethical considerations in the fields of medical and computer science. Ultimately the question is not only what AI can do, but rather, what AI should do.

While legislators and regulators work on finding common ground, health and medical organizations using AI today should have a defined set of Responsible AI principles in place to guide the development and use of intelligent solutions. Most often, these principles or guidelines are reviewed and approved at the highest level of leadership and incorporated into an organization’s overall approach to Data Governance.

A framework to manage decisions about decisions

Up until recently, all decisions in health and medicine were made by humans. As AI becomes pervasive in clinical processes it will increasingly make, aid, or impact thousands of granular decisions made each day.

Clinicians, researchers, and leaders must recognize how this impacts decision-making at all levels of an organization. Leaders will need to make a paradigm shift from “making decisions” to creating processes to guide making “decisions about decisions.”

One model gaining traction is put forward by Michael Ross, an executive fellow at London Business School, and James Taylor, author of Digital Decisioning: Using Decision Management to Deliver Business Impact from AI. The model creates a taxonomy well suited to the types of decisions made in providing health and medical services.  Categories include:

Human in the loop (HITL): A human is assisted by a machine. In this model, the human is making the decision, and the machine provides only decision support or partial automation of some decisions or parts of decisions. This is often referred to as intelligence amplification (IA).

Human in the loop for exceptions (HITLFE): Most decisions are automated in this model, and humans only handle exceptions. For the exceptions, the system requires some judgment or input from the human before it can make the decision, though it is unlikely to ask the human to make the whole decision. Humans also control the logic to determine which exceptions are flagged for review.

Human on the loop (HOTL): Here, the machine is assisted by a human. The machine makes the micro-decisions, but the human reviews the decision outcomes and can adjust rules and parameters for future decisions. In a more advanced setup, the machine also recommends parameters or rule changes approved by a human.

Human Out of the Loop (HOOTL): The machine is monitored by the human in this model. The machine makes every decision, and the human intervenes only by setting new constraints and objectives. Improvement is also an automated closed loop. Adjustments, based on feedback from humans, are automated.

Over time AI-driven leaders will create frameworks and new management models that optimize the use of AI in making decisions while supporting clinicians who will remain in control of improved clinical processes and workflows.

AI capabilities will continue to change. How it adds value won’t

In working with health and medical organizations I’m often surprised how many times I see significant investments being made in AI without leaders being able to explain how AI investments will actually drive value at scale.

Using AI to transform anything requires a fundamental understanding of the capability differences between Artificial Intelligence (AI) and what I call Natural Human Intelligence (NHI). Once understood, this becomes your superpower. You now have the ability to pair the unique characteristics of each to drive measurable change.

AI allows us to leverage massive quantities of data and information to look for patterns that humans simply don’t have the ability or time to see. AI is good at finding things we care about, such as identifying patients at high risk of readmissions, falls, or unexpected deterioration. Generative AI is increasingly showing it can generate plausible and evidence-based hypotheses, and automate many activities such as clinical notes or assigning diagnostic codes.

By contrast, humans have the unique ability to draw on a set of skills and characteristics that no form of AI can mimic or replicate. As “smart” as AI is becoming, no one has figured out how to imbue machines with qualities that are essential in the world of health and medicine like wisdom, reasoning, judgement, imagination, critical thinking, experience, abstract concepts and common sense. Such attributes remain as uniquely human characteristics that are essential to making decisions in the provision of health and medical services.

The key to AI driving value comes when it is planned and applied in ways that augment the experiences and skills of clinicians, researchers and knowledge works to help them be better at what they do.

AI in medicine is not about technology. It’s about empowerment

AI has a PR problem. The narrative in the popular press and professional journals is often negative. Headlines like “Half of U.S. Jobs Could be Eliminate With AI,” paint a picture of a future work world dominated by what novelist Arthur C. Clarke calls robo-sapiens.

It’s no wonder that people are worried. According to a study by the American Psychological Association, the potential impacts that AI could have on the workplace and jobs is now one of the top issues impacting the mental health of workers.

Generative AI is already impacting today’s workplace and will be the single greatest change affecting the future of work in the next decade. It will impact how all work is done. As you let that statement sink in, recognize that the issues to be addressed go beyond productivity. After all, work brings shape and meaning to our lives and is not just about a job or income.

In this regard, there is growing evidence to suggest that AI can increase not only productivity but also job satisfaction.

In a randomized trial using generative AI, 453 college-educated professionals were given a series of writing tasks to complete. Half were given support with ChatGPT. The control group was not given access to ChatGPT. The results showed that the time taken to complete tasks was reduced by 40% among those using this form of generative AI. Beyond increased productivity, those using ChatGPT reported an increase in job satisfaction and a greater sense of optimism. Most importantly, inequality between workers decreased.

Done right, AI is not about technology. It’s about empowerment. Properly curated, generative AI will help solve one of the most significant challenges facing healthcare – The shortage of human capital. Here are two immediate opportunities in medicine today.

Keyboard liberation

Many physicians spend more time doing administrative work than they do with patients. One-third of doctors spend 20 or more hours per week doing paperwork. The electronic health record (EHR) creates a virtual 24/7 work environment for physicians. The impact of such “desktop medicine” on their wellness is a challenge for clinicians and organizations alike.

The promise of AI reducing highly repetitive, low-value activities is what Eric Topol MD, Founder and Director of the Scripps Research Institute, and best-selling author of Deep Medicine, refers to as “keyboard liberation.” A study by McKinsey & Company suggests that using AI and intelligent solutions in healthcare could reduce lower-value, repetitive activities performed by clinicians by 36%.

Reducing cognitive burden

A newly minted physician in 1950 would go their entire career (fifty years) before medical knowledge doubled. Today it is doubling roughly every 72 days. Without the use of AI to tame the data explosion, growing cognitive burden impacts quality, patient safety and physician burnout.

Today, 50,000 data points are in the typical EHR system that physicians must sift through each day. Primary care physicians spend more than an hour each day processing notifications.

Value from the exponential growth of data will only be realized by humans harnessing the power of AI.

The effective introduction and use of generative AI in health and medicine enables both cost-cutting automation of routine work and value-adding augmentation of human capabilities. As it and other forms of AI become pervasive in health and medicine, a new intelligent health system will emerge. It will facilitate systems that improve health while delivering greater value. It will provide a more personalized experience for consumers and patients. It will liberate clinicians and restore them to be the caregivers they want to be rather than the data entry clerks we’re turning them into by forcing them to use systems and processes conceived decades ago.

And while generative AI is coming at us fast with much to understand in how we use it, it could not have come at a better time.



  1. Peter Lee, Carey Goldberg, Isaac Kohane, The AI Revolution in Medicine: GPT-4 and Beyond, Pearson Education, 2023
  2. Theodore H. Tulchinsky, MD MPH, John Snow, Cholera, the Broad Street Pump; Waterborne Diseases Then and Now, Case Studies in Public Health, March 30, 2018
  3. Endoscopic surgery: the history, the pioneers. Litynski GS.World J Surg. 1999 Aug;23(8):745-53. doi: 10.1007/s002689900576.PMID: 10415199
  4. Ryan Browne, EU lawmakers pass landmark artificial intelligence regulation, CNBC, June 14, 2023.
  5. Pause Giant AI Experiments: An Open Letter, Future of Life Institute, March 22, 2023.
  6. Michael Ross and James Taylor, Managing AI Decision-Making Tools, Harvard Business Review, November 10, 2021.
  7. Ibid
  9. Arthur C. Clark, Britannica.
  10. Worries about artificial intelligence, surveillance at work may be connected to poor mental health, American Psychological Association, September 7, 2023.
  11. Shakked Noy, Whitney Zhang, Experimental evidence on the productivity effects of generative artificial intelligence, Science, July 13, 2023.
  12. The Medscape Physician Compensation Report, Medscape, 2021.
  13. Michael Chui, Where Machines Could Replace Humans-And where They Can’t, McKinsey & Company, 2017.
  14. Brenda Corish, Medical knowledge doubles every few months; how can clinicians keep up?, Elsevier, April 23, 2018.
  15. Information Overload: Why Physicians Are Inundated With Data (And How to Manage It), Spok.
  16. Jacqueline LaPointe, Study Supports Alarm Fatigue Concern with Physician EHR Use, HER Intelligence, March 16, 2016.


Generative AI: Technology that creates content—Including text, images, video, and computer code—by identifying patterns in large quantities of training data, and then creating new, original material that has similar characteristics.

Large Language Models: A type of neural network that learns skills—including generating prose, conducting conversations, and writing computer code—by analyzing vast amounts of text from across the internet. That basic function is to predict the next word in a sequence, but these models have also surprised experts by learning new abilities.


The post Generative AI and Precision Medicine—The Future Is Not What It Used to Be appeared first on Inside Precision Medicine.

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Four burning questions about the future of the $16.5B Novo-Catalent deal

To build or to buy? That’s a classic question for pharma boardrooms, and Novo Nordisk is going with both.
Beyond spending billions of dollars to expand…



To build or to buy? That’s a classic question for pharma boardrooms, and Novo Nordisk is going with both.

Beyond spending billions of dollars to expand its own production capacity for its weight loss drugs, the Danish drugmaker said Monday it will pay $11 billion to acquire three manufacturing plants from Catalent. It’s part of a broader $16.5 billion deal with Novo Holdings, the investment arm of the pharma’s parent group, which agreed to acquire the contract manufacturer and take it private.

It’s a big deal for all parties, with potential ripple effects across the biotech ecosystem. Here’s a look at some of the most pressing questions to watch after Monday’s announcement.

Why did Novo do this?

Novo Holdings isn’t the most obvious buyer for Catalent, particularly after last year’s on-and-off M&A interest from the serial acquirer Danaher. But the deal could benefit both Novo Holdings and Novo Nordisk.

Novo Nordisk’s biggest challenge has been simply making enough of the weight loss drug Wegovy and diabetes therapy Ozempic. On last week’s earnings call, Novo Nordisk CEO Lars Fruergaard Jørgensen said the company isn’t constrained by capital in its efforts to boost manufacturing. Rather, the main challenge is the limited amount of capabilities out there, he said.

“Most pharmaceutical companies in the world would be shopping among the same manufacturers,” he said. “There’s not an unlimited amount of machinery and people to build it.”

While Novo was already one of Catalent’s major customers, the manufacturer has been hamstrung by its own balance sheet. With roughly $5 billion in debt on its books, it’s had to juggle paying down debt with sufficiently investing in its facilities. That’s been particularly challenging in keeping pace with soaring demand for GLP-1 drugs.

Novo, on the other hand, has the balance sheet to funnel as much money as needed into the plants in Italy, Belgium, and Indiana. It’s also struggled to make enough of its popular GLP-1 drugs to meet their soaring demand, with documented shortages of both Ozempic and Wegovy.

The impact won’t be immediate. The parties expect the deal to close near the end of 2024. Novo Nordisk said it expects the three new sites to “gradually increase Novo Nordisk’s filling capacity from 2026 and onwards.”

As for the rest of Catalent — nearly 50 other sites employing thousands of workers — Novo Holdings will take control. The group previously acquired Altasciences in 2021 and Ritedose in 2022, so the Catalent deal builds on a core investing interest in biopharma services, Novo Holdings CEO Kasim Kutay told Endpoints News.

Kasim Kutay

When asked about possible site closures or layoffs, Kutay said the team hasn’t thought about that.

“That’s not our track record. Our track record is to invest in quality businesses and help them grow,” he said. “There’s always stuff to do with any asset you own, but we haven’t bought this company to do some of the stuff you’re talking about.”

What does it mean for Catalent’s customers? 

Until the deal closes, Catalent will operate as a standalone business. After it closes, Novo Nordisk said it will honor its customer obligations at the three sites, a spokesperson said. But they didn’t answer a question about what happens when those contracts expire.

The wrinkle is the long-term future of the three plants that Novo Nordisk is paying for. Those sites don’t exclusively pump out Wegovy, but that could be the logical long-term aim for the Danish drugmaker.

The ideal scenario is that pricing and timelines remain the same for customers, said Nicole Paulk, CEO of the gene therapy startup Siren Biotechnology.

Nicole Paulk

“The name of the group that you’re going to send your check to is now going to be Novo Holdings instead of Catalent, but otherwise everything remains the same,” Paulk told Endpoints. “That’s the best-case scenario.”

In a worst case, Paulk said she feared the new owners could wind up closing sites or laying off Catalent groups. That could create some uncertainty for customers looking for a long-term manufacturing partner.

Are shareholders and regulators happy? 

The pandemic was a wild ride for Catalent’s stock, with shares surging from about $40 to $140 and then crashing back to earth. The $63.50 share price for the takeover is a happy ending depending on the investor.

On that point, the investing giant Elliott Investment Management is satisfied. Marc Steinberg, a partner at Elliott, called the agreement “an outstanding outcome” that “clearly maximizes value for Catalent stockholders” in a statement.

Elliott helped kick off a strategic review last August that culminated in the sale agreement. Compared to Catalent’s stock price before that review started, the deal pays a nearly 40% premium.

Alessandro Maselli

But this is hardly a victory lap for CEO Alessandro Maselli, who took over in July 2022 when Catalent’s stock price was north of $100. Novo’s takeover is a tacit acknowledgment that Maselli could never fully right the ship, as operational problems plagued the company throughout 2023 while it was limited by its debt.

Additional regulatory filings in the next few weeks could give insight into just how competitive the sale process was. William Blair analysts said they don’t expect a competing bidder “given the organic investments already being pursued at other leading CDMOs and the breadth and scale of Catalent’s operations.”

The Blair analysts also noted the companies likely “expect to spend some time educating relevant government agencies” about the deal, given the lengthy closing timeline. Given Novo Nordisk’s ascent — it’s now one of Europe’s most valuable companies — paired with the limited number of large contract manufacturers, antitrust regulators could be interested in taking a close look.

Are Catalent’s problems finally a thing of the past?

Catalent ran into a mix of financial and operational problems over the past year that played no small part in attracting the interest of an activist like Elliott.

Now with a deal in place, how quickly can Novo rectify those problems? Some of the challenges were driven by the demands of being a publicly traded company, like failing to meet investors’ revenue expectations or even filing earnings reports on time.

But Catalent also struggled with its business at times, with a range of manufacturing delays, inspection reports and occasionally writing down acquisitions that didn’t pan out. Novo’s deep pockets will go a long way to a turnaround, but only the future will tell if all these issues are fixed.

Kutay said his team is excited by the opportunity and was satisfied with the due diligence it did on the company.

“We believe we’re buying a strong company with a good management team and good prospects,” Kutay said. “If that wasn’t the case, I don’t think we’d be here.”

Amber Tong and Reynald Castañeda contributed reporting.

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Petrina Kamya, Ph.D., Head of AI Platforms at Insilico Medicine, presents at BIO CEO & Investor Conference

Petrina Kamya, PhD, Head of AI Platforms and President of Insilico Medicine Canada, will present at the BIO CEO & Investor Conference happening Feb….



Petrina Kamya, PhD, Head of AI Platforms and President of Insilico Medicine Canada, will present at the BIO CEO & Investor Conference happening Feb. 26-27 at the New York Marriott Marquis in New York City. Dr. Kamya will speak as part of the panel “AI within Biopharma: Separating Value from Hype,” on Feb. 27, 1pm ET along with Michael Nally, CEO of Generate: Biomedicines and Liz Schwarzbach, PhD, CBO of BigHat Biosciences.

Credit: Insilico Medicine

Petrina Kamya, PhD, Head of AI Platforms and President of Insilico Medicine Canada, will present at the BIO CEO & Investor Conference happening Feb. 26-27 at the New York Marriott Marquis in New York City. Dr. Kamya will speak as part of the panel “AI within Biopharma: Separating Value from Hype,” on Feb. 27, 1pm ET along with Michael Nally, CEO of Generate: Biomedicines and Liz Schwarzbach, PhD, CBO of BigHat Biosciences.

The session will look at how the latest artificial intelligence (AI) tools – including generative AI and large language models – are currently being used to advance the discovery and design of new drugs, and which technologies are still in development. 

The BIO CEO & Investor Conference brings together over 1,000 attendees and more than 700 companies across industry and institutional investment to discuss the future investment landscape of biotechnology. Sessions focus on topics such as therapeutic advancements, market outlook, and policy priorities.

Insilico Medicine is a leading, clinical stage AI-driven drug discovery company that has raised over $400m in investments since it was founded in 2014. Dr. Kamya leads the development of the Company’s end-to-end generative AI platform, Pharma.AI from Insilico’s AI R&D Center in Montreal. Using modern machine learning techniques in the context of chemistry and biology, the platform has driven the discovery and design of 30+ new therapies, with five in clinical stages – for cancer, fibrosis, inflammatory bowel disease (IBD), and COVID-19. The Company’s lead drug, for the chronic, rare lung condition idiopathic pulmonary fibrosis, is the first AI-designed drug for an AI-discovered target to reach Phase II clinical trials with patients. Nine of the top 20 pharmaceutical companies have used Insilico’s AI platform to advance their programs, and the Company has a number of major strategic licensing deals around its AI-designed therapeutic assets, including with Sanofi, Exelixis and Menarini. 


About Insilico Medicine

Insilico Medicine, a global clinical stage biotechnology company powered by generative AI, is connecting biology, chemistry, and clinical trials analysis using next-generation AI systems. The company has developed AI platforms that utilize deep generative models, reinforcement learning, transformers, and other modern machine learning techniques for novel target discovery and the generation of novel molecular structures with desired properties. Insilico Medicine is developing breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, immunity, central nervous system diseases, infectious diseases, autoimmune diseases, and aging-related diseases. 

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Another country is getting ready to launch a visa for digital nomads

Early reports are saying Japan will soon have a digital nomad visa for high-earning foreigners.



Over the last decade, the explosion of remote work that came as a result of improved technology and the pandemic has allowed an increasing number of people to become digital nomads. 

When looked at more broadly as anyone not required to come into a fixed office but instead moves between different locations such as the home and the coffee shop, the latest estimate shows that there were more than 35 million such workers in the world by the end of 2023 while over half of those come from the United States.

Related: There is a new list of cities that are best for digital nomads

While remote work has also allowed many to move to cheaper places and travel around the world while still bringing in income, working outside of one's home country requires either dual citizenship or work authorization — the global shift toward remote work has pushed many countries to launch specific digital nomad visas to boost their economies and bring in new residents.

Japan is a very popular destination for U.S. tourists. 


This popular vacation destination will soon have a nomad visa

Spain, Portugal, Indonesia, Malaysia, Costa Rica, Brazil, Latvia and Malta are some of the countries currently offering specific visas for foreigners who want to live there while bringing in income from abroad.

More Travel:

With the exception of a few, Asian countries generally have stricter immigration laws and were much slower to launch these types of visas that some of the countries with weaker economies had as far back as 2015. As first reported by the Japan Times, the country's Immigration Services Agency ended up making the leap toward a visa for those who can earn more than ¥10 million ($68,300 USD) with income from another country.

The Japanese government has not yet worked out the specifics of how long the visa will be valid for or how much it will cost — public comment on the proposal is being accepted throughout next week. 

That said, early reports say the visa will be shorter than the typical digital nomad option that allows foreigners to live in a country for several years. The visa will reportedly be valid for six months or slightly longer but still no more than a year — along with the ability to work, this allows some to stay beyond the 90-day tourist period typically afforded to those from countries with visa-free agreements.

'Not be given a residence card of residence certificate'

While one will be able to reapply for the visa after the time runs out, this can only be done by exiting the country and being away for six months before coming back again — becoming a permanent resident on the pathway to citizenship is an entirely different process with much more strict requirements.

"Those living in Japan with the digital nomad visa will not be given a residence card or a residence certificate, which provide access to certain government benefits," reports the news outlet. "The visa cannot be renewed and must be reapplied for, with this only possible six months after leaving the countr

The visa will reportedly start in March and also allow holders to bring their spouses and families with them. To start using the visa, holders will also need to purchase private health insurance from their home country while taxes on any money one earns will also need to be paid through one's home country.

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