#Computed Radiography

[arjun070]

The Future of Medical Imaging: Exploring the Digital X-Ray Market Landscape

United States of America – July 9, 2025 – The Insight Partners has released its latest market report, “Digital X-Ray Market: An In-depth Analysis of Global Developments and Growth Opportunities.” This report provides a comprehensive overview of the digital X-ray industry, highlighting key dynamics, current market trends, and future prospects through 2031.

Overview of Digital X-Ray Market

The Digital X-ray market is evolving rapidly as healthcare providers continue shifting toward more efficient and accurate diagnostic tools. Digital radiography has emerged as a preferred solution over traditional analog systems due to its superior imaging quality, lower radiation exposure, and faster processing times. This transformation is supported by ongoing technological advancements, increased adoption of portable diagnostic tools, and a greater focus on patient-centric care.

Key Findings and Insights

Market Growth Trends

The global digital X-ray market is projected to grow steadily during the forecast period, driven by a compound annual growth rate (CAGR) of 3.9% from 2025 to 2031. This growth is fueled by increasing demand for advanced diagnostic imaging, rising healthcare investments, and the expansion of radiology services across both developed and emerging regions.

Factors Influencing Market Growth

Technological Advancements: Continuous innovation in direct radiography and computed radiography systems has significantly improved image resolution and reduced diagnostic errors.

Portable Diagnostic Demand: The rise in demand for portable digital X-ray systems, especially in emergency care and remote healthcare settings, is a critical growth driver.

Aging Population: A growing geriatric population and increasing incidence of chronic diseases requiring regular diagnostic imaging also contribute to market expansion.

Market Segmentation

The report segments the digital X-ray market based on system, application, technology, and modality:

By System

Retrofit Digital X-ray Systems

New Digital X-ray Systems

By Application

General Radiography

Mammography

Fluoroscopy

Dental Applications

By Technology

Direct Radiography

Computed Radiography

By Modality

Fixed Digital X-ray Systems

Portable Digital X-ray Systems

Each segment offers unique insights into consumer adoption trends and technological demands across healthcare settings.

Emerging Trends and Opportunities

Increased Adoption of AI: Integration of artificial intelligence with digital X-ray systems is revolutionizing image analysis, offering faster and more accurate diagnostics.

Shift to Value-Based Healthcare: Emphasis on early disease detection and outcome-based treatment plans is increasing the reliance on digital imaging.

Remote Diagnostics: Demand for portable and mobile digital X-ray solutions is growing in underserved areas and field hospitals, presenting new market opportunities.

Conclusion

The digital X-ray Market: Global Industry Trends, Share, Size, Growth, Opportunity, and Forecast 2023-2031 report provides much-needed insight for a company willing to set up its operations in the digital X-ray Market. Since an in-depth analysis of competitive dynamics, the environment, and probable growth path are given in the report, a stakeholder can move ahead with fact-based decision-making in favor of market achievements and enhancement of business opportunities.

About The Insight Partners

The Insight Partners is among the leading market research and consulting firms in the world. We take pride in delivering exclusive reports along with sophisticated strategic and tactical insights into the industry. Reports are generated through a combination of primary and secondary research, solely aimed at giving our clientele a knowledge-based insight into the market and domain. This is done to assist clients in making wiser business decisions. A holistic perspective in every study undertaken forms an integral part of our research methodology and makes the report unique and reliable.

To know more and get access to Sample reports. https://www.theinsightpartners.com/sample/TIPRE00003075

[astartesting01]

Beyond Film: Why Computed Radiography is the Future of NDT

Computed Radiography is revolutionizing non-destructive testing (NDT) by offering faster, safer, and more accurate radiographic inspection compared to traditional film methods. With portable CR systems, technicians can perform high-resolution industrial radiography even in remote or challenging environments. Its digital capabilities streamline workflows, reduce exposure time, and enhance defect detection—making Computed Radiography the preferred choice for modern NDT operations across industries.

[vincivilworld]

Radiography Test: Key Techniques and Benefits Explained

Radiography test is a non-destructive testing (NDT) method. It uses X-rays or gamma rays to examine the internal structure of materials. This technique is essential for detecting hidden flaws without causing damage, ensuring the integrity and safety of components. Radiography test is widely applied in industries such as manufacturing, construction, and aerospace to inspect welding, castings, and…

[strjackst]

Industrial Computed Radiography Market Report, Market Size, Share, Trends, Analysis By Forecast Period

[mohitbisresearch]

The global industrial computed radiography market is estimated to reach $68.8 million in 2033 from $59.1 million in 2022, at a growth rate of 1.49% during the forecast period 2023-2033.

[ashimbisresearch]

Advancing Non-Destructive Testing: The Asia-Pacific Industrial Computed Radiography Market | BIS Research

Non-Destructive Testing (NDT) plays a crucial role in ensuring the safety, quality, and reliability of industrial infrastructure across various sectors. Computed Radiography (CR) has emerged as a powerful NDT technique, offering superior image quality, enhanced efficiency, and cost-effectiveness. The Asia-Pacific region is witnessing significant growth in the Industrial Computed Radiography Market, as industries increasingly adopt this advanced imaging technology.

According to BIS Research, the Asia-Pacific Industrial Computed Radiography Market is estimated to reach $19.30 million by 2033 at a growth rate of CAGR 3.49% during the forecast period 2023-2033.

Key Prominent Market Growth Drivers

Stringent Regulatory Standards and Safety Requirements:

Asia-Pacific industries like manufacturing, oil and gas, aerospace, and power generation face strict safety regulations.

Industrial computed radiography ensures compliance by accurately inspecting critical components for defects and anomalies.

Its precision in detecting flaws contributes to enhanced asset integrity and risk reduction in these regulated sectors.

Cost-Effectiveness and Efficiency:

Industrial computed radiography offers substantial cost and time savings compared to traditional film-based methods.

Eliminating film processing chemicals and reducing image development time, CR enhances operational efficiency.

Rapid image acquisition, storage, and sharing streamline inspection processes, minimizing downtime and boosting productivity.

Technological Advancements and Image Quality:

Ongoing advancements in digital imaging sensors and software have significantly enhanced CR image quality.

High-definition images provide clearer visibility of defects, enabling accurate assessments by inspectors.

Digital manipulation capabilities improve diagnostic accuracy, empowering inspectors to make informed decisions.

Wide Range of Applications:

Industrial computed radiography serves diverse industries, including weldments, castings, pipelines, and turbines.

Its versatility allows efficient inspection of complex geometries and hard-to-reach areas.

CR is invaluable for inspecting structural components across various sectors, contributing to overall quality assurance.

Access More: Get FREE Detailed Report on Asia-Pacific Industrial Computed Radiography Research!

Key Market Trends and Opportunities

Growing Adoption in Developing Economies:

Rapid industrialization and infrastructure development in Asia-Pacific's developing economies drive adoption.

Focus on quality control and safety standards prompts the use of industrial computed radiography.

Benefits include higher inspection accuracy, cost reduction, and enhanced asset reliability.

Shift from Analog to Digital NDT:

Asia-Pacific sees a transition from traditional analog NDT to digital methods like computed radiography.

Advantages such as improved image quality and streamlined data analysis drive this shift.

Digital NDT presents growth opportunities for industrial computed radiography in the region.

Integration with Industry 4.0 and Automation:

Industrial computed radiography integrates with Industry 4.0 tech like IoT, AI, and robotics.

Automated CR systems with AI-enabled analysis enhance inspection speed, accuracy, and repeatability.

This convergence enables real-time monitoring, predictive maintenance, and data-driven decisions in the Asia-Pacific market.

APAC Industrial Computed Radiography Market Segmentation by Application

Aerospace and Defense

Automotive

Oil and Gas

Power and Energy

Security

Explosive Ordnance Disposal and Improvised Explosive Device

Electronics and Semiconductors

Food and Drugs

Transportation Infrastructure

Construction

Marine

Manufacturing

Heavy Industries

Others

Market Challenges and Future Outlook

While the Asia-Pacific Industrial Computed Radiography Industry shows promising growth prospects, there are challenges to address. These include the need for skilled personnel to operate and interpret CR systems, ensuring compliance with regulatory standards, and addressing concerns related to radiation safety. Overcoming these challenges through training programs, standardization efforts, and continuous technological advancements will be crucial for the widespread adoption of industrial computed radiography in the region.

Conclusion

The APAC Industrial Computed Radiography Market is experiencing significant growth, driven by the demand for accurate, efficient, and cost-effective non-destructive testing solutions. As industries across the region embrace digital transformation and prioritize safety and quality control, the adoption of industrial computed radiography is set to rise. With ongoing technological advancements, increasing automation, and the integration of digital NDT techniques, the future of industrial computed radiography in the Asia-Pacific region looks promising, contributing to safer and more reliable industrial infrastructure.

[oz-oz]

Tomografías y coffe

[techndt]

Computed Radiography & Digital Radiography are NDT methods that play pivotal roles in contemporary radiological imaging, reshaping medical diagnostics landscape.

[covid-safer-hotties]

Also preserved in our archive

By Dr. Chinta Sidharthan

Unvaccinated volunteers who contracted COVID-19 in a human challenge study showed significant memory and executive function decline lasting up to a year, despite no reported subjective symptoms, prompting new questions about the virus’s long-term cognitive effects.

In a recent study published in the journal EClinicalMedicine, a team of researchers from the United Kingdom examined the cognitive deficits associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. They conducted the first human challenge study among a prospectively controlled group of unvaccinated SARS-CoV-2 naive volunteers, who were inoculated with the wild-type strain and observed for long-term cognitive problems.

Background Substantial research now indicates that long-lasting cognitive deficits impacting memory, comprehension, and concentration occur even after mild coronavirus disease 2019 (COVID-19) cases. A large proportion of individuals who recover from COVID-19 continue to experience “brain fog,” memory lapses, and difficulty forming words for months after the initial acute infection.

Cross-sectional and longitudinal studies have observed cognitive decline in patients one year after the infection, and brain scans have detected shrinkage in areas of the brain related to cognition and memory. Furthermore, blood tests in patients hospitalized due to SARS-CoV-2 infections have detected elevated levels of brain injury markers, such as neurofilament light (NfL) and glial fibrillary acidic protein (GFAP), indicative of potential future cognitive problems, though markers like Tau were not significantly different between infected and uninfected groups.

However, the retrospective nature of these studies has posed difficulties in accounting for the role of occupations, pre-existing health conditions, and social factors in the risk of cognitive deficits after COVID-19. Furthermore, the pace at which cognitive deficits develop after mild SARS-CoV-2 infections and the duration of these deficits remains unclear.

About the study In the present study, the researchers challenged a group of unvaccinated, SARS-CoV-2 naive volunteers with the wild-type strain of the virus in controlled conditions. The volunteers were then quarantined and followed up to determine the long-term cognitive impacts of COVID-19.

The researchers ensured that all the ethical guidelines were followed in this human challenge study, and written consent was obtained from all the volunteers, who were also compensated for the time spent in quarantine.

The study enrolled 36 healthy adults between 18 and 30 years who had never been vaccinated against or infected with SARS-CoV-2. Of these, 18 participants were classified as infected, while 16 were uninfected. The volunteers underwent extensive tests and screening, including blood tests, chest radiography, body mass index, and assessments for COVID-19 risk factors.

The participants were then intranasally inoculated with SARS-CoV-2 and quarantined for at least two weeks. The follow-ups occurred at non-regular intervals for up to a year after the inoculation.

The viral loads in all the infected participants were monitored twice a day through naso- and oropharyngeal swabs. Additionally, the researchers administered a subjective symptom survey thrice daily to track the symptoms. The participants were categorized based on whether they experienced a sustained viral infection, and six were administered remdesivir as a precaution.

The researchers measured the participants' cognitive performance through 11 computer-based tasks that measured various cognitive domains, such as reaction time, memory, spatial reasoning, and planning. The participants were required to perform these tasks at baseline, on each day of the quarantine, and at each of the five follow-ups. The primary cognitive measure was the baseline-corrected global cognitive composite score or bcGCCS.

Additionally, the researchers also analyzed the levels of brain injury markers, such as neurofilament light (NfL) and glial fibrillary acidic protein (GFAP), in the blood samples obtained from the participants.

Results The study found that bcGCCS scores indicated that the infected individuals exhibited significant cognitive deficits compared to the uninfected individuals. These deficits were sustained for almost a year, with no recovery or improvements noted. Despite these objective cognitive deficits, none of the infected volunteers reported subjective cognitive symptoms.

The cognitive area that showed the largest deficit was memory-related tasks, such as those measuring immediate and delayed memory recall. The infected individuals performed worse than the uninfected ones on memory-related and executive planning tasks.

The cognitive tasks were grouped based on whether learning effects were observed across sessions, and the results indicated that the cognitive differences between the uninfected and infected individuals were robust even after accounting for learning effects.

Furthermore, some brain injury biomarkers in the serum, such as GFAP, were higher in the infected participants than in the uninfected ones, but other markers, such as Tau and NfL, were not significantly different between the two groups.

Although these findings indicated that SARS-CoV-2 infections resulted in measurable differences in various aspects of cognitive decline, especially in the areas of memory and executive function, the statistical tests revealed no significant correlation between cognitive deficits and viral load, brain markers, and symptom severity.

Conclusions The study indicated that while objective and measurable changes could be observed in various aspects of cognitive performance due to SARS-CoV-2 infections, further research is essential to understand the biological mechanisms behind these cognitive deficits. The researchers believe that more long-term studies on larger cohorts are required to understand the long-term impact of COVID-19. Importantly, the study results suggest that these cognitive changes might persist even in the absence of subjective symptoms, highlighting the need for more sensitive assessment tools.

Journal reference: Trender, W., Hellyer, P. J., Killingley, B., Kalinova, M., Mann, A. J., Catchpole, A. P., Menon, D., Needham, E., Thwaites, R., Chiu, C., Scott, G., & Hampshire, A. (2024). Changes in memory and cognition during the SARS-CoV-2 human challenge study. EClinicalMedicine, 76. DOI:10.1016/j.eclinm.2024.102842, www.thelancet.com/journals/eclinm/article/PIIS2589-5370(24)00421-8/fulltext

[onioety]

Okay!!! I'm fucking around with searches on the thisisnotawebsitedotcom.com's computer. I'll keep this thing updated with everything I find!!

(Disclaimer: typing names several times can change the outcomes)

List below!!

-BILL: either jazz triangle YouTube video or a Wikipedia link to eye triangle dollar bill things.

-MCGUCKET: cottoned eye Joe video on YouTube.

-MABEL: stickers will get sticked in the lab until 'lab now fully mabelized' appears on the pc's screen.

-DIPPER: Note from Bill asking him to stare 13 hours at the sun to develope powers to see 'special sun ink'. If you keep clicking, more notes appear saying you're on the right path. Gradually, the notes will get black (you're blind)

-STAN: HERE ME OUT THIS ONE IS MARVELOUS. The first searches will lead you to eBay and different random objects related to Stan (8 ball, rings, compression vest, hat, bow...). If you keep going you'll be able to see this (I love Stanley he's my baby):

Here you can take a look at a TON of random Stan-related things. Special mention to one of Stan's fear being having very small fingertips. I'll never be over it.

-DIVORCE/BREAKUP: THE FUCKING O'SADLEYS SIGN. I'M.

-BOYFRIEND: the romance book.

No because maybe there's more to this... 'there's three sides to every story'. Yes, triangle, love triangle, it's a very good pun. But we had Ford's POV in Diaries 3 and Bill's in the book of Bill. Thinking thoughts.

-GIDEON: sweat resistant bolo ties search on Google.

-TRIANGLE: ')' and 'tri harder'

-ALEX: flannel search on google

-PORTAL: 'portal.exe has been deleted. I bet you could build one'

-GRAVITY FALLS: 'never heard of it'

-FORD/SIXER: 18th y.o Ford's hand radiography. Weird something written I cannot really tell?? H8T0? HBT0? HBTO? H8TO?

-WENDY: 👌

-SOOS: some notes written down by him. Claims that when looking at the book everything glitches and he just sees 'HE'S UNCORRUPTABLE'.

-PIÑATA: video of a girl punching a Bill-shaped piñata.

-ABUELITA: vacuum cleaner commercial spot on YouTube.

-Any insult: soap image, angry message. They do not approve us.

-LIES: okay I find this specially important because the book is very introspective and gives out a lot of info about Bill but we all know he's an unreliable narrator. He rants about post-truth, scientism and superstition (very cool and interesting philosophical topics if I may say). But it's specially interesting when it comes to understanding Bill and his mindset. Lie until what you want becomes true, which can't. Lie until you can't remember what's a lie and what isn't. Reality is doomed, if you can't physically escape, do so in your mind. Lie until you aren't lying anymore. Reality is a compendium, and ultimate construct, a truth of lies, a possibility among endless. Lies are truths

-GOD: video of an axolot swimming with a Bill figurine made out of rock.

-HEY NERD: advertisement

-EVEN HIS LIES ARE LIES:

SORRY: Old picture of Fiddleford and Stanford back in college :(

ONE EYED KING: fucking morse code. Took me some minutes to catch it: -./.-/../-/.../..-/.-/..-.

Resulting in: NAITSUAF. If you enter this word as a new code, you'll see a page with a contract and terms of service to sell your soul. It has a code that translates to: 'you're now twenty one grams lighter'.

[astartesting01]

A-STAR TESTING & INSPECTION (S) PTE LTD stands at the cutting edge of Non-Destructive Testing (NDT) with our advanced solutions and technologies. We specialize in Computed Radiography (CR), an innovative imaging technique that revolutionizes material and component inspection. Our CR technology ensures precise, high-quality images, enhancing the detection of defects and ensuring the integrity and safety of your assets. Trust us for reliable, state-of-the-art NDT services that set new standards in accuracy and efficiency.

[citedesdames]

MATHS & SCIENCES

STEM, architecture, astronomy, astrophysics, aviation, biology, computer science, economics, engineering, entomology, genetics, immunology, marine biology, maths, mechanics, medicine, nuclear physics, orbital mechanics, particle physics, physics, psychiatry, psychology, radiography, theoretical physics, virology, zoology

[alianoralacanta]

While a lot of this article is very good, the value proposition of radiography presented is incorrect. Professional radiographers will always be needed in part of the process, if only so that the accreditation boards are satisfied appropriate monitoring is in place and to help reassure patients who don't trust computers (a part of the population computer experts often forget to consider). The actual value proposition is to enable each each radiographer to do: - more accurate scans (something that is now the case for white British and USA people, albeit a long way off for everyone else), resulting in more efficient spending downstream (more accurate treatment, with more precision and more confidence). - more total scans (a radiography AI is intended to give results much faster than a human, and in some cases can help the radiographer see what exactly caused the scan to be classified as abnormal. Results on this are mixed). This means radiographers can get through more scans in a day, cutting waiting times and earning more money for the same wage base. In places where there simply aren't enough qualified radiographers to go round, that's a big advantage. - identify patterns within scans that researchers haven't spotted yet. This is both the potential biggest saving and the highest-risk proposition. While AI research is ongoing into this part, it's also the part which has so far revealed zero success. As for the type of bubble this is, I suspect AI is only going to partially explode, because large parts of AI are successfully implementing. You don't hear about how much AI is already integrated into areas like security, energy management and computer game NPC design because these have occurred largely successfully and without causing large media waves. The platform is large enough that some of the large numbers of people skilling in AI will definitely find work in AI after the bubble bursts, and it will be considerably more than before the bubble got going. There is one other promising legacy likely, which is the people who are trying to skill to get into AI generally need a solid base in at least one general programming language in order to make progress with the likes of Tensorflow or Pytorch (Python and C++ are the ones I see recommended most often as methods to learn enough programming for the AI stuff to make sense at a programming level). Python and C++ aren't going away, the number of uses for these that don't involve AI is growing every week, and even people who don't take a career in computing will benefit from the logical thinking that comes from learning a programming language due to something they are interested in (rather than as a rote lesson). A lot of AI is ridiculous. Some of it is worthwhile. We need to make sure the mess from the downfall is not too big, so that computing can build back from a solid base - and be aware AI will definitely be part of that base even if the AI grandees most heard in the press fall.

What kind of bubble is AI?

My latest column for Locus Magazine is "What Kind of Bubble is AI?" All economic bubbles are hugely destructive, but some of them leave behind wreckage that can be salvaged for useful purposes, while others leave nothing behind but ashes:

https://locusmag.com/2023/12/commentary-cory-doctorow-what-kind-of-bubble-is-ai/

Think about some 21st century bubbles. The dotcom bubble was a terrible tragedy, one that drained the coffers of pension funds and other institutional investors and wiped out retail investors who were gulled by Superbowl Ads. But there was a lot left behind after the dotcoms were wiped out: cheap servers, office furniture and space, but far more importantly, a generation of young people who'd been trained as web makers, leaving nontechnical degree programs to learn HTML, perl and python. This created a whole cohort of technologists from non-technical backgrounds, a first in technological history. Many of these people became the vanguard of a more inclusive and humane tech development movement, and they were able to make interesting and useful services and products in an environment where raw materials – compute, bandwidth, space and talent – were available at firesale prices.

Contrast this with the crypto bubble. It, too, destroyed the fortunes of institutional and individual investors through fraud and Superbowl Ads. It, too, lured in nontechnical people to learn esoteric disciplines at investor expense. But apart from a smattering of Rust programmers, the main residue of crypto is bad digital art and worse Austrian economics.

Or think of Worldcom vs Enron. Both bubbles were built on pure fraud, but Enron's fraud left nothing behind but a string of suspicious deaths. By contrast, Worldcom's fraud was a Big Store con that required laying a ton of fiber that is still in the ground to this day, and is being bought and used at pennies on the dollar.

AI is definitely a bubble. As I write in the column, if you fly into SFO and rent a car and drive north to San Francisco or south to Silicon Valley, every single billboard is advertising an "AI" startup, many of which are not even using anything that can be remotely characterized as AI. That's amazing, considering what a meaningless buzzword AI already is.

So which kind of bubble is AI? When it pops, will something useful be left behind, or will it go away altogether? To be sure, there's a legion of technologists who are learning Tensorflow and Pytorch. These nominally open source tools are bound, respectively, to Google and Facebook's AI environments:

https://pluralistic.net/2023/08/18/openwashing/#you-keep-using-that-word-i-do-not-think-it-means-what-you-think-it-means

But if those environments go away, those programming skills become a lot less useful. Live, large-scale Big Tech AI projects are shockingly expensive to run. Some of their costs are fixed – collecting, labeling and processing training data – but the running costs for each query are prodigious. There's a massive primary energy bill for the servers, a nearly as large energy bill for the chillers, and a titanic wage bill for the specialized technical staff involved.

Once investor subsidies dry up, will the real-world, non-hyperbolic applications for AI be enough to cover these running costs? AI applications can be plotted on a 2X2 grid whose axes are "value" (how much customers will pay for them) and "risk tolerance" (how perfect the product needs to be).

Charging teenaged D&D players $10 month for an image generator that creates epic illustrations of their characters fighting monsters is low value and very risk tolerant (teenagers aren't overly worried about six-fingered swordspeople with three pupils in each eye). Charging scammy spamfarms $500/month for a text generator that spits out dull, search-algorithm-pleasing narratives to appear over recipes is likewise low-value and highly risk tolerant (your customer doesn't care if the text is nonsense). Charging visually impaired people $100 month for an app that plays a text-to-speech description of anything they point their cameras at is low-value and moderately risk tolerant ("that's your blue shirt" when it's green is not a big deal, while "the street is safe to cross" when it's not is a much bigger one).

Morganstanley doesn't talk about the trillions the AI industry will be worth some day because of these applications. These are just spinoffs from the main event, a collection of extremely high-value applications. Think of self-driving cars or radiology bots that analyze chest x-rays and characterize masses as cancerous or noncancerous.

These are high value – but only if they are also risk-tolerant. The pitch for self-driving cars is "fire most drivers and replace them with 'humans in the loop' who intervene at critical junctures." That's the risk-tolerant version of self-driving cars, and it's a failure. More than $100b has been incinerated chasing self-driving cars, and cars are nowhere near driving themselves:

https://pluralistic.net/2022/10/09/herbies-revenge/#100-billion-here-100-billion-there-pretty-soon-youre-talking-real-money

Quite the reverse, in fact. Cruise was just forced to quit the field after one of their cars maimed a woman – a pedestrian who had not opted into being part of a high-risk AI experiment – and dragged her body 20 feet through the streets of San Francisco. Afterwards, it emerged that Cruise had replaced the single low-waged driver who would normally be paid to operate a taxi with 1.5 high-waged skilled technicians who remotely oversaw each of its vehicles:

https://www.nytimes.com/2023/11/03/technology/cruise-general-motors-self-driving-cars.html

The self-driving pitch isn't that your car will correct your own human errors (like an alarm that sounds when you activate your turn signal while someone is in your blind-spot). Self-driving isn't about using automation to augment human skill – it's about replacing humans. There's no business case for spending hundreds of billions on better safety systems for cars (there's a human case for it, though!). The only way the price-tag justifies itself is if paid drivers can be fired and replaced with software that costs less than their wages.

What about radiologists? Radiologists certainly make mistakes from time to time, and if there's a computer vision system that makes different mistakes than the sort that humans make, they could be a cheap way of generating second opinions that trigger re-examination by a human radiologist. But no AI investor thinks their return will come from selling hospitals that reduce the number of X-rays each radiologist processes every day, as a second-opinion-generating system would. Rather, the value of AI radiologists comes from firing most of your human radiologists and replacing them with software whose judgments are cursorily double-checked by a human whose "automation blindness" will turn them into an OK-button-mashing automaton:

https://pluralistic.net/2023/08/23/automation-blindness/#humans-in-the-loop

The profit-generating pitch for high-value AI applications lies in creating "reverse centaurs": humans who serve as appendages for automation that operates at a speed and scale that is unrelated to the capacity or needs of the worker:

https://pluralistic.net/2022/04/17/revenge-of-the-chickenized-reverse-centaurs/

But unless these high-value applications are intrinsically risk-tolerant, they are poor candidates for automation. Cruise was able to nonconsensually enlist the population of San Francisco in an experimental murderbot development program thanks to the vast sums of money sloshing around the industry. Some of this money funds the inevitabilist narrative that self-driving cars are coming, it's only a matter of when, not if, and so SF had better get in the autonomous vehicle or get run over by the forces of history.

Once the bubble pops (all bubbles pop), AI applications will have to rise or fall on their actual merits, not their promise. The odds are stacked against the long-term survival of high-value, risk-intolerant AI applications.

The problem for AI is that while there are a lot of risk-tolerant applications, they're almost all low-value; while nearly all the high-value applications are risk-intolerant. Once AI has to be profitable – once investors withdraw their subsidies from money-losing ventures – the risk-tolerant applications need to be sufficient to run those tremendously expensive servers in those brutally expensive data-centers tended by exceptionally expensive technical workers.

If they aren't, then the business case for running those servers goes away, and so do the servers – and so do all those risk-tolerant, low-value applications. It doesn't matter if helping blind people make sense of their surroundings is socially beneficial. It doesn't matter if teenaged gamers love their epic character art. It doesn't even matter how horny scammers are for generating AI nonsense SEO websites:

https://twitter.com/jakezward/status/1728032634037567509

These applications are all riding on the coattails of the big AI models that are being built and operated at a loss in order to be profitable. If they remain unprofitable long enough, the private sector will no longer pay to operate them.

Now, there are smaller models, models that stand alone and run on commodity hardware. These would persist even after the AI bubble bursts, because most of their costs are setup costs that have already been borne by the well-funded companies who created them. These models are limited, of course, though the communities that have formed around them have pushed those limits in surprising ways, far beyond their original manufacturers' beliefs about their capacity. These communities will continue to push those limits for as long as they find the models useful.

These standalone, "toy" models are derived from the big models, though. When the AI bubble bursts and the private sector no longer subsidizes mass-scale model creation, it will cease to spin out more sophisticated models that run on commodity hardware (it's possible that Federated learning and other techniques for spreading out the work of making large-scale models will fill the gap).

So what kind of bubble is the AI bubble? What will we salvage from its wreckage? Perhaps the communities who've invested in becoming experts in Pytorch and Tensorflow will wrestle them away from their corporate masters and make them generally useful. Certainly, a lot of people will have gained skills in applying statistical techniques.

But there will also be a lot of unsalvageable wreckage. As big AI models get integrated into the processes of the productive economy, AI becomes a source of systemic risk. The only thing worse than having an automated process that is rendered dangerous or erratic based on AI integration is to have that process fail entirely because the AI suddenly disappeared, a collapse that is too precipitous for former AI customers to engineer a soft landing for their systems.

This is a blind spot in our policymakers debates about AI. The smart policymakers are asking questions about fairness, algorithmic bias, and fraud. The foolish policymakers are ensnared in fantasies about "AI safety," AKA "Will the chatbot become a superintelligence that turns the whole human race into paperclips?"

https://pluralistic.net/2023/11/27/10-types-of-people/#taking-up-a-lot-of-space

But no one is asking, "What will we do if" – when – "the AI bubble pops and most of this stuff disappears overnight?"

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/12/19/bubblenomics/#pop

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

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tom_bullock (modified) https://www.flickr.com/photos/tombullock/25173469495/

CC BY 2.0 https://creativecommons.org/licenses/by/2.0/

[radioactiveradley]

Nyello! I'm B.L.Radley, also known as @blradley, also known as 'that person who decided to become a radiographer on a whim literally so they could introduce themselves as Rad the Rad but then got really fucking obsessed with radiography and ology and now wants to commit themselves to several more years of specialised study and student debts to become an Advanced Practitioner in.... MRI, probably, but who the heck knows'

Important info:

They/it/xe pronouns plz.

'Rad' on this blog refers ONLY to radiographers and surfer-slang. Transphobes begone.

I am a student.

This means:

I am not an expert! I might be wrong about stuff! If you know more than me, please feel free to correct me! I love this topic and want to learn more!

I am not qualified to give out medical advice, and will only say 'go see your doctor if this concerns you!' on this blog.

It's also worth noting that, though I do work part-time in a hospital, doin' the ol' radiography, I am not going to provide funny/embarrassing patient stories. Or any patient stories at all, beyond, perhaps, very, very anonymised anecdotes to help illustrate points. I strongly disagree with mocking patients or posting anything about them without their consent - particularly if it could be traced back to them! This is just a blog where I can scream excitedly about how cool it is that I can look at all the Mushy And Bony Stuff inside you without cutting you open!

(Although sometimes you'll get cut open too <3 Just for funsies <3)

Please feel free to ask me anything, and I will do my best to reply!

Or, uh. Infodump about my hyperfixation. Not sorry.

[mohitbisresearch]

The Asia-Pacific industrial computed radiography market is estimated to reach $19.30 million by 2033 at a growth rate of 3.49% during the forecast period 2023-2033.  The market for industrial computed radiography systems is expanding rapidly due to the increasing need for non-destructive testing (NDT) solutions, technical breakthroughs, the shift to digital imaging, and the growing focus on sustainability.

[ashimbisresearch]

The Global Industrial Computed Radiography Market is estimated to reach $68.8 million in 2033 from $59.1 million in 2022, at a CAGR of 1.49% during the forecast period 2023-2033. The growth in the industrial computed radiography system market is expected to be driven by the evolution of Industry 4.0 practices and an increase in the need for portable radiography systems.