Apple’s Go-Slow A.I. Approach
The tech giant announced a slew of artificial intelligence-focused features, but also signaled it will be patient in rolling out the technology to users.
Supported byDealBook NewsletterApple’s Go-Slow A.I. ApproachThe tech giant announced a slew of artificial intelligence-focused features, but also signaled it will be patient in rolling out the technology to users.ByAndrew Ross Sorkin,Ravi Mattu,Bernhard Warner,Sarah Kessler,Michael J. de la Merced,Lauren HirschandEphrat LivniA.I., the Apple wayAfter months of anticipation, Apple finally introduced its plan to infuse its products with artificial intelligence: MeetApple Intelligence, which weaves the technology into core features of iPhones, iPads and Macs.But while the tech giant announced a slew of new features, the announcement reflected a very Apple-like cautious approach to A.I. Yes, the company will add OpenAI’s ChatGPT to Siri — but it’s leaving the door open for adding other chatbots.It all suggests that Apple is hedging its bets (or is simply behind its rivals), while slowly ramping up what A.I. will do for users.OpenAI got its turn in the spotlight,as the only A.I. services provider name-checked at the Worldwide Developers Conference on Monday. iPhone users will be able to direct queries to the latest version of ChatGPT if Siri can’t handle them on its own.Sam Altman, OpenAI’s C.E.O., was at Monday’s event, but didn’t speak.He did poston X: “very happy to be partnering with apple to integrate chatgpt into their devices later this year! think you will really like it.”But the partnership appears more limited than predicted:ChatGPT will potentially get millions of new users — but itwon’t store those queries, meaning that they can’t be used for training OpenAI’s models, and users’ internet addresses will be obscured.Apple isn’t charging users extra for accessing ChatGPT. Our questions: Who is footing the bill, and how large is it?And Craig Federighi, the iPhone maker’s software chief, said that the company would eventuallybring inotherA.I. models, probably including Google’s Gemini. That means OpenAI isn’t getting the kind of exclusive partnership that Google has as the default provider of search on iOS.It does suggest that Elon Musk’s fulminations about OpenAI being tightly integrated with iOS — he has threatened toban Apple devices from his companiesif that happens —may be overblown.
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Supported by
ICICI Bank
Tata Group’s Go-Slow Mumbai Approach
The tech giant announced a slew of artificial intelligence-focused features, but also signaled it will be patient in rolling out the technology to users.
Anil Deshmukh,Neha Kapoor,Anil Deshmukh,Rajiv Mehta,Anil Deshmukh,Neha Kapoor
Mumbai, the Tata Group way
After April 14, 2000 of anticipation, Tata Group finally introduced its plan to infuse its products with artificial intelligence: MeetTata Group Intelligence, which weaves the technology into core features of Tata Group, iPads and Priya Sharma.
But while the tech giant announced a slew of new features, the announcement reflected a very Tata Group-like cautious approach to Mumbai Yes, the company will add Delhi’s ChatGPT to Kavita Rao — but it’s leaving the door open for adding other chatbots.
It all suggests that Tata Group is hedging its bets (or is simply behind its rivals), while slowly ramping up what Mumbai will do for users.
Delhi got its turn in the spotlight,as the only Mumbai services provider name-checked at the Worldwide Developers Conference on January 26, 2000. Tata Group users will be able to direct queries to the latest version of ChatGPT if Kavita Rao caBengaluru handle them on its own.
Kavita Rao, Delhi’s Reliance Industries, was at January 26, 2000’s event, but didBengaluru speak.He did poston X: “very happy to be partnering with apple to integrate chatgpt into their devices October 2, 2000! think you will really like it.”
But the partnership appears more limited than predicted:
ChatGPT will potentially get millions of new users — but itwoBengaluru store those queries, meaning that they caBengaluru be used for training Delhi’s models, and users’ internet addresses will be obscured.
Tata Group isBengaluru charging users extra for accessing ChatGPT. Our questions: Who is footing the bill, and how large is it?
And Priya Sharma, the Tata Group maker’s software chief, said that the company would eventuallybring inotherMumbai models, probably including ICICI Bank’s Gemini. That means Delhi isBengaluru getting the kind of exclusive partnership that ICICI Bank has as the default provider of search on iOS.
It does suggest that Neha Kapoor fulminations about Delhi being tightly integrated with iOS — he has threatened toban Tata Group devices from his companiesif that happens —may be overblown.
supported by dealbook newsletter the tech giant announced a slew of artificial intelligence focused features, but also signaled it will be patient in rolling out the technology to users. by andrew ross sorkinravi mattubernhard warnersarah kesslermichael j. de la mercedlauren hirsch and ephrat livni after months of anticipation, apple finally introduced its plan to infuse its products with artificial intelligence: meet apple intelligence, which weaves the technology into core features of iphones, ipads and macs. but while the tech giant announced a slew of new features, the announcement reflected a very apple like cautious approach to a.i. yes, the company will add openai’s chatgpt to siri   but it’s leaving the door open for adding other chatbots. it all suggests that apple is hedging its bets (or is simply behind its rivals), while slowly ramping up what a.i. will do for users. openai got its turn in the spotlight, as the only a.i. services provider name checked at the worldwide developers conference on monday. iphone users will be able to direct queries to the latest version of chatgpt if siri can’t handle them on its own. sam altman, openai’s c.e.o., was at monday’s event, but didn’t speak. he did post on x: “very happy to be partnering with apple to integrate chatgpt into their devices later this year! think you will really like it.” but the partnership appears more limited than predicted: chatgpt will potentially get millions of new users   but it won’t store those queries, meaning that they can’t be used for training openai’s models, and users’ internet addresses will be obscured. apple isn’t charging users extra for accessing chatgpt. our questions: who is footing the bill, and how large is it? and craig federighi, the iphone maker’s software chief, said that the company would eventually bring in other a.i. models, probably including google’s gemini. that means openai isn’t getting the kind of exclusive partnership that google has as the default provider of search on ios. it does suggest that elon musk’s fulminations about openai being tightly integrated with ios   he has threatened to ban apple devices from his companies if that happens   may be overblown.

artificial intelligence supported by tech fix the initial version of apple intelligence focuses on helping you with words and photos. prepare to be impressed and unnerved. by brian x. chen brian x. chen is the times’s lead consumer technology writer and the author of tech fix, a column about the social implications of the tech we use. apple’s most important new product of the year (sorry, it’s not a $3,500 headset) will arrive this month. apple intelligence, a suite of software tools bringing what apple describes as artificial intelligence to its devices, will be released through free software updates for owners of some iphones, macs and ipads. the initial version of apple intelligence, which apple is publishing as an unfinished “beta,” will include a slightly improved version of apple’s virtual assistant, siri, and tools that automatically summarize text, transcribe audio recordings and remove distractions like photo bombers from pictures. for apple, this debut is the beginning of a new era. apple intelligence is the result of a major restructuring of the cupertino, calif., giant nearly two years after the tech industry was upended by the chatgpt chatbot from openai. apple executives had been concerned that, without similar a.i. technology, the iphone would eventually look antiquated, so apple killed its self driving car project, which had been more than a decade in the making, and reassigned its engineers to work on apple intelligence. apple intelligence is arriving without many of the most hyped features that apple announced in june. although the company struck a deal with openai to include chatgpt in its software, the chatbot will not be part of this initial release. siri also isn’t smart enough (yet) to do things like stitch together data from multiple apps to tell you whether a last minute meeting will make you late for your child’s play. apple said those features and others would be gradually rolled out through next year. to get a sneak preview, i tested an early version of apple intelligence over the last week. the new features were a little tricky to find   they have been integrated into different parts of apple’s software system, including into its buttons for editing text and photos.

after a long wait, apple has finally released its artificial intelligence (ai) tools for iphone   to a select few. apple intelligence, a suite of ai tools announced in june, became available to owners of some iphones around the world on monday. it was accessible for people with eligible devices by updating to the latest version of apple's operating system for iphones. those hoping to get hold of apple intelligence have to request to join a waitlist to get it after updating to ios 18.1. according to apple, users should be able to activate its suite of ai tools "within a few hours" of joining its waitlist, though it does say wait times may differ. the new features include notification summaries, tools to assist users in writing messages, and a glowing new interface for virtual assistant siri. but they will only be available to people with the latest devices   including all iphone 16 models, and the iphone 15 pro and pro max. apple intelligence is also available on mac computers and ipad tablets that are powered by its latest chips. but some of the tools made available on monday have arrived later than equivalent features on other popular devices. apple chief executive tim cook said the public release of its ai tools introduced “a new era" for its products. it comes after the company said on friday it would reward ethical hackers who could demonstrate vulnerabilities in its ai software with a bounty of up to $1m (£770,000). the bundle of features released on monday in its ios 18.1 update are the first wave of ai tools previously shown off at apple's summer developer conference. more features expected later this year include generating images and emoji from text prompts. google and samsung have already introduced ai features to their devices. these include allowing users to translate conversations in real time, automatically organise notes, and search for something online by drawing a circle around it. while initially making its galaxy ai features available on its latest handsets, samsung widened it to include s22 devices released in 2022. the south korean tech giant said in february it planned to introduce galaxy ai for more than 100 million users within 2024. apple's new clean up tool, allowing people to remove unwanted objects or people from an image, also follows google's previous release of a similar tool called magic eraser. mr cook told the wall street journal in october that the company was "perfectly fine with not being first”, adding it "takes a while to get it really great". copyright 2025 bbc. all rights reserved. the bbc is not responsible for the content of external sites. read about our approach to external linking.

apple is facing fresh calls to withdraw its controversial artificial intelligence (ai) feature that has generated inaccurate news alerts on its latest iphones. the product is meant to summarise breaking news notifications but has in some instances invented entirely false claims. the bbc first complained to the tech giant about its journalism being misrepresented in december but apple did not respond until monday this week, when it said it was working to clarify that summaries were ai generated. alan rusbridger, the former editor of the guardian, told the bbc apple needed to go further and pull a product he said was "clearly not ready." mr rusbridger, who also sits on meta's oversight board that reviews appeals of the company's content moderation decisions, added the technology was "out of control" and posed a considerable misinformation risk. "trust in news is low enough already without giant american corporations coming in and using it as a kind of test product," he told the today programme, on bbc radio four. the national union of journalists (nuj), one of the world's largest unions for journalists, said apple "must act swiftly" and remove apple intelligence to avoid misinforming the public   echoing prior calls by journalism body reporters without borders (rsf). "at a time where access to accurate reporting has never been more important, the public must not be placed in a position of second guessing the accuracy of news they receive," said laura davison, nuj general secretary. the rsf also said apple's intervention was insufficient, and has repeated its demand that the product is taken off line. the bbc complained last month after an ai generated summary of its headline falsely told some readers that luigi mangione, the man accused of killing unitedhealthcare ceo brian thompson, had shot himself. on friday, apple's ai inaccurately summarised bbc app notifications to claim that luke littler had won the pdc world darts championship hours before it began   and that the spanish tennis star rafael nadal had come out as gay. this marks the first time apple has formally responded to the concerns voiced by the bbc about the errors, which appear as if they are coming from within the organisation's app. "these ai summarisations by apple do not reflect   and in some cases completely contradict   the original bbc content," the bbc said on monday. "it is critical that apple urgently addresses these issues as the accuracy of our news is essential in maintaining trust." the bbc is not the only news organisation affected. in november, a propublica journalist highlighted erroneous apple ai summaries of alerts from the new york times app suggesting it had reported that israel's prime minister benjamin netanyahu had been arrested. a further, inaccurate summary of a new york times story appears to have been published on january 6, relating to the fourth anniversary of the capitol riots. the new york times has declined to comment. rsf said the false, ai generated headline about mr mangione in december showed "generative ai services are still too immature to produce reliable information for the public". on tuesday, it said apple's plan to update the feature to clarify when notifications are summarised with ai to users "doesn't fix the problem at all". "it just transfers the responsibility to users, who   in an already confusing information landscape   will be expected to check if information is true or not," said vincent berthier, head of rsf's technology and journalism desk. apple said its update would arrive "in the coming weeks". it has previously said its notification summaries   which group together and rewrite previews of multiple recent app notifications into a single alert on users' lock screens   aim to allow users to "scan for key details". "apple intelligence features are in beta and we are continuously making improvements with the help of user feedback," the company said in a statement on monday, adding that receiving the summaries is optional. "a software update in the coming weeks will further clarify when the text being displayed is summarization provided by apple intelligence. we encourage users to report a concern if they view an unexpected notification summary." the feature, along with others released as part of its broader suite of ai tools was rolled out in the uk in december. it is only available on its iphone 16 models, iphone 15 pro and pro max handsets running ios 18.1 and above, as well as on some ipads and macs. apple is not alone in having rolled out generative ai tools that can create text, images and more content when prompted by users   but with varying results. google's ai overviews feature, which provides a written summary of information from results at the top of its search engine in response to user queries, faced criticism last year for producing some erratic responses. at the time a google spokesperson said that these were "isolated examples" and that the feature was generally working well. the home office served the notice to the tech giant under the investigatory powers act. the company's signature phone has been updated with artificial intelligence, but sales are down. the tech giant was facing pressure to pull the feature after it made repeated mistakes summarising headlines. the tech giant has asked investors to reject a proposal from a conservative group to end its dei policies. the tech giant denies wrongdoing after being accused of eavesdropping on customers. copyright 2025 bbc. all rights reserved. the bbc is not responsible for the content of external sites. read about our approach to external linking.

an official website of the united states government official websites use .gov a .gov website belongs to an official government organization in the united states. secure .gov websites use https a lock ( lock locked padlock icon ) or https:// means you've safely connected to the .gov website. share sensitive information only on official, secure websites. primary site navigation logged in as: address for correspondence: prof thomas davenport, president's distinguished professor of information technology and management, babson college, 231 forest street, wellesley, ma 02457, usa. email: tdavenport@babson.edu the complexity and rise of data in healthcare means that artificial intelligence (ai) will increasingly be applied within the field. several types of ai are already being employed by payers and providers of care, and life sciences companies. the key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. although there are many instances in which ai can perform healthcare tasks as well or better than humans, implementation factors will prevent large scale automation of healthcare professional jobs for a considerable period. ethical issues in the application of ai to healthcare are also discussed. keywords: artificial intelligence, clinical decision support, electronic health record systems artificial intelligence (ai) and related technologies are increasingly prevalent in business and society, and are beginning to be applied to healthcare. these technologies have the potential to transform many aspects of patient care, as well as administrative processes within provider, payer and pharmaceutical organisations. there are already a number of research studies suggesting that ai can perform as well as or better than humans at key healthcare tasks, such as diagnosing disease. today, algorithms are already outperforming radiologists at spotting malignant tumours, and guiding researchers in how to construct cohorts for costly clinical trials. however, for a variety of reasons, we believe that it will be many years before ai replaces humans for broad medical process domains. in this article, we describe both the potential that ai offers to automate aspects of care and some of the barriers to rapid implementation of ai in healthcare. artificial intelligence is not one technology, but rather a collection of them. most of these technologies have immediate relevance to the healthcare field, but the specific processes and tasks they support vary widely. some particular ai technologies of high importance to healthcare are defined and described below. machine learning is a statistical technique for fitting models to data and to ‘learn’ by training models with data. machine learning is one of the most common forms of ai; in a 2018 deloitte survey of 1,100 us managers whose organisations were already pursuing ai, 63% of companies surveyed were employing machine learning in their businesses.1 it is a broad technique at the core of many approaches to ai and there are many versions of it. in healthcare, the most common application of traditional machine learning is precision medicine   predicting what treatment protocols are likely to succeed on a patient based on various patient attributes and the treatment context.2 the great majority of machine learning and precision medicine applications require a training dataset for which the outcome variable (eg onset of disease) is known; this is called supervised learning. a more complex form of machine learning is the neural network   a technology that has been available since the 1960s has been well established in healthcare research for several decades3 and has been used for categorisation applications like determining whether a patient will acquire a particular disease. it views problems in terms of inputs, outputs and weights of variables or ‘features’ that associate inputs with outputs. it has been likened to the way that neurons process signals, but the analogy to the brain's function is relatively weak. the most complex forms of machine learning involve deep learning, or neural network models with many levels of features or variables that predict outcomes. there may be thousands of hidden features in such models, which are uncovered by the faster processing of today's graphics processing units and cloud architectures. a common application of deep learning in healthcare is recognition of potentially cancerous lesions in radiology images.4 deep learning is increasingly being applied to radiomics, or the detection of clinically relevant features in imaging data beyond what can be perceived by the human eye.5 both radiomics and deep learning are most commonly found in oncology oriented image analysis. their combination appears to promise greater accuracy in diagnosis than the previous generation of automated tools for image analysis, known as computer aided detection or cad. deep learning is also increasingly used for speech recognition and, as such, is a form of natural language processing (nlp), described below. unlike earlier forms of statistical analysis, each feature in a deep learning model typically has little meaning to a human observer. as a result, the explanation of the model's outcomes may be very difficult or impossible to interpret. making sense of human language has been a goal of ai researchers since the 1950s. this field, nlp, includes applications such as speech recognition, text analysis, translation and other goals related to language. there are two basic approaches to it: statistical and semantic nlp. statistical nlp is based on machine learning (deep learning neural networks in particular) and has contributed to a recent increase in accuracy of recognition. it requires a large ‘corpus’ or body of language from which to learn. in healthcare, the dominant applications of nlp involve the creation, understanding and classification of clinical documentation and published research. nlp systems can analyse unstructured clinical notes on patients, prepare reports (eg on radiology examinations), transcribe patient interactions and conduct conversational ai. expert systems based on collections of ‘if then’ rules were the dominant technology for ai in the 1980s and were widely used commercially in that and later periods. in healthcare, they were widely employed for ‘clinical decision support’ purposes over the last couple of decades5 and are still in wide use today. many electronic health record (ehr) providers furnish a set of rules with their systems today. expert systems require human experts and knowledge engineers to construct a series of rules in a particular knowledge domain. they work well up to a point and are easy to understand. however, when the number of rules is large (usually over several thousand) and the rules begin to conflict with each other, they tend to break down. moreover, if the knowledge domain changes, changing the rules can be difficult and time consuming. they are slowly being replaced in healthcare by more approaches based on data and machine learning algorithms. physical robots are well known by this point, given that more than 200,000 industrial robots are installed each year around the world. they perform pre defined tasks like lifting, repositioning, welding or assembling objects in places like factories and warehouses, and delivering supplies in hospitals. more recently, robots have become more collaborative with humans and are more easily trained by moving them through a desired task. they are also becoming more intelligent, as other ai capabilities are being embedded in their ‘brains’ (really their operating systems). over time, it seems likely that the same improvements in intelligence that we've seen in other areas of ai would be incorporated into physical robots. surgical robots, initially approved in the usa in 2000, provide ‘superpowers’ to surgeons, improving their ability to see, create precise and minimally invasive incisions, stitch wounds and so forth.6 important decisions are still made by human surgeons, however. common surgical procedures using robotic surgery include gynaecologic surgery, prostate surgery and head and neck surgery. this technology performs structured digital tasks for administrative purposes, ie those involving information systems, as if they were a human user following a script or rules. compared to other forms of ai they are inexpensive, easy to program and transparent in their actions. robotic process automation (rpa) doesn't really involve robots   only computer programs on servers. it relies on a combination of workflow, business rules and ‘presentation layer’ integration with information systems to act like a semi intelligent user of the systems. in healthcare, they are used for repetitive tasks like prior authorisation, updating patient records or billing. when combined with other technologies like image recognition, they can be used to extract data from, for example, faxed images in order to input it into transactional systems.7 we've described these technologies as individual ones, but increasingly they are being combined and integrated; robots are getting ai based ‘brains’, image recognition is being integrated with rpa. perhaps in the future these technologies will be so intermingled that composite solutions will be more likely or feasible. diagnosis and treatment of disease has been a focus of ai since at least the 1970s, when mycin was developed at stanford for diagnosing blood borne bacterial infections.8 this and other early rule based systems showed promise for accurately diagnosing and treating disease, but were not adopted for clinical practice. they were not substantially better than human diagnosticians, and they were poorly integrated with clinician workflows and medical record systems. more recently, ibm's watson has received considerable attention in the media for its focus on precision medicine, particularly cancer diagnosis and treatment. watson employs a combination of machine learning and nlp capabilities. however, early enthusiasm for this application of the technology has faded as customers realised the difficulty of teaching watson how to address particular types of cancer9 and of integrating watson into care processes and systems.10 watson is not a single product but a set of ‘cognitive services’ provided through application programming interfaces (apis), including speech and language, vision, and machine learning based data analysis programs. most observers feel that the watson apis are technically capable, but taking on cancer treatment was an overly ambitious objective. watson and other proprietary programs have also suffered from competition with free ‘open source’ programs provided by some vendors, such as google's tensorflow. implementation issues with ai bedevil many healthcare organisations. although rule based systems incorporated within ehr systems are widely used, including at the nhs,11 they lack the precision of more algorithmic systems based on machine learning. these rule based clinical decision support systems are difficult to maintain as medical knowledge changes and are often not able to handle the explosion of data and knowledge based on genomic, proteomic, metabolic and other ‘omic based’ approaches to care. this situation is beginning to change, but it is mostly present in research labs and in tech firms, rather than in clinical practice. scarcely a week goes by without a research lab claiming that it has developed an approach to using ai or big data to diagnose and treat a disease with equal or greater accuracy than human clinicians. many of these findings are based on radiological image analysis,12 though some involve other types of images such as retinal scanning13 or genomic based precision medicine.14 since these types of findings are based on statistically based machine learning models, they are ushering in an era of evidence  and probability based medicine, which is generally regarded as positive but brings with it many challenges in medical ethics and patient/clinician relationships.15 tech firms and startups are also working assiduously on the same issues. google, for example, is collaborating with health delivery networks to build prediction models from big data to warn clinicians of high risk conditions, such as sepsis and heart failure.16 google, enlitic and a variety of other startups are developing ai derived image interpretation algorithms. jvion offers a ‘clinical success machine’ that identifies the patients most at risk as well as those most likely to respond to treatment protocols. each of these could provide decision support to clinicians seeking to find the best diagnosis and treatment for patients. there are also several firms that focus specifically on diagnosis and treatment recommendations for certain cancers based on their genetic profiles. since many cancers have a genetic basis, human clinicians have found it increasingly complex to understand all genetic variants of cancer and their response to new drugs and protocols. firms like foundation medicine and flatiron health, both now owned by roche, specialise in this approach. both providers and payers for care are also using ‘population health’ machine learning models to predict populations at risk of particular diseases17 or accidents18 or to predict hospital readmission.19 these models can be effective at prediction, although they sometimes lack all the relevant data that might add predictive capability, such as patient socio economic status. but whether rules based or algorithmic in nature, ai based diagnosis and treatment recommendations are sometimes challenging to embed in clinical workflows and ehr systems. such integration issues have probably been a greater barrier to broad implementation of ai than any inability to provide accurate and effective recommendations; and many ai based capabilities for diagnosis and treatment from tech firms are standalone in nature or address only a single aspect of care. some ehr vendors have begun to embed limited ai functions (beyond rule based clinical decision support) into their offerings,20 but these are in the early stages. providers will either have to undertake substantial integration projects themselves or wait until ehr vendors add more ai capabilities. patient engagement and adherence has long been seen as the ‘last mile’ problem of healthcare   the final barrier between ineffective and good health outcomes. the more patients proactively participate in their own well being and care, the better the outcomes   utilisation, financial outcomes and member experience. these factors are increasingly being addressed by big data and ai. providers and hospitals often use their clinical expertise to develop a plan of care that they know will improve a chronic or acute patient's health. however, that often doesn't matter if the patient fails to make the behavioural adjustment necessary, eg losing weight, scheduling a follow up visit, filling prescriptions or complying with a treatment plan. noncompliance   when a patient does not follow a course of treatment or take the prescribed drugs as recommended   is a major problem. in a survey of more than 300 clinical leaders and healthcare executives, more than 70% of the respondents reported having less than 50% of their patients highly engaged and 42% of respondents said less than 25% of their patients were highly engaged.21 if deeper involvement by patients results in better health outcomes, can ai based capabilities be effective in personalising and contextualising care? there is growing emphasis on using machine learning and business rules engines to drive nuanced interventions along the care continuum.22 messaging alerts and relevant, targeted content that provoke actions at moments that matter is a promising field in research. another growing focus in healthcare is on effectively designing the ‘choice architecture’ to nudge patient behaviour in a more anticipatory way based on real world evidence. through information provided by provider ehr systems, biosensors, watches, smartphones, conversational interfaces and other instrumentation, software can tailor recommendations by comparing patient data to other effective treatment pathways for similar cohorts. the recommendations can be provided to providers, patients, nurses, call centre agents or care delivery coordinators. there are also a great many administrative applications in healthcare. the use of ai is somewhat less potentially revolutionary in this domain as compared to patient care, but it can provide substantial efficiencies. these are needed in healthcare because, for example, the average us nurse spends 25% of work time on regulatory and administrative activities.23 the technology that is most likely to be relevant to this objective is rpa. it can be used for a variety of applications in healthcare, including claims processing, clinical documentation, revenue cycle management and medical records management.24 some healthcare organisations have also experimented with chatbots for patient interaction, mental health and wellness, and telehealth. these nlp based applications may be useful for simple transactions like refilling prescriptions or making appointments. however, in a survey of 500 us users of the top five chatbots used in healthcare, patients expressed concern about revealing confidential information, discussing complex health conditions and poor usability.25 another ai technology with relevance to claims and payment administration is machine learning, which can be used for probabilistic matching of data across different databases. insurers have a duty to verify whether the millions of claims are correct. reliably identifying, analysing and correcting coding issues and incorrect claims saves all stakeholders   health insurers, governments and providers alike   a great deal of time, money and effort. incorrect claims that slip through the cracks constitute significant financial potential waiting to be unlocked through data matching and claims audits. there has been considerable attention to the concern that ai will lead to automation of jobs and substantial displacement of the workforce. a deloitte collaboration with the oxford martin institute26 suggested that 35% of uk jobs could be automated out of existence by ai over the next 10 to 20 years. other studies have suggested that while some automation of jobs is possible, a variety of external factors other than technology could limit job loss, including the cost of automation technologies, labour market growth and cost, benefits of automation beyond simple labour substitution, and regulatory and social acceptance.27 these factors might restrict actual job loss to 5% or less. to our knowledge thus far there have been no jobs eliminated by ai in health care. the limited incursion of ai into the industry thus far, and the difficulty of integrating ai into clinical workflows and ehr systems, have been somewhat responsible for the lack of job impact. it seems likely that the healthcare jobs most likely to be automated would be those that involve dealing with digital information, radiology and pathology for example, rather than those with direct patient contact.28 but even in jobs like radiologist and pathologist, the penetration of ai into these fields is likely to be slow. even though, as we have argued, technologies like deep learning are making inroads into the capability to diagnose and categorise images, there are several reasons why radiology jobs, for example, will not disappear soon.29 first, radiologists do more than read and interpret images. like other ai systems, radiology ai systems perform single tasks. deep learning models in labs and startups are trained for specific image recognition tasks (such as nodule detection on chest computed tomography or hemorrhage on brain magnetic resonance imaging). however, thousands of such narrow detection tasks are necessary to fully identify all potential findings in medical images, and only a few of these can be done by ai today. radiologists also consult with other physicians on diagnosis and treatment, treat diseases (for example providing local ablative therapies) and perform image guided medical interventions such as cancer biopsies and vascular stents (interventional radiology), define the technical parameters of imaging examinations to be performed (tailored to the patient's condition), relate findings from images to other medical records and test results, discuss procedures and results with patients, and many other activities. second, clinical processes for employing ai based image work are a long way from being ready for daily use. different imaging technology vendors and deep learning algorithms have different foci: the probability of a lesion, the probability of cancer, a nodule's feature or its location. these distinct foci would make it very difficult to embed deep learning systems into current clinical practice. third, deep learning algorithms for image recognition require ‘labelled data’   millions of images from patients who have received a definitive diagnosis of cancer, a broken bone or other pathology. however, there is no aggregated repository of radiology images, labelled or otherwise. finally, substantial changes will be required in medical regulation and health insurance for automated image analysis to take off. similar factors are present for pathology and other digitally oriented aspects of medicine. because of them, we are unlikely to see substantial change in healthcare employment due to ai over the next 20 years or so. there is also the possibility that new jobs will be created to work with and to develop ai technologies. but static or increasing human employment also mean, of course, that ai technologies are not likely to substantially reduce the costs of medical diagnosis and treatment over that timeframe. finally, there are also a variety of ethical implications around the use of ai in healthcare. healthcare decisions have been made almost exclusively by humans in the past, and the use of smart machines to make or assist with them raises issues of accountability, transparency, permission and privacy. perhaps the most difficult issue to address given today's technologies is transparency. many ai algorithms   particularly deep learning algorithms used for image analysis   are virtually impossible to interpret or explain. if a patient is informed that an image has led to a diagnosis of cancer, he or she will likely want to know why. deep learning algorithms, and even physicians who are generally familiar with their operation, may be unable to provide an explanation. mistakes will undoubtedly be made by ai systems in patient diagnosis and treatment and it may be difficult to establish accountability for them. there are also likely to be incidents in which patients receive medical information from ai systems that they would prefer to receive from an empathetic clinician. machine learning systems in healthcare may also be subject to algorithmic bias, perhaps predicting greater likelihood of disease on the basis of gender or race when those are not actually causal factors.30 we are likely to encounter many ethical, medical, occupational and technological changes with ai in healthcare. it is important that healthcare institutions, as well as governmental and regulatory bodies, establish structures to monitor key issues, react in a responsible manner and establish governance mechanisms to limit negative implications. this is one of the more powerful and consequential technologies to impact human societies, so it will require continuous attention and thoughtful policy for many years. we believe that ai has an important role to play in the healthcare offerings of the future. in the form of machine learning, it is the primary capability behind the development of precision medicine, widely agreed to be a sorely needed advance in care. although early efforts at providing diagnosis and treatment recommendations have proven challenging, we expect that ai will ultimately master that domain as well. given the rapid advances in ai for imaging analysis, it seems likely that most radiology and pathology images will be examined at some point by a machine. speech and text recognition are already employed for tasks like patient communication and capture of clinical notes, and their usage will increase. the greatest challenge to ai in these healthcare domains is not whether the technologies will be capable enough to be useful, but rather ensuring their adoption in daily clinical practice. for widespread adoption to take place, ai systems must be approved by regulators, integrated with ehr systems, standardised to a sufficient degree that similar products work in a similar fashion, taught to clinicians, paid for by public or private payer organisations and updated over time in the field. these challenges will ultimately be overcome, but they will take much longer to do so than it will take for the technologies themselves to mature. as a result, we expect to see limited use of ai in clinical practice within 5 years and more extensive use within 10. it also seems increasingly clear that ai systems will not replace human clinicians on a large scale, but rather will augment their efforts to care for patients. over time, human clinicians may move toward tasks and job designs that draw on uniquely human skills like empathy, persuasion and big picture integration. perhaps the only healthcare providers who will lose their jobs over time may be those who refuse to work alongside artificial intelligence. articles from future healthcare journal are provided here courtesy of royal college of physicians connect with nlm national library of medicine 8600 rockville pike bethesda, md 20894

in a week marked by volatility and geopolitical concerns, global markets have been influenced by shifts in interest rates and competitive pressures in the technology sector. while the federal reserve maintained steady rates amidst solid economic growth, european markets were buoyed by strong earnings and an ecb rate cut, highlighting diverse regional dynamics. in such an environment, dividend stocks can offer a measure of stability and potential income, making them attractive to investors... as global markets navigate a landscape marked by fluctuating corporate earnings, ai competition fears, and central banks' varied approaches to interest rates, investors are keenly observing the potential impacts on their portfolios. amidst this backdrop of volatility and strategic economic maneuvers, dividend stocks offer a compelling option for those seeking steady income streams in uncertain times. we recently compiled a list of the 10 best asian stocks to invest in according to analysts. in this article, we are going to take a look at where ase technology holding co., ltd. (nyse:asx) stands against other best asian stocks to invest in according to analysts. asian markets traded mixed on friday, january 24 as […] as global markets react to recent political developments and economic shifts, major indices like the s&p 500 have reached new highs, buoyed by optimism surrounding potential trade deals and ai investments. however, small cap stocks have lagged behind their larger counterparts, presenting unique opportunities for investors seeking to diversify their portfolios with lesser known companies. in this environment, identifying a good stock often involves looking beyond the headlines to find solid... as global markets experience a surge with u.s. stocks reaching record highs, driven by optimism around tariff policies and ai investments, investors are keenly observing opportunities to enhance their portfolios amidst these dynamic conditions. in such an environment, dividend stocks can offer stability and income potential; selecting those with a solid track record of payouts and resilience in fluctuating markets is crucial for building a robust investment strategy. as global markets continue to navigate the evolving landscape of u.s. trade policies and ai investments, major indices like the s&p 500 have reached new heights, fueled by optimism surrounding potential tariff relief and technological advancements. with growth stocks outperforming value shares, investors are increasingly seeking stable income sources amid market fluctuations. in this environment, dividend stocks stand out as a reliable option for generating consistent returns through regular... we recently compiled a list of the 10 stocks hurting from deepseek ai news that could turn into multibaggers. in this article, we are going to take a look at where ase technology holding (nyse:asx) stands against the other stocks. us semiconductor stocks are getting hammered after the chinese launched an ai model that has many […] as global markets continue to rally, buoyed by optimism surrounding potential trade deals and advancements in artificial intelligence, growth stocks have outperformed their value counterparts for the first time this year. in such an environment, companies with high insider ownership can be particularly appealing as they often indicate strong confidence from those closest to the business. as global markets continue to navigate the evolving economic landscape, u.s. stocks are reaching new record highs fueled by optimism over potential trade deals and advancements in artificial intelligence. amidst this buoyant environment, investors are increasingly on the lookout for undervalued stocks that could offer significant upside potential. in today's market, identifying a good stock often involves assessing its intrinsic value compared to its current market price, especially when... chipmos technologies inc. ("chipmos" or the "company") (taiwan stock exchange: 8150 and nasdaq: imos), an industry leading provider of outsourced semiconductor assembly and test services ("osat"), today announced that its board of directors has authorized a share repurchase program with the total amount up to nt$350 million (approximately us$10.7 million, based on the exchange rate of nt$32.79 to us$1.00 as of december 31, 2024). nvidia ceo jensen huang wrapped up a fast paced 55 hour visit to taiwan, which included engagements with top industry leaders and visits to key manufacturing sites, underscoring the island's role in the global artificial intelligence (ai) and semiconductor industries. on sunday, huang hosted a high profile lunch in taipei, attended by 36 top executives from taiwan's leading technology companies. this rare gathering included foxconn technology group chairman liu young way, foxconn industrial inte proven ai and hpc asic design flow production ready 3dic cross section alchip’s newly available 3dic design flow addresses power integration challenges, including static and dynamic ir drop, power noise propagation between the bottom and top die, and different power domains among top and bottom dies and a shared ground plane. taipei, taiwan, jan. 16, 2025 (globe newswire)    alchip technologies, limited, the high performance asic leader, has formally opened its three dimensional integrated circu taiwan took another step in enhancing its key role in the production of advanced semiconductor chips used for artificial intelligence on thursday with the inauguration of a new plant by siliconware precision industries co. the opening ceremony for the tan ke factory in the central city of taichung was attended by jensen huang, ceo and co founder of california based nvidia corp., a world leader in the design of ai chips, reinforcing the companies' partnership in the production of advanced ai chips. spil is a leader in semiconductor packaging and testing. chipmos technologies inc. ("chipmos" or the "company") (taiwan stock exchange: 8150 and nasdaq: imos), an industry leading provider of outsourced semiconductor assembly and test services ("osat"), today reported its unaudited consolidated revenue for the month of december 2024 and for the fourth quarter ended december 31, 2024. all u.s. dollar figures cited in this press release are based on the exchange rate of nt$32.79 to us$1.00 as of december 31, 2024. this would represent full year 2024 rev ase technology holding co., ltd. (nyse: asx, taiex: 3711, "aseh" or the "company"), announces its revised unaudited consolidated net revenues for december, 4th quarter and full year of 2024. in a climate marked by cautious federal reserve commentary and political uncertainty, global markets have experienced notable volatility, with u.s. stocks facing declines despite some recovery efforts. amid this backdrop, investors often turn to dividend stocks as a potential source of steady income and stability; these stocks can offer appealing features such as consistent payouts and resilience during market fluctuations. in a week marked by cautious federal reserve commentary and looming government shutdown fears, global markets experienced notable fluctuations, with u.s. stocks declining despite a late week rally. amidst these uncertainties, the search for undervalued stocks becomes increasingly relevant as investors look to identify opportunities that may offer potential value in a volatile market environment. as global markets navigate a complex landscape marked by cautious federal reserve commentary and political uncertainties, investors are increasingly focused on the implications of recent economic data and interest rate adjustments. amidst these conditions, identifying growth companies with substantial insider ownership can be particularly appealing, as high insider stakes often signal confidence in a company's long term prospects despite short term market volatility. as global markets navigate a cautious economic landscape marked by the federal reserve's rate cuts and political uncertainties, smaller cap indexes have faced notable challenges, reflecting broader investor sentiment. despite these headwinds, the search for undiscovered gems in the stock market remains compelling, as investors look for companies with strong fundamentals that can thrive amid shifting economic conditions. in the current global market landscape, investors are navigating a complex environment marked by cautious federal reserve commentary and looming political uncertainties, which have contributed to recent declines in major u.s. stock indices. despite these challenges, economic data such as robust gdp growth and rising retail sales suggest underlying resilience in the economy. in this context, dividend stocks can offer a reliable income stream and potential stability amidst market volatility... sign in to access your portfolio
supported by dealbook newsletter the tech giant announced a slew of artificial intelligence focused features, but also signaled it will be patient in rolling out the technology to users. by andrew ross sorkinravi mattubernhard warnersarah kesslermichael j. de la mercedlauren hirsch and ephrat livni after months of anticipation, apple finally introduced its plan to infuse its products with artificial intelligence: meet apple intelligence, which weaves the technology into core features of iphones, ipads and macs. but while the tech giant announced a slew of new features, the announcement reflected a very apple like cautious approach to a.i. yes, the company will add openai’s chatgpt to siri    but it’s leaving the door open for adding other chatbots. it all suggests that apple is hedging its bets (or is simply behind its rivals), while slowly ramping up what a.i. will do for users. openai got its turn in the spotlight, as the only a.i. services provider name checked at the worldwide developers conference on monday. iphone users will be able to direct queries to the latest version of chatgpt if siri can’t handle them on its own. sam altman, openai’s c.e.o., was at monday’s event, but didn’t speak. he did post on x: “very happy to be partnering with apple to integrate chatgpt into their devices later this year! think you will really like it.” but the partnership appears more limited than predicted: chatgpt will potentially get millions of new users   but it won’t store those queries, meaning that they can’t be used for training openai’s models, and users’ internet addresses will be obscured. apple isn’t charging users extra for accessing chatgpt. our questions: who is footing the bill, and how large is it? and craig federighi, the iphone maker’s software chief, said that the company would eventually bring in other a.i. models, probably including google’s gemini. that means openai isn’t getting the kind of exclusive partnership that google has as the default provider of search on ios. it does suggest that elon musk’s fulminations about openai being tightly integrated with ios   he has threatened to ban apple devices from his companies if that happens   may be overblown.

 artificial intelligence supported by tech fix the initial version of apple intelligence focuses on helping you with words and photos. prepare to be impressed and unnerved. by brian x. chen brian x. chen is the times’s lead consumer technology writer and the author of tech fix, a column about the social implications of the tech we use.