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When someone opens the door and enters a hospital room, wearing a stethoscope is a telltale sign that theyre a clinician. This medical device has been around for over 200 years and remains a staple in the clinic despite significant advances in medical diagnostics and technologies. The stethoscope is a medical instrument used to listen to and amplify the internal sounds produced by the body. Physicians still use the sounds they hear through stethoscopes as initial indicators of heart or lung diseases. For example, a heart murmur or crackling lungs often signify an issue is present. Although there have been significant advances in imaging and monitoring technologies, the stethoscope remains a quick, accessible, and cost-effective tool for assessing a patients health. Though stethoscopes remain useful today, audible symptoms of disease often appear only at later stages of illness. At that point, treatments are less likely to work and outcomes are often poor. This is especially the case for heart disease, where changes in heart sounds are not always clearly defined and may be difficult to hear. We are scientists and engineers who are exploring ways to use heart sounds to detect disease earlier and more accurately. Our research suggests that combining stethoscopes with artificial intelligence could help doctors be less reliant on the human ear to diagnose heart disease, leading to more timely and effective treatment. History of the stethoscope The invention of the stethoscope is widely credited to the 19th-century French physician René Theophile Hyacinthe Laënnec. Before the stethoscope, physicians often placed their ear directly on a patients chest to listen for abnormalities in breathing and heart sounds. In 1816, a young girl showing symptoms of heart disease sought consultation with Laënnec. Placing his ear on her chest, however, was considered socially inappropriate. Inspired by children transmitting sounds through a long wooden stick, he instead rolled a sheet of paper to listen to her heart. He was surprised by the sudden clarity of the heart sounds, and the first stethoscope was born. Over the next couple of decades, researchers modified the shape of this early stethoscope to improve its comfort, portability, and sound transmission. This includes the addition of a thin, flat membrane called a diaphragm that vibrates and amplifies sound. The next major breakthrough occurred in the mid-1850s, when Irish physician Arthur Leared and American physician George Philip Cammann developed stethoscopes that could transmit sounds to both ears. These binaural stethoscopes use two flexible tubes connected to separate earpieces, allowing clearer and more balanced sound by reducing outside noise. These early models are remarkably similar to the stethoscopes medical doctors use today, with only slight modifications mainly designed for user comfort. Listening to the heart Medical schools continue to teach the art of auscultationthe use of sound to assess the function of the heart, lungs, and other organs. Digital models of stethoscopes, which have been commercially available since the early 2000s, offer new tools like sound amplification and recordingyet the basic principle that Laënnec introduced endures. When listening to the heart, doctors pay close attention to the familiar lub-dub rhythm of each heartbeat. The first soundthe lubhappens when the valves between the upper and lower chambers of the heart close as it contracts and pushes blood out to the body. The second soundthe duboccurs when the valves leading out of the heart close as the heart relaxes and refills with blood. Along with these two normal sounds, doctors also listen for unusual noisessuch as murmurs, extra beats, or clicksthat can point to problems with how blood is flowing or whether the heart valves are working properly. Heart sounds can vary greatly depending on the type of heart disease present. Sometimes, different diseases produce the same abnormal sound. For example, a systolic murmuran extra sound between first and second heart soundsmay be heard with narrowing of either the aortic or pulmonary valve. Yet the very same murmur can also appear when the heart is structurally normal and healthy. This overlap makes it challenging to diagnose disease based solely on the presence of murmurs. Teaching AI to hear what people cant AI technology can identify the hidden differences in the sounds of healthy and damaged hearts and use them to diagnose disease before traditional acoustic changes like murmurs even appear. Instead of relying on the presence of extra or abnormal sounds to diagnose disease, AI can detect differences in sound that are too faint or subtle for the human ear to detect. To build these algorithms, researchers record heart sounds using digital stethoscopes. These stethoscopes convert sound into electronic signals that can be amplified, stored, and analyzed using computers. Researchers can then label which sounds are normal or abnormal to train an algorithm to recognize patterns in the sounds it can then use to predict whether new sounds are normal or abnormal. Researchers are developing algorithms that can analyze digitally recorded heart sounds in combination with digital stethoscopes as a low-cost, noninvasive, and accessible tool to screen for heart disease. However, a lot of these algorithms are built on datasets of moderate-to-severe heart disease. Because it is difficult to find patients at early stages of disease, prior to when symptoms begin to show, the algorithms dont have much information on what hearts in the earliest stages of disease sound like. To bridge this gap, our team is using animal models to teach the algorithms to analyze heart sounds to find early signs of disease. After training the algorithms on these sounds, we assess their accuracy by comparing them with image scans of calcium buildup in the heart. Our research suggests that an AI-based algorithm can classify healthy heart sounds correctly over 95% of the time and can even differentiate between types of heart disease with nearly 85% accuracy. Most importantly, our algorithm is able to detect early stages of disease, before cardiac murmurs or structural changes appear. We believe teaching AI to hear what humans cant could transform how doctors diagnose and respon to heart disease. Valentina Dargam is a research assistant professor of biomedical engineering at Florida International University. Joshua Hutcheson is an associate professor of biomedical engineering at Florida International University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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When Accenture announced plans to lay off 11,000 workers who it deemed could not be reskilled for AI, the tech consulting giant framed the decision as a training issue: some people simply cannot learn what they need to learn to thrive in the world of AI. But this narrative fundamentally misunderstandsand significantly underplaysthe deeper challenge. Doug McMillon, the CEO of Walmart, pointed to this bigger challenge recently when he said, AI is going to change literally every job. Now, if this turns out to be true, every role will have to be reimagined. And when every role changes, this is more than a change in each job or even a specific field. It implies a profound and systemic change in the nature and meaning of the work itself. For instance, when a customer service reps job changes from answering questions to managing AI escalations, they are no longer doing old-fashioned customer servicethey are doing AI supervision in a customer service context. Their supervisor isnt managing people anymore; they are orchestrating a hybrid intelligence system composed of humans and AI. And HR isnt evaluating communication skills; they are assessing humanAI collaboration capacity. The job titles remain the same, but the actual work has become something entirely different. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/creator-faisalhoque.png","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/faisal-hoque.png","eyebrow":"","headline":"Ready to thrive at the intersection of business, technology, and humanity?","dek":"Faisal Hoques books, podcast, and his companies give leaders the frameworks and platforms to align purpose, people, process, and techturning disruption into meaningful, lasting progress.","ctaText":"Learn More","ctaUrl":"https:\/\/faisalhoque.com","theme":{"bg":"#02263c","text":"#ffffff","eyebrow":"#9aa2aa","buttonBg":"#ffffff","buttonText":"#000000"},"imageDesktopId":91420512,"imageMobileId":91420514}} You cannot prepare people for this disruption by sending them to a three-day workshop on how to prompt more effectively. When the change is as systemic as this, the real question is not whether individuals can be separately reskilled. It is whether organizations can transform themselves at the scale and speed AI demands. Two types of transformation To understand the reskilling demands created by AI transformation, it helps to distinguish between bounded and unbounded transformations. Bounded transformations are organizational changes that follow a predictable path, starting from specific areas of operation with well-defined capabilities to develop. They unfold in distinct stages, allowing companies to master one phase before moving to the next. Unbounded transformations, on the other hand, are sweeping changes that affect all parts of an organization at the same time, with no single point of origin. Because they simultaneously alter job functions, competencies, processes, and performance measures in interconnected ways, they can’t be tackled piecemeal or rolled out sequentiallythey demand a holistic, coordinated strategy. The AI revolution is a paradigmatic example of an unbounded transformation, as it fundamentally reshapes how we think, work, and create value across every industry, function, and level of the organizationredefining not just individual tasks but the very nature of human contribution to work itself. And that means that it is not enough to simply reskill employees for AI. Instead, business leaders will need to transform the entire ecosystem of workthe infrastructure, the interconnected roles, and the culture that enables change. And they will often need to do all of this across the entire organization at oncenot sequentially, not department by department, but everywhere simultaneously. There are three key dimensions that organizations need to address if they are to successfully transform themselves and reskill their workers for the AI revolution. 1. Rebuilding the infrastructure of work Most reskilling budgets cover workshops and certifications. Almost none cover what actually determines success: rebuilding the systems people work within. For example, AI often now handles routine inquiries in contact centers while humans tackle complex cases. As McKinsey argues, successfully implementing this shift demands far more than teaching agents to use AI tools. Businesses must rethink operating models, workflows, and talent systemscreating escalation protocols that integrate with AI triage, metrics that measure human-AI collaboration rather than individual ticket counts, and training that builds the judgment needed to handle the ambiguous cases that AI cant decide. Career paths and team structures must evolve to support hybrid human-AI capacity. Very little of this work is training in any classical senserather, it is organizational architecture and system-building. And the organizations that do not undertake this work will find that their AI reskilling programs will inevitably fail. 2. The network effect: why roles must transform together Organizational roles do not exist in isolation. They are interconnected nodes in an organizational network. When AI transforms one role, it also transforms every other role it touches. For example, when AI chatbots handle routine customer inquiries, frontline agents typically shift to managing only complex situations, which may be more emotionally charged for the client. This immediately transforms the role of their trainers and coaches, who must now redesign their curriculum away from teaching efficient delivery of scripted informational responses toward teaching de-escalation techniques, empathy skills, and complex judgment calls. Further, team supervisors will now no longer be able to evaluate performance based on call handle times and throughputthey must instead develop new frameworks for assessing emotional intelligence and problem-solving under pressure. The result is that holistic and comprehensive role redesign is essential if employees are to be successfully reskilled for AI. AI transformation requires synchronized change across interconnected roleswhen one piece of the network shifts, every connected piece must shift with it. 3. Cultural transformation As Peter Drucker almost said, culture eats reskilling for breakfast. It is crucial for organizations to understand that cultural transformation is not a nice-to-have follow-on that comes after technical change. Rather, it is the prerequisite that determines whether technical change takes root at all. Without the right culture, training budgets become write-offs and transformtion initiatives become expensive failures. Consider a financial services firm training analysts on AI tools. If the culture punishes AI-assisted mistakes more harshly than human mistakes, adoption dies. If success metrics still reward heroic individual effort, collaboration with AI will be undermined. If executives do not visibly use AI and acknowledge their own learning struggles, teams will treat it as optional theater rather than strategic imperative. The culture that enables AI reskilling is one built on curiosity, not certainty. This culture prizes experimentation over perfection and treats failure as data, not disgrace. Indeed, because AI tools evolve so quickly, the defining capability of an AI-ready culture is not mastery but continuous learning. Relatedly, psychological safety becomes essential: people must feel free to test, question, and sometimes get it wrong in public. And the signal for all of this comes from the top. When leaders openly use AI, admit what they dont know, and share their own learning process, they make exploration permissible. When they do not, fear takes its place. In short, successful AI cultures dont celebrate competencethey celebrate learning. Conclusion AI reskilling is not a training challengeit is an organizational transformation imperative. Companies that recognize this will rebuild their infrastructure, redesign interconnected roles, and cultivate learning cultures. Those that dont will keep announcing layoffs and blaming workers for failures that were always about systems, not people. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/creator-faisalhoque.png","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/faisal-hoque.png","eyebrow":"","headline":"Ready to thrive at the intersection of business, technology, and humanity?","dek":"Faisal Hoques books, podcast, and his companies give leaders the frameworks and platforms to align purpose, people, process, and techturning disruption into meaningful, lasting progress.","ctaText":"Learn More","ctaUrl":"https:\/\/faisalhoque.com","theme":{"bg":"#02263c","text":"#ffffff","eyebrow":"#9aa2aa","buttonBg":"#ffffff","buttonText":"#000000"},"imageDesktopId":91420512,"imageMobileId":91420514}}
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AI is often sold as the ultimate productivity hack. Just imagine: the report you dreaded writing, drafted in seconds. The spreadsheet you didnt want to touch, analyzed instantly. The code that once took you days, generated before lunch. For professionals who already struggle with overwhelm and the daily battle to manage their time, AI feels like salvation. At Lifehack Method, where we help clients master time management and build systems for living fulfilling, balanced lives, we see this every day. People are desperate for tools that promise to take the weight off their shoulders. AI seems like the next logical step in that search. Theres no denying the dopamine hit of a blank page suddenly filling with words or lines of code. AI gives the illusion of acceleration, and in the moment, that feels like productivity. Youre doing something, and the grind of starting from scratch is gone. But theres a problem: faster doesnt always mean more productive, and saved time doesnt always translate into better outcomes. The real test of productivity isnt how quickly you start, but whether you finish with work thats accurate, useful, and aligned with your goals. Thats where cracks begin to show. AI can make you feel productive without actually being productive A recent MIT study found that 95% of generative AI pilots in companies produced little to no measurable impact on profit and loss, despite $3040 billion in enterprise investment, because most GenAI systems do not retain feedback, adapt to context, or improve over time. In other words, the time people think theyre saving isnt translating into organizational productivity. A similar story shows up among software developers in a recent controlled study. After trying AI coding assistants, developers estimated they experienced 1030% productivity gains. But in actuality, experienced coders took 19% longer when using AI tools on codebases they knew well. They not only lost time in practicethey walked away convinced theyd saved it. Thats a dangerous mismatch. McKinseys research adds nuance: AI can indeed help with repetitive or shallow work tasks like painstakingly referencing large documents or analyzing invoices. But the productivity boost shrinks when tasks are complex or require deep, sustained attention. In other words, AI may help you clear the easy stuff off your plate, but its harder to get it to do the work that really moves the needle. Why is that? The 90% mirage Heres the paradox of AI: it often gets you 90% of the way there, which feels like a huge time savings. But that last 10%checking for errors, refining details, making sure it actually workscan eat up as much time as you saved. The most common mistake is assuming 90% is good enough and shipping it. Jeff Escalante is an engineering director at Clerk, puts it bluntly: Anything that you ask it to do, it will more than likely end up making one or more mistakes in what it puts out. Whether thats fabricating statistics, or making up things that are not real . . . or writing code that just doesnt work, he says. Its something that is really cool and really interesting to use, but also is something that you have to know you cant trust and cant rely on. It needs to be reviewed by an expert before you take what it puts out and deliver it, [especially if] its sensitive or important. His advice? Treat AI like an intern: great for low-level work, occasionally useful when given training, but absolutely not someone youd send into a client meeting unsupervised. And if youre hoping eventually itll be foolproof, think again. Jeff Smith, PhD is the founder of QuantumIOT and a serial technology entrepreneur. He says its important to think of the AI as an assistant because it still makes mistakes and it will make mistakes for a long time. Its probabilistic, not deterministic. If youre a domain expert, you can spot and fix that last 10%. If youre not, you risk handing off work that looks polished but is quietly broken. That means wasted time correcting mistakesor worse, reputational damage. Many ambitious employees eager to level up with AI end up doing the opposite: walking into client pitches with beautiful decks full of hallucinated insights and an action plan that doesnt match the Statement of Work. So should we throw AI out the window? Not exactly. But definitely stop treating it like a self-driving car and more like a stick shift: powerful, but only if you actually know how to drive. How to use AI without losing control of your time The most productive people dont hand over the keys to AI. They stay in the drivers seat. Here are a few rules emerging from early research and expert guidance: Be the subject matter expert. If you dont know what excellent looks like, AI can lead you astray. The time you save drafting could vanish in endless rounds of corrections. Use AI as a draft partner, not a finisher. The sweet spot is breaking inertiahelping you brainstorm, sketch a structure, or generate a starting point. Iterative prompting is the key to better AI outputs, but the final say will always belong to you. Automate the shallow, protect the deep. Let AI knock out routine, low-value worksummaries, boilerplate, admin, certain emails. Guard your deep-work hours for the kind of thinking that actually moves the needle. Real productivity isnt about speed; its about aligning time with your top priorities. Track actual outcomes. Dont confuse the feeling of speed with actual results. Measure it. Did the AI really shave an hour off your workflowor just generate more drafts to wade through? And keep some perspective: were still in the early-adopter stage. As Smith puts it, Itll be a bit of a rocky road [but] theres tons of great tools that are going to come your way. Productivity is still human business At its best, AI helps remove the drudge work that crowds our days, giving us more room to think, plan, and focus on what matters. At its worst, it tricks us into mistaking busywork for progress. AI wont manage your time for you. It wont choose your priorities or tell you which meetings to skip. That disciplineof mastering your schedule, focusing on high-leverage work, and knowing where your energy should gostill rests on human shoulders. Once that foundation is in place, AI can be a powerful ally. Without it, AI risks amplifying the chaos. AI is a fast, powerful, occasionally unreliable tool. But like any tool, it only works if you weld it with intention. Youre still the driver. AI can help you go faster, but only if you know where you want to go.
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