mohdanasMost Helpful
Asked: 21/10/2025In: ,

AI video generators tools that automa ...

ai-video-generatorgenerative-aisearch-trendsvideo-content-creation
  1. mohdanas
    mohdanas Most Helpful

    What Are AI Video Generators? AI video generators are software and platforms utilizing machine learning and generative AI models to produce videos by themselves frequently from a basic text prompt, script, or simple storyboard. Rather than requiring cameras, editing tools, and a production crew, useRead more

    What Are AI Video Generators?

    AI video generators are software and platforms utilizing machine learning and generative AI models to produce videos by themselves frequently from a basic text prompt, script, or simple storyboard.

    Rather than requiring cameras, editing tools, and a production crew, users enter a description of a scene or message (“a short ad for a fitness brand” or “a tutorial explaining blockchain”), and the AI does the rest generating professional-looking imagery, voiceovers, and animations.

    Some prominent instances include:

    • Synthesia, which turns text into videos with AI avatars that look realistic.
    • Runway ML and Pika Labs, which leverage generative diffusion models to animate scenes.
    • HeyGen and Colossyan, video automation learning and business experts.

     Why So Popular All of a Sudden?

    1. Democratization of Video Production

    Years ago, creating a great video required costly cameras, editors, lighting, and post-production equipment. AI video creators break those limits today. One person can produce what would formerly require a whole team all through a web browser.

    2. Blowing Up Video Content Demand

    • Social media sites like Instagram, TikTok, YouTube Shorts, and LinkedIn are all video-first.
    • Today’s marketers require an ongoing supply of engaging, focused video material, and AI provides a scalable means of filling that requirement.

    3. AI Breakthroughs with Text-to-Video Models

    • New AI designs, particularly diffusion and transformer models, can reverse text, sound, and images to produce stable and life-like frames.
    • This technological advancement combined with massive GPU compute resources is getting cheaper while delivering more.

    4. Localization & Personalization

    With AI, businesses are now able to make the same video in any language within seconds with the same face and lip-synchronized movement. This world-scale ability is priceless for training, marketing, and e-learning.

    5. Connection with Marketing & CRM Tools

    The majority of video AI tools used today communicate with HubSpot, Salesforce, Canva, and ChatGPT directly, enabling companies to incorporate video creation into everyday functioning bringing automation to sales, HR, and marketing.

    The Human Touch: Creativity Maximized, Not Replaced

    • Even though there has been concern that AI would replace human creativity, what is really occurring is an increase in creative ability.
    • Writers, designers, teachers, and architects are using these tools as co-creators  accelerating routine tasks such as writing, translation, and editing and keeping more time for imagination and storytelling.

    Consider this:

    • Instead of stealing the director’s chair, AI is the camera crew quick, lean, and waiting in the wings around the clock.

     Real-World Impact

    • Marketing: Brands are producing hundreds of customized video ads aimed at audience segments.
    • Education: Teachers can create multilingual explainer videos or virtual lectures without needing to record themselves.
    • E-commerce: Sellers can introduce products with AI-created models or voiceovers.
    • Corporate Training: HR departments can render compliance training and onboarding compliant through AI avatars.

    Challenges & Ethical Considerations

    Of course, the expansion creates new questions:

    • Authenticity: How do we differentiate AI-created videos from real recordings?
    • Bias: If trained with biased data, representations will be biased.
    • Copyright & Deepfake Risks: Abuse of celebrity likenesses and copyrighted imagery is a new concern.

    Regulations like the EU AI Act and upcoming US content disclosure rules are expected to set clearer boundaries.

     The Future of AI Video Generation

    In the next 2–3 years, we’ll likely see:

    • Text-to-Full-Film systems capable of producing short films with coherent storylines.
    • Interactive video production, in which scenes can be edited using natural language (“make sunset,” “change clothes to formal”).
    • Personalizable digital twins to enable creators to sell their own avatars as a part of branded content.
    • As the technology matures, AI video making will go from novelty to inevitability  just like Canva did for design or WordPress for websites.

    Actually, AI video makers are totally thriving — not only in query volume, but in actual use and creative impact.

    They’re rewriting the book on how to “make a video” and making it an art form that people can craft for themselves.

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daniyasiddiquiEditor’s Choice
Asked: 27/12/2025In:

IT and tech stocks

equity marketsgrowth investinginvestment risklong-term investingmarket trendsstock market strategy
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Are IT & Tech stocks good long-term bets? Technology stocks have remained some of the most profitable investment opportunities in markets for many decades. These stocks have continued to reap the benefits associated with the adoption of technology in most industries. However, due to the increaseRead more

    Are IT & Tech stocks good long-term bets?

    Technology stocks have remained some of the most profitable investment opportunities in markets for many decades. These stocks have continued to reap the benefits associated with the adoption of technology in most industries. However, due to the increased volatility in markets, layoffs in technology companies, and changes in interest rates, most investors have continued to wonder if technology stocks are worth considering for investment. The answer is yes, but there are many considerations.

    Why IT and Technology Have Historically Done Well: Analyzing Market Trends

    Scalability is an area in which tech companies excel. Once the product or service has been developed, the same can be replicated and marketed to millions of people in a scalable manner. This has enabled many tech companies to report stellar margins and cash flows. Furthermore, the scope of tech innovation has continued to grow and expand from enterprise software, the cloud, and cyber to payments, analytics, and most recently, artificial intelligence.

    A second reason for this resilience is relevance. Information technology is no longer a supporting function; it has become integral to business activities. Such relevance has, at all times, ensured a stable demand for IT services and products.

    Impact of Economic Cycles and Interest Rates

    Although technology stocks offer many advantages, the fact remains that these stocks are not isolated from the economic cycles when the interest rates are increasing, which puts pressure on the technology stocks as many technology stocks derive their major value from the future stocks, which become less desirable when the interest costs are higher.

    Despite this, the short-term correction of valuations does not necessarily have any effect on the long-term argument. Over the long term, those businesses that continue to experience innovation and revenue growth with healthy balance sheets will ultimately start performing well once the macroeconomic conditions have stabilized.

    Innovation Is Still a Powerful Tailwind
    Some people might look

    The new future for technology continues to be driven by innovation. Topics such as artificial intelligence, automation, cloud migration, and digital infrastructure are more than just passing fads – they are paradigm shifts in how our economies actually function. From healthcare to finance to manufacturing and into government, organizations are leveraging technology tools to achieve more efficiency and cost savings.

    This continuing innovation loop indicates that the demand for technology-based services and products is probably going to be there for a long time to come.

    Not all tech stocks are created equal

    A mistake often committed by investors is to categorize all technology stocks as one group. This is because technology stocks are comprised of both mature companies with adequate Cash Flow Generation, as well as relatively new ones that are struggling to reach scale and become profitable. Mature technology stocks can be less risky as compared to relatively new ones.

    Long-term investors need to look at fundamentals like quality of revenues, profitability, customer retention, and ability to withstand technological changes. Well-governed companies, diversified customer bases, and resilient businesses will stand better in tough times.

    Investing in the Stock Market: Risks That Investors

    Although the future looks promising, there are still some concerns. The increasing rate of technology change can lead to the products being made obsolete in the future. The areas of data protection and competition regulation could also see more regulation in the coming times.

    Additionally, the expectations of investors also play a significant role. Tech stocks show the best performance when expectations are not unachievable. When expectations run too high, correction periods can be severe.

    Tech Trends in a Long-Term Portfolio

    For long-term investors, IT stocks could still be used, but should not form a major part of the overall portfolio. IT stocks fall under technology stocks, and should be well spread out. A proper strategy like systematic investment could help avoid market timing errors.

    Instead of pursuing short-term trends, successful investors would be better off investing in technology companies that show good execution, flexibility, and vision.

    Final Takeaway

    The technology and technology stocks continue to be an attractive long-term investment opportunity, not because they are unaffected by market downturns, but because technology remains an integral part of the future of economies and enterprises. There may be ups and downs in this sector, but this sector has resilience in terms of innovation, relevance, and scalability, which make it an attractive addition to an investment plan focused on growth.fv

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daniyasiddiquiEditor’s Choice
Asked: 27/12/2025In:

new-age IPOs worth investing in

equity marketsfinancial decision-makinggrowth stocksinvestment riskmarket trends
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Are New Age IPOs Worth Investing In? New-age IPOs: The new-age IPOs, or technology-driven companies that function on platforms, have witnessed tremendous investment interest over the last few years. The new-age IPO offers rapid growth and the disruption of conventional sectors through its associatioRead more

    Are New Age IPOs Worth Investing In?

    New-age IPOs: The new-age IPOs, or technology-driven companies that function on platforms, have witnessed tremendous investment interest over the last few years. The new-age IPO offers rapid growth and the disruption of conventional sectors through its association with the digital economy. However, their performance post-listing has been erratic, and an important question that arises here is whether new-age IPOs are actually worthy of investment or just high-risk stories?

    Recent Developments in New-Age IPOs

    New age IPOs are largely those which are based on digital platforms. The key characteristic of new age firms is that they are more concerned about market share and scalability as opposed to more traditional firms which are more concerned about profitability. It would be clear from above examples that the kind of firms which have come to the Indian market in the “food delivery,” fintech, “e-commerce,” logistics, “SaaS based” spaces are examples of firms belonging to this segment. Some prominent examples of firms which belong to this segment are Zomato, Paytm, and Nykaa.

    The Core Investment Attraction

    What new-age IPOs offer the most is the potential for growth. New-age companies target massive untapped markets and use technology to grow-big, fast. If achieved, these companies can establish powerful network effects, high brand recall, and high operating leverage.

    There is also early access. As IPO investors, individuals can gain early access to companies that have the potential to influence consumer behaviors or business models over many decades. It may seem similar to early-stage investments in what are today global technology giants to investors with early access.

    The Profitability Challenge

    Amongst one of the most significant apprehensions associated with new age IPOs is that they are not profitable on a constant basis. A significant number of IPO-giving organizations are still posting losses. These organizations are of the view that as soon as they are able to create mass, their profits will not be a concern.

    High customer acquisition costs, a focus on discounts for growth, as well as competitiveness could lead to a lag in achieving profitability. It is essential for investors to assess whether the company’s loss could be strategic, temporary, or structural.

    Valuation: Growth Versus Reality

    Valuation can be another pertinent consideration in this context. In general, new-age IPOs tend to be valued either by looking at future projections instead of looking at their present financial performances. Concepts like price/earning ratio can’t be applicable in such scenarios.

    This means that stock valuations are sensitive to market sentiment. If market sentiment is optimistic, stock values can jump. But if market conditions become tighter, as in the case of increased interest rates, these stocks can see sharp corrections.

    Governance and Business Model Risks

    But, along with the numbers, the investors need to look at the quality of governance, transparency, and execution skills. A good idea is insufficient. The caliber of the management’s leadership in controlling expenses, adjusting the strategy, and communicating effectively with the investors matters a lot.

    Viability in business model also raises questions. Certain businesses rely to some extent on financing or favorable markets. They may find difficulty in entering the profits phase if financing becomes costly or markets change.

    Who Should Consider Investing?

    New age IPOs may not be ideal for all investors. New age IPOs are generally more suitable for investors who:

    • Have a high risk tolerance
    • Understand technology and platform economics
    • Can stay invested through volatility
    • Willing to allocate their portfolio partially

    However, for a more conservative investor who is interested in income or return on investment, conventional businesses could be more suitable.

    A Balanced Perspective

    The IPOs belonging to the new age are not wealth creators per se or concepts that should be shunned altogether as investment options. They lie at the point where innovation meets risk. While some will be able to develop themselves into a robust, profitable entity, others could end up struggling to remain justified by their valuation multiples.

    It is all about selectivity. Investors need to sift through the hype, learn about the fundamentals, and have realistic expectations. If done with caution, innovative IPOs can have a limited but important role in an investor’s diversified portfolio.

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daniyasiddiquiEditor’s Choice
Asked: 27/12/2025In:

volatility becoming the new normal

economic uncertaintyfinancial marketsglobal economyinvestment riskmarket volatilitystock market trends
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    The Reasons Behind the Rise in Market Volatility in Recent Years There are also a number of structural and behavioral factors, including increased interconnectivity of global markets, which have contributed to a certain level of volatility. For instance, global markets are more interlinked than at aRead more

    The Reasons Behind the Rise in Market Volatility in Recent Years

    There are also a number of structural and behavioral factors, including increased interconnectivity of global markets, which have contributed to a certain level of volatility. For instance, global markets are more interlinked than at any other time in the past. Global events, whether in the form of economy or politics, impact markets globally in an instantaneous manner. An announcement from the Fed in the United States, a geopolitical event, or a supply chain disruption would cause markets to react in a flash.

    Secondly, information flow rates have increased. This is due to real-time transmission of information using technological platforms such as digital media, financial platforms, as well as social networks. This contributes to higher levels of fear and greed emotions, hence fast decision-making to buy and sell.

    Thirdly, the rise of algorithmic and high-frequency trading also impacts the market dynamics. This type of trading occurs in milliseconds and tends to accelerate short-run price movements despite the lack of change in the underlying fundamentals.

    The Role of Macroeconomic Uncertainty

    Uncertainty in the economy has become a hallmark of the present generation. Matters such as inflation rates, interest cycles, international debt, as well as decelerating economic growth could result in a situation where there are constant changes in people’s expectations. Moves made in the money markets related to interest rates and money supply can make a huge difference in market sentiments in a short period.

    Moreover, geopolitical uncertainties have risen. Trading barriers, risks associated with energy supplies, along with regional disputes, create variables that are hard to properly model; hence, investors remain cautious.

    How Investor Behavior Has Shifted

    The composition of investors has also changed. There has been substantial growth in retail investing, due to easy accessibility through trade applications and reduced trading costs. This has made investing more democratic, but it has also resulted in more sentiment-based investing. Market reactions based on news, social media, or market rumors can lead to sudden price movements.

    On the other hand, institutional investors are more aggressively seeking to optimize their risks and are often rebalancing their portfolios on a constant basis. Such nimbleness may be adding to market volatility in uncertain seasons.

    Is Volatility the ‘New Normal’?

    Volatility does seem unusually high, but one must be aware that market cycles of calmness and turmoil were present in markets at all times. The difference is in how often and how quickly markets oscillate, not in how much. In view of present structural realities, interconnectedness of markets globally, speed of information distribution, and complexity of market issues, one could expect increased average levels of market volatility.

    But this does not mean that markets will continue to be unstable. Stable periods will continue to be realized, particularly as economic clarity is gained. Volatility is a condition that can be considered a cycle in and of itself, as opposed to a state of crisis.

    What Volatility Means for Long-Term Investors
    A volatility

    Volatility does not have to pose a threat to long-term investors. On the contrary, it can provide opportunities to gain exposure to high-quality assets at better valuation levels. It has been observed that markets tend to overreact in short periods, while fundamentals are restored over time.

    The answer lies in discipline. Investors who are strategic about asset allocation, diversification, or long-term orientation have a better chance of riding the tide of fluctuating markets. Overwhelming reliance on impulse or judgment, as in panic selling or trending investment, could be counter-productive.

    Handling a More Volatile Market Environment

    Volatility is here to stay, and investors must learn to live with it. When faced with this situation, investors must learn to be ready to adapt to this reality as opposed to fighting against it. It is important to have clear return expectations and liquidity as well as occasionally reviewing portfolios.

    Instead, risk management, patience, and having an investment framework are more valuable than being able to predict market movements. In this aspect, volatility is no longer an adversary but an aspect that must be dealt with.

    Final Perspective

    In Market volatility can become more regular and more apparent as new structures emerge that shape market activity. Even though volatility can be unsettling, this by itself is not an undeniably bad thing. Informed and disciplined investors can learn how to not only survive but thrive during times of market volatility instead of being frightened by it.

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daniyasiddiquiEditor’s Choice
Asked: 27/12/2025In: ,

AI tool causes a clinical error

artificial intelligence regulationclinical decision support systemshealthcare law and ethicsmedical accountabilitymedical negligencepatient safety
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    AI in Healthcare: What Healthcare Providers Should Know Clinical AI systems are not autonomous. They are designed, developed, validated, deployed, and used by human stakeholders. A clinical diagnosis or triage suggestion made by an AI model has several layers before being acted upon. There is, thereRead more

    AI in Healthcare: What Healthcare Providers Should Know

    Clinical AI systems are not autonomous. They are designed, developed, validated, deployed, and used by human stakeholders. A clinical diagnosis or triage suggestion made by an AI model has several layers before being acted upon.

    There is, therefore, an underlying question:

    Was the damage caused by the technology itself, by the way it was implemented, or by the way it was used?

    The answer determines liability.

    1. The Clinician: Primary Duty of Care

    In today’s health care setup, health care providers’ decisions, even in those supported by AI, do not exempt them from legal liability.

    If a recommendation is offered by an AI and the following conditions are met by the clinician, then:

    • Accepts it without appropriate clinical judgment, or
    • Neglects obvious signs that go against the result produced by AI,

    So, in many instances, the liability may rest with the clinician. AI systems are not considered autonomous decision-makers but rather decision-support systems by courts.

    Legally speaking, the doctor’s duty of care for the patient is not relinquished merely because software was used. This is supported by regulatory bodies, including the FDA in the United States, which considers a majority of the clinical use of AI to be assistive, not autonomous.

    2. The Hospital or Healthcare Organization

    Healthcare providers can be held responsible for damage caused by system-level issues, for instance:

    • Lack of adequate training among staff
    • Poor incorporation of AI in clinical practices
    • Ignoring known limitations of the system or warnings about safety

    For instance, if an AI decision-support system is required by a hospital in terms of triage decisions but an accompanying guideline is lacking regarding under what circumstances an override decision by clinicians is warranted, then the hospital could be held jointly liable for any errors that occur.

    With the aspect of vicarious liability in place, the hospital can be potentially responsible for negligence committed through its in-house professionals utilizing hospital facilities.

    3. AI Vendor or Developer

    Under product liability or negligence, AI developers can be made responsible, especially if negligence occurs in relation to:

    • Inherently Flawed Algorithm/Design Issues in Models
    • Biased or poor quality training data
    • Lack of Pre-Deployment Testing
    • Lack of disclosure of known limitations or risks

    If an AI system is malfunctioning in a manner inconsistent with its approved use, market claims, legal liability could shift toward the vendor. This leaves developers open to legal liability in case their tools end up malfunctioning in a manner inconsistent with their approved use

    But vendors tend to mitigate any responsibility for liability by stating that the use of the AI system should be under clinical supervision, since it is advisory only. Whether this will be valid under any legal system is yet to be tested.

    4. Regulators & Approval Bodies (Indirect Role)

    The regulatory bodies are not responsible for liability pertaining to clinical mistakes, but regulatory standards govern liability.

    The World Health Organization, together with various regulatory bodies, is placing a mounting importance on the following:

    • Transparency and explainability
    • Human-in-loop decision making
    • Continuous monitoring of AI performance

    Non-compliance with legal standards may enhance the validity of legal action against hospitals or suppliers in the event of injuries.

    5. What If the AI Is “Autonomous”?

    This is where the law gets murky.

    This becomes an issue if an AI system behaves independently without much human interference, such as in cases of fully automated triage decisions or treatment choices. The existing liability mechanism becomes strained in this scenario because the current laws were never meant for software that can independently impact medical choices.

    Some jurists have argued for:

    • Contingent liability schemes
    • Mandatory Insurance for AI MitsuruClause Insurance for AI
    • New legal categorizations for autonomous medical technologies

    At least, in today’s world, most medical organizations do not put themselves at risk in this manner, as they do, in fact, mandate supervision by medical staff.

    6. Factors Judged by the Court for Errors Associated with AI

    In applying justice concerning harm caused by artificial intelligence, the courts usually consider:

    • Was the AI used for the intended purpose?
    • Was the practitioner prudent in medical judgment?
    • Was the AI system sufficiently tested and validated?
    • Were limitations well defined?
    • Was there proper training and governance in the organization?

    The absence or presence of AI may not be as crucial to liability but rather its responsible use.

    The Emerging Consensus

    The general world view is that AI does not replace responsibility. Rather, the responsibility is shared in the AI environment in the following ways:

    • Healthcare Organizations: Responsible for the governance & implementation
    • Suppliers of AI systems: liable for secure design and honest representation

    This shared responsibility model acknowledges that AI is not a value-neutral tool or an autonomous system it is a socio-technical system that is situated within healthcare practice.

    Conclusion

    Consequently, it is not only technology errors but also system errors. The issue of blame in assigning liability focuses not on pinning down whose mistake occurred but on making all those in the chain, from the technology developer to the medical practitioner, do their share.

    Until such time as laws catch up to define the specific role of autonomous biomedical AI, being responsible is a decidedly human task. There is no question about the best course in either safety or legal terms. Being human is the key. Keep the responsibility visible, traceable, and human.

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daniyasiddiquiEditor’s Choice
Asked: 27/12/2025In: ,

healthcare workers in the next decade

ai in healthcaredigital health literacyfuture of healthcarehealthcare innovationtelemedicine
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. Health Literacy in the Digital Age and Confidence in Technology On a basic level, healthcare workers must be digitally literate, meaning they can comfortably use EHRs, telemedicine platforms, mobile health applications, and digital diagnostic tools. Digital literacy goes beyond basic computer useRead more

    1. Health Literacy in the Digital Age and Confidence in Technology

    On a basic level, healthcare workers must be digitally literate, meaning they can comfortably use EHRs, telemedicine platforms, mobile health applications, and digital diagnostic tools.

    Digital literacy goes beyond basic computer use to involve or include the use and understanding of how digital systems store, retrieve, and then display patient information; recognition of limitations within those systems; and the efficient navigation of workflow through digital means. As global health systems, such as those guided by the World Health Organization, continue their focus on the need for digital transformation, their staff working at the front line of service must feel confident, rather than overwhelmed, by technologies.

    2. Data Interpretation and Clinical Decision Support Skills

    Health care professionals will be working increasingly with dashboards, alerts, predictive scores, and population health analytics. The new systems probably won’t be built by them, but they have to know how to interpret data meaningfully.

    Core competencies:

    • It enables you to understand trends, risk scores, and visual analytics.
    • The key: distinguishing between correlation and clinical causation.
    • Knowing when to trust the recommendations of automation and when to question it.

    For instance, a triage nurse that would have to review AI-generated risk alerts must be able to appraise whether the recommendation aligns with clinical context. Data literacy ensures technology enhances judgment rather than replaces it.

    3. AI Awareness and Human-in-the-Loop Decision Making

    Artificial Intelligence will increasingly support diagnostics, triage, imaging, and administrative workflows. Healthcare workers do not need to design algorithms, but they must understand what AI can and cannot do.

    Key competencies related to AI include:

    • Understanding AI Outputs, Confidence Scores, And Limitations
    • Recognizing possible biases in AI recommendations
    • Having responsibility for final clinical decisions

    For health systems, including the National Health Service, emphasis is placed on “human-in-the-loop” models where the clinicians remain responsible for the outcomes of patients, with AI acting only as a decision-support tool.

    4. Competency on Telemedicine and Virtual Care

    Remote care is no longer a choice. It is about teleconsultations, remote monitoring, and virtual follow-ups that are becoming routine.

    Health workers need to develop:

    • Effective virtual communication and bedside manner
    • Ability to evaluate patients without the need for physical examination
    • Ability to use remote monitoring devices and interpret incoming data

    A digital consultation requires different communication skills-clear questioning, active listening, and empathy-delivered through a screen rather than in person.

    5. Cybersecurity and Data Privacy Awareness

    With increased digital practices in healthcare, the risk of cybersecurity threats also grows. Data breaches and ransomware attacks can have a direct bearing on patient safety, as can misuse of patient data.

    Healthcare staff should know that:

    • Basic cybersecurity hygiene-security passwords, for example, and awareness of phishing
    • Safe handling of patients’ data across systems and devices
    • Legal and ethical responsibilities concerning confidentiality and consent

    Digital health regulations in many countries are increasingly holding individuals accountable, not just institutions, for failures in data protection.

    6. Interoperability and Systems Thinking

    Contemporary healthcare integrates data exchange among hospitals, laboratories, insurers, public health agencies, and national platforms. Health professionals must know how systems are connected.

    This includes:

    • awareness of shared records and data flows
    • Recognizing how an error in data entry propagates across systems
    • Care coordination across digital platforms

    Systems thinking helps clinicians appreciate the downstream impact of their digital actions on continuity of care and population health planning.

    7. Change Management and Continuous Learning Mindset

    Technology in the field of health is bound to grow very fast. The most important long-term skill for the future is the ability to adapt and learn continuously.

    • Healthcare workers should be comfortable with the
    • Regular system upgrades, including new tools
    • Continuous training and reskilling in the use of digital technology
    • Participate in feedback loops to help improve digital systems.

    Instead of considering technology to be a disruption, the future-ready professional views it as an evolving part of the clinical practice.

    8. Digital Ethics, Empathy, and Patient Engagement

    The more digital care becomes, the more, not less, important it is to maintain trust and human connection.

    The following competencies shall be developed for the healthcare workers:

    • Ethical judgment around digital consent and use of data
    • Competencies to describe digital instruments to patients in an easy-to-understand manner
    • Sensitivity to digital divides affecting elderly, rural, or underserved populations
    • Technology should enhance patient empowerment, not create new barriers to care.

    Final View

    During the next decade, the best health professionals will not be the ones who know most about technology but those who know how to work wisely with it. Digital skills will sit alongside clinical expertise, communication, and ethics as the core professional competencies.

    The future of healthcare needs digitally confident professionals who will combine human judgment with technological support to make the care safe, equitable, and truly human in an increasingly digital world.

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daniyasiddiquiEditor’s Choice
Asked: 27/12/2025In: ,

triage better than human clinicians

clinical decision supportdigital health technologyhealthcare ai evaluationhuman-ai collaborationmedical accuracy metricsmedical triage systems
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Can AI Diagnose or Triage Better Than Human Physicians? When it comes to specific, well-identified tasks, the capabilities of AI systems will meet or, in some instances, exceed those of human doctors. For instance, an AI system trained on a massive repository of images has shown remarkable sensitiviRead more

    Can AI Diagnose or Triage Better Than Human Physicians?

    When it comes to specific, well-identified tasks, the capabilities of AI systems will meet or, in some instances, exceed those of human doctors. For instance, an AI system trained on a massive repository of images has shown remarkable sensitivity in diagnosing diabetic retinopathy, cancers through radiological images, or skin lesions. The reason for the immense success of such a system is its ability to analyze millions of examples.

    AI-based solutions can quickly short-list patients in triage conditions based on their symptoms, vitals, past health issues, and other factors. In emergency or telemedicine environments, AI can point out critical patients (e.g., those with possible strokes or sepsis) much faster than the manual process in peak times.

    However, medical practice is more than pattern recognition. Clinicians have the ability to add context to pattern recognition. They possess the ability to think ethically, have empathy in their dealings, and be able to infer information that may not be evident from pattern recognition. Artificial systems lack in situations that lie outside their patterns or when people behave unconventionally.

    This leads to a situation where the best possible results are obtained when both AI and healthcare professionals collaborate as opposed to competing.

    Why ‘Better’ Is Context-Dependent

    AI can potentially do better than humans in:

    •  Functions Related to the Health Care Market
    • Interpretation based on images or
    • Early Risk Stratification and Notices

    Areas where humans excel over AI are:

    • Complex, multi-morbidity
    • Ethics in Decision-Making and Consentua

    What does interpreting patient narratives and social context mean?

    • Hence, the pertinent inquiry that arises is: Better at what, under what conditions, and with what safeguards?
    • Validation Methods of AI Capabilities in Diagnoses and Triage Procedures
      In diagnosing

    In order to be clinically trustworthy, AI systems must meet certain criteria that have been established by health regulators, authorities, and professionals. These criteria involve metrics that have been specifically defined in the domain.

    1. Clinical Accuracy Metrics

    These evaluate the frequency at which the correct conclusion is drawn by the AI.

    • Sensitivity (Recall): The power of a screening tool to identify patients with the condition.
    • Specificity: Capacity to exclude patients who are free from the condition

    The overall rate of correct predictions

    • Precision (Positive Predictive Value): The rate at which a positive prediction made by an AI is confirmed to be correct. Precision aims
    • Triage: Here, high sensitivity is especially important to avoid missed diagnoses of life-threatening illnesses.

    2. Area Under the Curve (AUC-ROC

    The Receiver Operating Characteristic (ROC) curve evaluates the ability of an AI model to separate conditions across different threshold values. A high AUC of 1.0 reveals outstanding discriminating capabilities, but an AUC of 0.5 would indicate purely random guessing. For most AI-based medical software, the goal may be to outperform experienced practitioners.

    3. Clinical Outcome Metrics

    • Accuracy is no guarantee. It is the patient outcomes that count.
    • Reduction in diagnostic delays
    • Higher rates of survival or recovery
    • More patients can be seen
    • Reduction in adverse events

    If an AI model is statistically correct but doesn’t lead to an improvement in outcomes, that particular AI model doesn’t have any practical use in

    4. Generalizability and Bias Metrics

    • AI must be effective for all people.
    • Performance by age, gender, and ethnicity
    • Difference in accuracy between various hospitals or locations
    • Stability in relation to actual instances versus training data

    There could be discrepancies in clinical judgments in the case of failure.

    5. Explainability & Transparency

    • Doctors also need to know why a recommendation was made.
    • Feature importance or decision reasoning
    • Ability to audit output
    • A study at Memorial University of Newfoundland compared

    Approvals of Clinical AI by Regulators like the US FDA have recently been focusing on explainability.

    6. Workflow and Efficiency Metrics

    In triage, in particular, quickness and usability count.

    • Time saved per case
    • Reduction of Clinician Cognitive Load
    • Ease of integration in Electronic Health Records (EHRs)
    • Adoption and trust among professionals

    If an AI solution slows down operations or is left untouched by employees, it does no good.

    The Current Consensus

    Computers designed to recognize patterns may be as good as, if not better than, humans in making diagnoses in narrowly circumscribed tasks if extensive structured datasets are available. But they lack comprehensive clinical reasoning, ethics, and accountabilities.

    Care providers, like the UK’s NHS, as well as international organizations, the World Health Organization, for example, have recommended human-in-the-loop systems, where the responsibility lies with the human when AI decisions are involved.

    Final Perspective

    The AI is “neither better nor worse” compared to human clinicians in a general way. Rather, AI is better at particular tasks in a controlled environment when clinical and outcome criteria are rigorously met. The future role of diagnosis and triage can be found in what has come to be known as collaborative intelligence.

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