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

social media-led health

digital health awarenessevidence-based medicinehealth misinformationonline health trendssocial media and healthviral health hacks
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Why Health Hacks Spread So Fast Online 1. They Offer Fast Results People look for solutions that happen overnight. “Do this before bedtime,” or “One spoonful a day” is the hope for quick and effortless improvement. 2. They Feel Personal and Relatable Creators give personal anecdotes: “this helped meRead more

    Why Health Hacks Spread So Fast Online

    1. They Offer Fast Results

    People look for solutions that happen overnight. “Do this before bedtime,” or “One spoonful a day” is the hope for quick and effortless improvement.

    2. They Feel Personal and Relatable

    Creators give personal anecdotes: “this helped me with insomnia” or “this resolved digestive issues”. They ring true, even if they lack medical validity.

    3. Algorithms are Rewarding Engagement over Accuracy

    Social media sites are designed to promote information that is emotionally engaging, surprising, or visually striking. Accuracy and medical peer review are not part of the algorithm for ranking information.

    When Social Media Health Hacks can Actually Help

    Some viral health tips are effective not because of their originality but because of the promotion of healthy habits through them.

    Examples that often have some benefit:

    • Drinking more water
    • Walking after meals
    • Minimizing late-night screen use
    • Engaging in breathing exercise
    • Consuming more fruits and vegetables

    These are not novel medical findings, but rather well-known lifestyle tips in trendy packaging. These would likely be harmless to, and possibly of some marginal health benefit for, those in good health.

    Where Health Hacks Go wrong

    1. Oversimplification of Complex Health Issues

    Conditions such as diabetes, anxiety, hormonal imbalance, or gastrointestinal disorders are quite complicated. Nothing, not even a particular food or supplement, can cure these conditions once for all.

    2. Lack of Scientific Evidence

    Most hacks are anecdotal, not peer-reviewed. What worked for a person is not necessarily going to work for someone else. Often, a hack might actually prove dangerous for a certain individual.

    3. “One Size

    Bodies react diversely according to age, genetics, health conditions, and lifestyle choices. What may work as a hack for some people could be useless or even harmful to others.

    4. Hidden Risks

    Some viral trends promote:

    • Over-the-counter supplements
    • Extreme Fasting
    • Dangers of Non-Clin
    • Misuse of medicines
    • Refusal to seek medical attention.

    These can aggravate health problems or hamper receiving proper diagnoses.

    The Role of Misinformation

    Health information can spread easily online since it is usually unreflected and uncorrected in real time. Online influencers do not have to reveal:

    • Medical Qualification
    • Conflicts of interest
    • Sponsored content

    This means that individuals could end up trusting health advice from people who lack medical knowledge.

    How to Evaluate a Health Hack Before Giving It a Try

    Think about these few basic questions:

    • Is there credible medical literature to support it?
    • Try to find advice from physicians, hospitals, or health organizations.
    • Does this sound too good to be true?

    Be alert if promises of quick and guaranteed success are being made.

    Is it safe for most people?

    “Anything involving extreme restriction, high doses, or medical claims should be handled with caution.”

    • Does it discourage professional care?

    Examples that involve replacing doctors or prescription drugs can be dangerous.

    How to Use Social Media for Health in a Balanced Way

    Social Media could be a beginning source, not a definitive one. It could inspire individuals about health awareness, talking, and changes. It is not meant for replacing health counseling, diagnosing, and treatments.

    “The safest approach is:” Embark on social media for inspiration

    In order to avoid You should discuss any changes with a medical professional, particularly if you have any existing conditions

    In Summary

    In Health hacks on social media are neither good nor bad. Some are perfectly positive and very useful. But most of the time, they oversimplify very complicated medical problems or are simply inaccurate. Being smart and smart-erring on the side of caution is the answer. Health is a personal thing, and nothing will ever replace common sense.

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

AI assist health care

ai in medicinedigital healthhealthcare technologymedical innovationpersonalized medicineprecision medicine
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. Faster and More Accurate Diagnosis AI models are capable of analyzing medical images, such as X-rays, CT, MRI, and pathology slides, in seconds. These models are trained on millions of cases, making them detect potential signs of ailments such as cancer, tuberculosis, strokes, and bone fracturesRead more

    1. Faster and More Accurate Diagnosis

    AI models are capable of analyzing medical images, such as X-rays, CT, MRI, and pathology slides, in seconds. These models are trained on millions of cases, making them detect potential signs of ailments such as cancer, tuberculosis, strokes, and bone fractures even in their nascent stages, in many cases, prior to the onset of symptoms.

    To doctors, it translates to early detection, reduced missed diagnoses, and rapid decision-making regarding treatments.

    2. Clinical Decision Support

    Computers analyze huge amounts of data for patients, like medical records, lab tests, vital signs, and treatment response, and suggest appropriate courses of treatment for doctors. For instance, computers can alert doctors to risky patients or identify lab tests that fall beyond the norm.

    It minimizes human error and assists medical professionals in making confident decisions, especially in hectic hospitals.

    3. Predictive & Preventive Care

    Rather than responding to an illness once it has progressed, AI is able to predict problems before they occur. It is able to detect patients that have the possibility of developing problems such as diabetes, heart conditions, infections, and readmission to their hospital.

    This allows medical teams to step in early with lifestyle advice, changes in medication, or increased monitoring, thereby turning the focus of healthcare from a reactive to a prophylactic mode.

    4. Remote Monitoring and Telehealth

    Devices as well as mobile applications monitor vital signs such as heart rate, oxygen level, blood pressures, and glucose levels. As soon as any abnormality is found, notifications are delivered to doctors.

    This is especially important in elderly patients, in dealing with chronic conditions, as well as in rural areas where access may be restricted to hospitals.

    5. Administrative Efficiency

    Healthcare requires document-intensive activities such as appointments, billing, insurance, and reporting. One area where AI can benefit is in the elimination of paper-intensive tasks that doctors perform.

    This leads to reduced operations cost and an enhanced patient experience.

    What Is Personalized Medicine?

    Personalized medicine, also known as precision medicine, is a model that involves tailoring medical treatment for an individual, unlike general treatment for all.

    1. Beyond “One Size Fits

    Conventional medicine treats patients with the same diagnosis alike. Personalized medicine understands that each person has his or her own biology. Many variables, including genetics, lifestyle, surroundings, age, and comorbid conditions, can affect the progression of the disease as well as the course of treatment for the patient.

    2. Role of AI in Personalization

    “Artificial Intelligence examines a variety of data at once such as genetic, lab, imaging, medical histories, and even lifestyle patterns. On the basis of all these, Artificial Intelligence assists a doctor in choosing”:

    • The drug that works best
    • The appropriate dosage
    • Factor that could have led to an increase in abnormal

    This will lessen errors in trial-and-error prescription and reduce adverse effects.

    3. More Favorable Outcomes and Safer Treatment

    For instance, in cancer treatment, a personal approach to medicine enables a patient to be advised on the type of medication that will be most effective on a particular type of cancer in their body. For patients suffering from diabetes and/or high blood pressure, a personal approach can be used depending on the way the patient’s body reacts.

    Patients enjoy benefits such as rapid recovery, reduced complication, and improved quality of life.

    4. Patient Centered

    Personalized medicine gives the patient the power of participation in his or her treatment plan. His or her treatment plan will match his or her needs and preferences. This does not focus on the treatment of the symptoms.

    How AI and Personalized Medicine Work Together

    How AI and Personalized Medicine Work Together

    When AI and personalized medicine come together, healthcare becomes predictive, precise, and patient-focused. AI provides the analytical intelligence, while personalized medicine ensures that insights are applied in a way that respects individual differences.

    In simple terms:

    • AI finds patterns in data

    • Personalized medicine uses those patterns to treat the right patient, at the right time, with the right care

    In Summary

    The AI is revolutionizing the medical industry by providing better diagnostic tools, risk prediction systems, assistant functions for doctors, and easier administrative tasks. Personalized medicine takes it further by personalizing treatment so that medications are more effective and safer for consumption. Both can be considered the start of smarter, more humane, and more efficient health systems.

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

fraudulent health cards in Lucknow to ...

fake health benefithealth card fraudhealth system exploitationinsurance fraudlucknow fraud casemedical scam
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. Selling Personal and Demographic Data One of the main reasons this fraud was able to succeed is because of unauthorized access to Aadhaar and demographic details. The accused allegedly collected personal details of individuals, at times without their knowledge, through middlemen, local agents, orRead more

    1. Selling Personal and Demographic Data

    One of the main reasons this fraud was able to succeed is because of unauthorized access to Aadhaar and demographic details. The accused allegedly collected personal details of individuals, at times without their knowledge, through middlemen, local agents, or informal networks that worked by exchanging information. In some instances, beneficiaries were deceived under false pretensions into providing documents in order to receive government benefits or signing up for a particular scheme.

    2. Enrollment and Verification Gaps Exploitation

    Most of the health schemes nowadays depend on a digital enrollment system, but verifications in most cases are semi-automated. The accused got away with fraud in areas where either physical verification was weak or hurried, or where there were a very large number of enrollments. In such cases, they would manipulate documents and upload them or re-use genuine data to create health cards that passed the system’s verification.

    3. Collusion and Insider Knowledge

    Frauds involving such processes rarely succeed without insider knowledge. The arrested individuals reportedly knew about backend processes, like how the applications move from submission to approval. This helped them in bypassing some red flags, delaying scrutinies, or submitting them in batches so as not to be noticed.

    4. Utilization of Nominee or Proxy Beneficiaries

    In many cases, fictitious identities or proxy beneficiaries were created. Such cards were then utilized at empanelled hospitals for raising claims for treatments that never took place. At times, genuine patients were shown procedures they never received, while in other cases, entirely fictitious admissions were created in the system.

    5. Poor Real-time Claim Monitoring

    Although claims are recorded electronically, there is no uniform use of real-time analytics or anomaly detection. This enabled the suspicious patterns, like repeated claims from the same facilities or unusually high-value treatments, to go undetected until law enforcement stepped in to take action.

    6. Lack of Beneficiary Awareness

    Most of the genuine beneficiaries are unaware as to how and when their health cards are used. The absence of instant alerts-through SMS or apps-means fraudulent usage of their identity did not raise immediate alarms. This delayed complaints and the perpetuation of fraud.

    7. Reactive Rather Than Preventive Controls

    This racket was brought to light through intelligence inputs and focused investigations, rather than automatic alerts from the systems. This highlights the fact that while the systems exist, their enforcement becomes reactive in most cases—post-financial leakage, rather than upfront.

    Broader Takeaway

    This certain incident has underlined that digital governance is only as strong as the weakest point of control. While technology allows scale and speed, it has to be duly supported by strong audits, beneficiary communication, periodic verification, and strict accountability. The arrests in Lucknow also point out that corrective steps and warnings go hand in hand with continuous strengthening of the system to protect the public welfare funds.

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daniyasiddiquiEditor’s Choice
Asked: 26/12/2025In:

generative AI models

artificial intelligencedeep learningfine-tuningmachine learningpre-trainingtransfer learning
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Understanding the Two Model Types in Simple Terms Both generative and predictive AI models learn from data at the core. However, they are built for very different purposes. Generative AI models are designed to create content that had not existed prior to its creation. Predictive models are designedRead more

    Understanding the Two Model Types in Simple Terms

    Both generative and predictive AI models learn from data at the core. However, they are built for very different purposes.

    • Generative AI models are designed to create content that had not existed prior to its creation.
    • Predictive models are designed to forecast or classify outcomes based on existing data.

    Another simpler way of looking at this is:

    • Generative models generate something new.
    • Predictive models make decisions or estimates by deciding to do something or estimating something.

    What are Generative AI models?

    Generative AI models learn from the underlying patterns, structure, and relationships in data to produce realistic new outputs that resemble the data they have learned from.

    Instead of answering “What is likely to happen?”, they answer:

    • “What could be made possible?
    • What would be a realistic answer?
    • “How can I complete or extend this input?

    These models synthesize completely new information rather than simply retrieve already existing pieces.

    Common Examples of Generative AI

    • Text Generations and Conversational AI
    • Image and Video creation
    • Music and audio synthesis
    • Code generation
    • Document summarization, rewriting

    When you ask an AI to write an email for you, design a rough idea of the logo, or draft code, you are basically working with a generative model.

    What is Predictive Modeling?

    Predictive models rely on the analysis of available data to forecast an outcome or classification. They are trained on recognizing patterns that will generate a particular outcome.

    They are targeted at accuracy, consistency, and reliability, rather than creativity.

    Predictive models generally answer such questions as:

    • “Will this customer churn?”
    • Q: “Is this transaction fraudulent?
    • “What will sales be next month?”
    • “Does this image contain a tumor?”

    They do not create new content, but assess and decide based on learned correlations.

    Key Differences Explained Succinctly

    1. Output Type

    Generative models create new text, images, audio, or code. Predictive models output a label, score, probability, or numeric value.

    2. Aim

    Generative models aim at modeling the distribution of data and generating realistic samples. Predictive models aim at optimizing decision accuracy for a well-defined target.

    3. Creativity vs Precision

    Generative AI embraces variability and diversity, while predictive models are all about precision, reproducibility, and quantifiable performance.

    4. Assessment

    Evaluations of generative models are often subjective in nature-quality, coherence, usefulness-whereas predictive models are objectively evaluated using accuracy, precision, recall, and error rates.

    A Practical Example

    Let’s consider a sample insurance company.

    A generative model is able to:

    • Create draft summaries of claims
    • Generate customer responses
    • Explain policy details in plain language

    A predictive model can:

    • Predict claim fraud probability
    • Estimate claim settlement amounts
    • Risk classification of claims

    Both models use data, but they serve entirely different functions.

    How the Training Approach Differs

    • The generative models learn by trying to reconstruct data-sometimes instances of data, like an image, or parts of data, like the next word in a sentence.
    • Predictive models learn by mapping input features to a known output: predict yes/no, high/medium/low risk, or numeric value.
    • This difference in training objectives leads to very different behaviours in real-world systems.

    Why Generative AI is getting more attention

    Generative AI has gained much attention because it:

    • Allows for natural human–computer interaction
    • Automates content-heavy workflows
    • Creative, design, and communication support
    • Acts as an intelligence layer that is flexible across many tasks

    However, generative AI is mostly combined with predictive models that will make sure control, validation, and decision-making are in place.

    When Predictive Models Are Still Essential

    Predictive models remain fundamental when:

    • Decisions carry financial, legal, or medical consequences.
    • Outputs should be explainable and auditable.
    • It should operate consistently and deterministically.

    Compliance is strictly regulated. In many mature systems, generative models support humans, while predictive models make or confirm final decisions.

    Summary

    The end The generative AI models focus on the creation of new and meaningful content, while predictive models focus on outcome forecasting and decision-making. Generative models will bring flexibility and creativity, while predictive models will bring precision and reliability. Together, they provide the backbone of contemporary AI-driven systems, balancing innovation with control.

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daniyasiddiquiEditor’s Choice
Asked: 26/12/2025In:

pre-training vs fine-tuning

artificial intelligencedeep learningfine-tuningmachine learningpre-trainingtransfer learning
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    “The Big Picture: Why Two Training Stages Exist” Nowadays, training of AI models is not done in one step. In most cases, two phases of learning take place. These two phases of learning are known as pre-training and fine-tuning. Both phases have different objectives. One can consider pre-training toRead more

    “The Big Picture: Why Two Training Stages Exist”

    Nowadays, training of AI models is not done in one step. In most cases, two phases of learning take place. These two phases of learning are known as pre-training and fine-tuning. Both phases have different objectives.

    One can consider pre-training to be general education, and fine-tuning to be job-specific training.

    Definition of Pre-Training

    This is the first and most computationally expensive phase of an AI system’s life cycle. In this phase, the system is trained on very large and diverse datasets so that it can infer general patterns about the world from them.

    For language models, it would mean learning:

    • Grammar and sentence structure
    • Lexical meaning relationships
    • Common facts

    Conversations and directions typically follow this pattern:

    Significantly, during pre-training, the training of the model does not focus on solving a particular task. Rather, it trains the model to predict either missing values or next values, such as the next word in an utterance, and in doing so, it acquires a general idea of language or data.

    This stage may require:

    • Large datasets (Terabytes of Data)
    • Strong GPUs or TPUs
    • Weeks or months of training time

    After the pre-training process, the result will be a general-purpose foundation model.

    Definition of Fine-Tuning

    Fine-tuning takes place after a pre-training process, aiming at adjusting a general model to a particular task, field, or behavior.

    Instead of having to learn from scratch, the model can begin with all of its pre-trained knowledge and then fine-tune its internal parameters ever so slightly using a far smaller dataset.

    • Fine-tuning is performed in
    • Enhance accuracy for a specific task
    • Assist alignment of the model’s output with business and ethical imperatives
    • Train for domain-specific language (medical, legal, financial, etc.)
    • Control tone, format, and/or response type

    For instance, a universal language understanding model may be trained to:

    • Answer medical questions more safely
    • Claims classification
    • Aid developers with code
    • Follow organizational policies

    This stage is quicker, more economical, and more controlled than the pre-training stage.

    Main Points Explained Clearly

    Conclusion

    General intelligence is cultivated using pre-training, while specialization in expert knowledge is achieved through

    Data

    It uses broad, unstructured, and diverse data for pre-training. Fine-tuning requires curated, labeled, or instruction-driven data.

    Cost and Effort

    The pre-training process involves very high costs and requires large AI labs. However, fine-tuning is relatively cheap and can be done by enterprises.

    Model Behavior

    After pre-training, it knows “a little about a lot.” Then, after fine-tuning, it knows “a lot about a little.”

    A Practical Analogy

    Think of a doctor.

    • “Pre-training” is medical school, wherein the doctor acquires education about anatomy, physiology, and general medicine.
    • Fine-tuning refers to specialization. It may include specialties such as cardiology or
    • Specialization is impossible without pre-training. Fine-tuning is necessary for the doctor to remain specialist.

    Why Fine-Tuning Is Significant for Real-World Systems

    Raw pre-trained models aren’t typically good enough in production contexts. There’s a benefit to fine-tuning a:

    • Decrease hallucinations in critical domains
    • Enhance consistency and reliability
    • synchronize results with legal stipulations
    • Adapt to local language, work flows, and terms

    It is even more critical within industries such as the medical sector, financial sectors, and government institutions that require accuracy and adherence.

    Fine-Tuning vs Prompt Engineering

    It should be noted that fine-tuning is not the same as prompt engineering.

    • Prompt engineering helps to steer the model’s conduct by providing more refined instructions, without modifying the model.
    • No, fine-tuning simply adjusts internal model parameters, making it behave in a predictable manner for all inputs.
    • Organizations begin their journey of machine learning tasks from prompt engineering to fine-tuning when greater control is needed.

    Whether a fine-tuning task can replace

    No. Fine-tuning is wholly reliant upon the knowledge derived during pre-trained models. There is no possibility of deriving general intelligence using fine-tuning with small data sets—it only molds and shapes what already exists or is already present.

    In Summary

    Pre-training represents the foundation of understanding in data and language that AI systems have, while fine-tuning allows them to apply this knowledge in task-, domain-, and expectation-specific ways. Both are essential for what constitutes the spine of the development of modern artificial intelligence.

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daniyasiddiquiEditor’s Choice
Asked: 26/12/2025In:

foundation models differ from task-sp ...

ai modelsartificial intelligencedeep learningfoundation modelsmachine learningmodel architecture
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    The Meaning of Ground From a higher perspective, the distinction between foundation models and task-specific AI models is based on scope and purpose. In other words, foundation models constitute general intelligence engines, while task-specific models have a singular purpose accomplishing a single tRead more

    The Meaning of Ground

    From a higher perspective, the distinction between foundation models and task-specific AI models is based on scope and purpose. In other words, foundation models constitute general intelligence engines, while task-specific models have a singular purpose accomplishing a single task.

    Foundation models might be envisioned as highly educated generalists, while task-specific models might be considered specialists trained to serve only one role in society.

    What Are Foundation Models?

    Foundation models are large-scale AI models. They require vast and diverse data sets. These data sets involve various domains like language, images, code, audio, and structure. Foundation models are not trained on a fixed task. They learn universal patterns and then convert them into task-specific models.

    Once trained, the same foundation model can be applied to the following tasks:

    • Text generation
    • Question Answering
    • Summar
    • Translation
    • Image understanding
    • Code assistance
    • Data analysis

    “These models are ‘ foundational’ because a variety of applications are built upon these models using a prompt, fine-tuning, or a light-weight adapter. ”

    What Are Task-Specific AI Models?

    The models are trained using a specific, narrow objective. Models are built, trained, and tested based on one specific, narrowly defined task.

    These include:

    • An email spam classifier
    • A face recognition system.
    • Medical Image Tumor Detector
    • A credit default prediction model
    • A speech-to-text engine for a given language

    These models are not meant for generalization for a domain other than their use case. For any domain other than their trained tasks, their performance abruptly deteriorates.

    Differences Explained in Simple Terms

    1. Scope of Intelligence

    Foundation models generalize the learned knowledge and can perform a large number of tasks without needing additional training. Task-specific models specialize in a single task or a single specific function and cannot be readily adapted or applied to other tasks.

    2. Training Methodology

    Foundation models are trained once on large datasets and are computationally intensive. Task-specific models are trained on smaller datasets but are specific to the task they are meant to serve.

    3. Reusability & Adapt

    An existing foundation model can be easily applied to different teams, departments, or industries. In general, a task-specific model will have to be recreated or retrained for each new task.

    4. Cost and Infrastructure

    Nonetheless, training a foundation model is costly but efficient in the use of models since they accomplish multiple tasks. Training task-specific models is rather inexpensive but turns costly if multiple models have to be developed.

    5. Performance Characteristics

    Task-specific models usually perform better than foundation models on a specific task. But for numerous tasks, foundation models provide “good enough” solutions that are much more desirable in practical systems.

    Actual Example

    Consider a hospital network.

    A foundation model can:

    1. Generate

    • Summarize patient files
    • Respond to questions from clinicians.
    • Create discharge summaries
    • Translation of medical records
    • Provide help regarding coding and billing questions

    Task-specific models could:

    • Pneumonia identification from chest X-rays alone
    • Both are important, but they are quite different.

    Why Foundation Models Are Gaining Popularity

    Organisations have begun to favor foundation models because they:

    • Cut the need for handling scores of different models
    • Accelerate adoption of AI solutions by other departments in
    • Allow fast experimentation with prompts over having to retrain
    • Support multimodal workflows (text + image + data combined)

    This has particular importance in business, healthcare, finance, and e-governance applications, which need to adapt to changing demands.

    Even when task-specific models are still useful

    Although foundation models have become increasingly popular, task-specific models continue to be very important for:

    • Approvals need to be deterministic
    • Very high accuracy is required for one task
    • Latency and compute are very constrained.
    • The job deals with sensitive or controlled data

    In principle many existing mature systems would employ foundation models for general intelligence and task-specific models for critical decision-making.

    In Summary

    Foundation models add the ingredient of width or generic capability with scalability and adaptability. Task-specific models add the ingredient of depth or focused capability with efficiency. Contemporary AI models and applications increasingly incorporate the best aspects of the first two models.

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