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

Can AI systems diagnose or triage better than human clinicians? What metrics validate this?

triage better than human clinicians

clinical decision supportdigital health technologyhealthcare ai evaluationhuman-ai collaborationmedical accuracy metricsmedical triage systems
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice
    Added an answer on 27/12/2025 at 1:28 pm

    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: 23/11/2025In: Technology

How will AI agents reshape daily digital workflows?

l AI agents reshape daily digital wor ...

agentic-systemsai-agentsdigital-productivityhuman-ai collaborationworkflow-automation
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice
    Added an answer on 23/11/2025 at 2:26 pm

    1. From “Do-it-yourself” to “Done-for-you” Workflows Today, we switch between: emails dashboards spreadsheets tools browsers documents APIs notifications It’s tiring mental juggling. AI agents promise something simpler: “Tell me what the outcome should be I’ll do the steps.” This is the shift from mRead more

    1. From “Do-it-yourself” to “Done-for-you” Workflows

    Today, we switch between:

    • emails

    • dashboards

    • spreadsheets

    • tools

    • browsers

    • documents

    • APIs

    • notifications

    It’s tiring mental juggling.

    AI agents promise something simpler:

    • “Tell me what the outcome should be I’ll do the steps.”

    This is the shift from

    manual workflows → autonomous workflows.

    For example:

    • Instead of logging into dashboards → you ask the agent for the final report.

    • Instead of searching emails → the agent summarizes and drafts responses.

    • Instead of checking 10 systems → the agent surfaces only the important tasks.

    Work becomes “intent-based,” not “click-based.”

    2. Email, Messaging & Communication Will Feel Automated

    Most white-collar jobs involve communication fatigue.

    AI agents will:

    • read your inbox

    • classify messages

    • prepare responses

    • translate tone

    • escalate urgent items

    • summarize long threads

    • schedule meetings

    • notify you of key changes

    And they’ll do this in the background, not just when prompted.

    Imagine waking up to:

    • “Here are the important emails you must act on.”

    • “I already drafted replies for 12 routine messages.”

    • “I scheduled your 3 meetings based on everyone’s availability.”

    No more drowning in communication.

     3. AI Agents Will Become Your Personal Project Managers

    Project management is full of:

    • reminders

    • updates

    • follow-ups

    • ticket creation

    • documentation

    • status checks

    • resource tracking

    AI agents are ideal for this.

    They can:

    • auto-update task boards

    • notify team members

    • detect delays

    • raise risks

    • generate progress summaries

    • build dashboards

    • even attend meetings on your behalf

    The mundane operational “glue work” disappears humans do the creative thinking, agents handle the logistics.

     4. Dashboards & Analytics Will Become “Conversations,” Not Interfaces

    Today you open a dashboard → filter → slice → export → interpret → report.

    In future:

    You simply ask the agent.

    • “Why are sales down this week?”
    • “Is our churn higher than usual?”
    • “Show me hospitals with high patient load in Punjab.”
    • “Prepare a presentation on this month’s performance.”

    Agents will:

    • query databases

    • analyze trends

    • fetch visuals

    • generate insights

    • detect anomalies

    • provide real explanations

    No dashboards. No SQL.

    Just intention → insight.

     5. Software Navigation Will Be Handled by the Agent, Not You

    Instead of learning every UI, every form, every menu…

    You talk to the agent:

    • “Upload this contract to DocuSign and send it to John.”

    • “Pull yesterday’s support tickets and group them by priority.”

    • “Reconcile these payments in the finance dashboard.”

    The agent:

    • clicks

    • fills forms

    • searches

    • uploads

    • retrieves

    • validates

    • submits

    All silently in the background.

    Software becomes invisible.

    6. Agents Will Collaborate With Each Other, Like Digital Teammates

    We won’t just have one agent.

    We’ll have ecosystems of agents:

    • a research agent

    • a scheduling agent

    • a compliance-check agent

    • a reporting agent

    • a content agent

    • a coding agent

    • a health analytics agent

    • a data-cleaning agent

    They’ll talk to each other:

    • “Reporting agent: I need updated numbers.”
    • “Data agent: Pull the latest database snapshot.”
    • “Schedule agent: Prepare tomorrow’s meeting notes.”

    Just like teams do except fully automated.

     7. Enterprise Workflows Will Become Faster & Error-Free

    In large organizations government, banks, hospitals, enterprises work involves:

    • repetitive forms

    • strict rules

    • long approval chains

    • documentation

    • compliance checks

    AI agents will:

    • autofill forms using rules

    • validate entries

    • flag mismatches

    • highlight missing documents

    • route files to the right officer

    • maintain audit logs

    • ensure policy compliance

    • generate reports automatically

    Errors drop.

    Turnaround time shrinks.

    Governance improves.

     8. For Healthcare & Public Sector Workflows, Agents Will Be Transformational

    AI agents will simplify work for:

    • nurses

    • doctors

    • administrators

    • district officers

    • field workers

    Agents will handle:

    • case summaries

    • eligibility checks

    • scheme comparisons

    • data entry

    • MIS reporting

    • district-wise performance dashboards

    • follow-up scheduling

    • KPI alerts

    You’ll simply ask:

    • “Show me the villages with overdue immunization data.”
    • “Generate an SOP for this new workflow.”
    • “Draft the district monthly health report.”

    This is game-changing for systems like PM-JAY, NHM, RCH, or Health Data Lakes.

     9. Consumer Apps Will Feel Like Talking To a Smart Personal Manager

    For everyday people:

    • booking travel

    • managing finances

    • learning

    • tracking goals

    • organizing home tasks

    • monitoring health

    • …will be guided by agents.

    Examples:

    • “Book me the cheapest flight next Wednesday.”

    • “Pay my bills before due date but optimize cash flow.”

    • “Tell me when my portfolio needs rebalancing.”

    • “Summarize my medical reports and upcoming tests.”

    • Agents become personal digital life managers.

    10. Developers Will Ship Features Faster & With Less Friction

    Coding agents will:

    • write boilerplate

    • fix bugs

    • generate tests

    • review PRs

    • optimize queries

    • update API docs

    • assist in deployments

    • predict production failures

    • Developers focus on logic & architecture, not repetitive code.

    In summary…

    • AI agents will reshape digital workflows by shifting humans away from clicking, searching, filtering, documenting, and navigating and toward thinking, deciding, and creating.

    They will turn:

    • dashboards → insights

    • interfaces → conversations

    • apps → ecosystems

    • workflows → autonomous loops

    • effort → outcomes

    In short,

    the future of digital work will feel less like “operating computers” and more like directing a highly capable digital team that understands context, intent, and goals.

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daniyasiddiquiEditor’s Choice
Asked: 15/10/2025In: Education, Technology

How can AI assist rather than replace teachers?

AI assist rather than replace teacher

ai in educationclassroom innovationedtecheducaion technologyhuman-ai collaborationteacher support
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice
    Added an answer on 15/10/2025 at 12:24 pm

    What can the AI do instead of replacing teachers? The advent of Artificial Intelligence (AI) in education has sparked both excitement and fear. Teachers wonder — will AI replace teachers? But the truth is, AI has its greatest potential not in replacing human teachers, but assisting them. When used sRead more

    What can the AI do instead of replacing teachers?

    The advent of Artificial Intelligence (AI) in education has sparked both excitement and fear. Teachers wonder — will AI replace teachers? But the truth is, AI has its greatest potential not in replacing human teachers, but assisting them. When used strategically, AI can make teachers more effective, more customized, and more creative in their work, so that they can focus on the things computers can’t do — empathy, motivation, and relating to individuals.

    Let us observe how AI can assist rather than substitute teachers in the new classrooms of today.

     1. Personalized Instruction for All Pupils

    • Every pupil has a distinct learning style — some learn fast, while others need more time or instructions. With AI, teachers can know such differences in learning in real time.
    • Adaptive learning software reviews the way in which students interact with content — how long on a question, what they get wrong, or what they’re having difficulty with.
    • Based on that, the system slows down or suggests more practice.
    • For instance, AI systems like Khanmigo (the artificial intelligence tutor from Khan Academy) or Century Tech allow teachers to track individual progress and view who needs additional support or challenge.

     Human edge: Educators then use this data to guide interventions, provide emotional support, or adjust strategy — stuff AI doesn’t understand or feel.

    2. Reducing Administrative Tasks

    Teachers waste their time grading assignments, creating materials, or composing reports — activities that steal time from teaching.

    AI can handle the drudgework:

    • Grading assistance: AI automatically grades objective tests (e.g., multiple choice or short answer).
    • Lesson planning: AI apps can create sample lesson plans or quizzes for a topic or skill.
    • Progress tracking: AI dashboards roll together attendance, grades, and progress in learning, so instructors can focus on strategy and not spreadsheets.
    • Teacher benefit: Saving paperwork time, instructors have more one-on-one time with students — listening, advising, and encouraging inquiry.

     3. Differentiated Instruction Facilitation

    • In a single classroom, there can be advanced students, average students, and struggling students with basic skills. AI can offer differentiated instruction automatically by offering customized materials to every learner.
    • For example, AI can recommend reading passages of different difficulty levels but on a related topic to ensure all of them contribute to class discussions.
    • For language learning, AI is able to personalize practice exercises in pronunciation or grammar practice to the level of fluency of the student.

     Human benefit: Teachers are able to use these learnings to put students in groups so they can learn from each other, get group assignments, or deliver one-on-one instruction where necessary.

     4. Overcoming Language and Accessibility Barriers

    • Artificially intelligent speech recognition and translation software (e.g., Microsoft’s Immersive Reader or Google’s Live Transcribe) aid multilingual or special-needs students to fully participate in class.
    • Text-to-speech and speech-to-text software helps hearing loss or dyslexia students.
    • AI translation allows non-native speakers to hear classes in real-time.

     Human strength: Educators are still the bridge — not only translating words, but also context, tone, and feeling — and making it work for inclusion and belonging.

    5. Data-Driven Insights for Better Teaching

    • Computer systems can look across patterns of learning over the course of a class — perhaps seeing that the majority of students had trouble with a certain concept. Teachers can then respond promptly by adjusting lessons or re-teaching to stop misunderstandings from spreading.
    • AI doesn’t return grades — it returns patterns.
    • Teachers can use them to guide teaching approach, pace, and even classroom layout.

    Human edge: AI gives us data, but only educators can take that and turn it into knowledge — when to hold, when to move forward, and when to just stop and talk.

     6. Innovative Co-Teaching Collaborator

    • AI can serve as a creative brainstorming collaborator for instructors.
    • Generative AI (Google Gemini or ChatGPT) can be leveraged by educators to come up with examples, analogies, or ideas for a project within seconds.
    • AI can replicate debate opponents or generate practice essays for class testing.

    Human strength: Teachers infuse learning with imagination, moral understanding, and a sense of humor — all out of the reach of algorithms.

     7. Emotional Intelligence and Mentorship — The Human Core

    • The most significant difference, perhaps, is this one: AI lacks empathy. It can simulate feeling in voice or words but never feels compassion, enthusiasm, or concern.
    • Teachers don’t just teach facts — they also give confidence, character, and curiosity. They notice when a child looks blue, when a student is off task, or when a class needs to laugh at more than one more worksheet.

    AI can’t replace that. But it can amplify it — releasing teachers from soul-crushing drudgery and giving them real-time feedback, it allows them to remain laser-sharp on what matters most: being human with children.

    8. The Right Balance: Human–AI Collaboration

    The optimal classroom of the future will likely be hybrid — where data, repetition, and adaptation are handled by AI, but conversation, empathy, and imagination are crafted by teachers.

    In balance:

    • AI is a tool, and not an educator.
    • Teachers are designers of learning, utilizing AI as a clever assistant, and not a competitor.

     Last Thought

    • AI does not substitute for teachers; it needs them.
    • Without the hand of a human to steer it, AI can be biased, uninformed, or emotionally numb.
    • But with a teacher in charge, AI is a force multiplier — enabling each student to learn more effectively, more efficiently, and more profoundly.

    AI shouldn’t be replacing the teacher in the classroom. It needs to make the teacher more human — less.

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