Tembeling 2.0

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  • Rosenhan versus Cahalan: The Importance of Proper Records and Methodology in Research

    In the history of psychiatric research, few studies have made an impact as profound as David Rosenhan’s 1973 paper, “On Being Sane in Insane Places.” It challenged the validity of psychiatric diagnosis and exposed the depersonalisation of patients in mental hospitals. Decades later, journalist Susannah Cahalan revisited the same study in her 2019 book, The Great Pretender, only to uncover troubling questions about its accuracy and documentation. Together, their work presents more than a disagreement. It is a reminder that no matter how compelling a message may be, research must rest on a foundation of reliable records and sound methodology.

    Rosenhan’s Experiment

    Rosenhan led a group of eight pseudopatients who each presented themselves at different psychiatric hospitals claiming to hear voices. Once admitted, they behaved entirely normally and reported no further symptoms. Despite this, all were diagnosed with serious mental illnesses, mostly schizophrenia, and were prescribed strong antipsychotic medication. The average hospital stay was 19 days, with one patient held for 52 days. None were identified by staff as imposters, although other patients often suspected the truth.

    In a second phase, a hospital challenged Rosenhan to send more pseudopatients as a test of their ability to detect imposters. Over the following months, the staff identified 41 such individuals. In reality, Rosenhan had sent no one. This revealed how psychiatric labels could cloud judgement and foster error.

    The study was published in Science and quickly became one of the most influential critiques of psychiatry. It led to greater scrutiny of mental health institutions, the development of new diagnostic manuals, and the closure of many asylums.

    Cahalan’s Re-examination

    Susannah Cahalan approached Rosenhan’s study with admiration, but her investigative journey revealed serious flaws. Despite extensive effort, she was only able to locate two of the supposed eight pseudopatients. The others could not be verified. Hospital records, raw data, and detailed transcripts were either missing or had never been released. Even more concerning, one of the individuals who had taken part described their experience positively, in contrast to Rosenhan’s bleak narrative.

    Cahalan also discovered an unpublished manuscript written by Rosenhan. It contained inconsistencies and altered case details, raising concerns that parts of the study may have been exaggerated or fictionalised. This lack of transparency stood in sharp contrast to the study’s enduring influence.

    Scientific Integrity

    Rosenhan’s core argument about the dangers of psychiatric labelling was valid. However, the absence of clear documentation raises questions about the reliability of his findings. The study lacked:

    Clear and replicable methodology Comprehensive records and raw data Transparency in patient selection and hospital procedures

    Scientific research depends on verifiability. Without access to original data, no study can be replicated or properly critiqued. Rosenhan’s failure to preserve and share such records weakens the credibility of what was once considered a foundational piece of psychiatric literature.

    Why This Still Matters

    The debate between Rosenhan and Cahalan is not only about psychiatry. It highlights a broader concern within science: the need for rigorous, accountable research practices. Especially in fields that affect people’s lives so directly, such as mental health, ethical research must be rooted in truth and open to scrutiny.

    Public trust in science depends not only on powerful stories, but on the integrity of the research behind them. Narrative alone cannot replace evidence. Researchers must ensure that their work can withstand examination, even many years after it is published.

    Conclusion

    Rosenhan’s study brought attention to real issues in mental health care, and Cahalan’s investigation reminded us that lasting change must be based on fact, not fiction. Their contrasting accounts demonstrate that bold claims require careful evidence. Proper documentation, transparent methods, and reproducibility are not optional features of good science. They are its very foundation. Without them, the line between truth and assumption becomes dangerously unclear.

    References

    Cahalan, S. (2019). The Great Pretender: The Undercover Mission That Changed Our Understanding of Madness. New York, NY: Grand Central Publishing.

    Rosenhan, D. L. (1973). On being sane in insane places. Science, 179(4070), 250–258. https://doi.org/10.1126/science.179.4070.250

    Spiegel, A. (2008, July 31). On being sane in insane places: Revisiting a classic study. NPR. https://www.npr.org/templates/story/story.php?storyId=93646216

    Carey, B. (2019, November 27). The Rosenhan experiment: On being sane in insane places. The New York Times. https://www.nytimes.com/2019/11/27/books/review/the-great-pretender-susannah-cahalan.html

  • Tawhidic Epistemology and the Islamisation of Knowledge in Medical Education

    Introduction

    The modern university, especially in the fields of science and medicine, often functions within a paradigm that disconnects knowledge from values, science from ethics, and intellect from faith. This fragmented epistemology, rooted in secular modernity, results in professionals who are technically proficient but morally and spiritually unmoored. In the Muslim world, this disjunction has precipitated a crisis of meaning in education.

    The International Islamic University Malaysia (IIUM), since its inception, has sought to address this crisis through the vision of Islamisation of Human Knowledge (IoHK). First conceptualised by Syed Muhammad Naquib al-Attas and institutionalised by IIUM’s early leadership, especially the late Tan Sri Professor Dr. Mohammad Kamal Hassan, the founding Rector, IoHK proposes that all branches of knowledge must be critically assessed, purified, and realigned with Islamic values, ethics, and metaphysical worldview.

    This foundational vision has evolved. Under the guidance of Professor Emeritus Datuk Dr. Osman Bakar, the current Rector of IIUM, the process of Islamisation is being deepened through the framework of Tawhidic Epistemology (TE). TE serves not only as a tool for knowledge reform but also as a worldview that re-centres all human inquiry on tawhid, the oneness of Allah.

    In the Kulliyyah of Medicine (KOM), this renewed vision is operationalised through seven TE principles, which guide the holistic development of future Muslim doctors, competent in skill, rich in character, and conscious of divine accountability.

    Tawhidic Epistemology – Rebuilding the Unity of Knowledge

    Tawhidic Epistemology asserts that all knowledge, whether revealed (naqli) or acquired through reason (aqli), emanates from a single divine source. It rejects the artificial division between “religious” and “secular” knowledge and calls instead for a unified understanding of reality, rooted in tawhid.

    TE addresses the intellectual fragmentation of modern education by emphasising:

    1. The unity of truth under the oneness of Allah
    2. The integration of scientific inquiry with spiritual ethics
    3. A holistic view of the human being as a physical, moral, intellectual, and spiritual entity

    This philosophy underpins the contemporary direction of IIUM. Rector Osman Bakar’s notion of the Tawhidic Mind, Ummatic Mind, and Ummatic Excellence encapsulates a developmental framework in which students are nurtured to become not only learned individuals but ethical leaders and khalifahs of Allah.

    Seven Principles of Tawhidic Epistemology in Medical Education

    1.     Unify Divine Knowledge

    Students are taught that the Qur’an, Prophetic traditions, and empirical knowledge are not in conflict but are harmonious components of a unified truth.

    Example 1: In organ transplantation modules, students learn both the medical criteria and the ethical rulings from Islamic jurisprudence, fostering an integrated approach to decision-making.

    Example 2: In anaesthesiology, students examine the issue of euthanasia by exploring both biomedical perspectives, such as the management of end-of-life pain and palliative sedation and Islamic ethical positions, which uphold the sanctity of life and prohibit any form of deliberate life-ending interventions. This integrative teaching helps students distinguish between relieving suffering and violating divine principles regarding life and death.

    2.     Uphold Ethical Trust

    Knowledge is an amanah, a trust from Allah. This principle instils sincerity, fairness, and accountability as part of the student’s ethical compass.

    Example 1: Research ethics and professional conduct are framed as spiritual obligations, not merely institutional requirements. Students are taught that informed consent, avoiding plagiarism, and honest data reporting are forms of worship when done with integrity and consciousness of divine accountability.

    Example 2: In clinical practice, maintaining patient privacy and dignity is emphasised as both a professional and spiritual duty. For example, when examining patients of the opposite gender, students are trained to use a chaperone, lower their gaze, and seek consent respectfully, upholding Islamic adab (etiquette) while fulfilling clinical responsibilities.

    3.     Pursue Higher Purpose

    Through the Ummatic Mind, students are aligned with the maqasid al-shariah (higher objectives of Islamic law), such as the preservation of life, intellect, and faith. Medical education is framed not merely as skill acquisition, but as a sacred journey that integrates clinical excellence with spiritual awareness.

    Example 1: The intention behind treating patients is not only to preserve life and advance knowledge in medicine, but also to serve as a means of drawing both the caregiver and the patient closer to Allah. This transforms everyday clinical tasks into acts of worship and service to humanity.

    Example 2: In palliative care training, students are taught to go beyond symptom control by addressing the emotional, psychological, and spiritual dimensions of dying. Upholding dignity at the end of life becomes an act of compassion and a reflection of the Islamic value of mercy (rahmah).

    4.     Contribute Meaningful Impact

    Knowledge must serve the ummah and uplift the marginalised. Learning is not solely for personal success, but for advancing social justice, improving equity, and fulfilling the duty of khilafah (stewardship) on Earth.

    Example: During the community medicine posting, students engage in health outreach activities in underserved and remote areas. These efforts, which include screening programmes, health education, and preventive care, go beyond academic fulfilment. They are expressions of the Islamic imperative to use knowledge in the service of others, especially the vulnerable and neglected.

    5.     Develop Professional Mastery

    Professional mastery in medicine demands the structured attainment of competencies, not only in clinical knowledge and technical skills but also in communication, decision-making, and professionalism. Within the Tawhidic framework, competence is pursued as an obligation (fard) and a form of amanah (trust), to ensure safe, effective, and ethical care.

    Example: The curriculum is designed to ensure students achieve clearly defined learning outcomes and clinical competencies, including history-taking, examination, procedural skills, and clinical judgement. These are continuously assessed through workplace-based methods and objective clinical examinations, ensuring graduates are both capable and accountable in fulfilling their professional responsibilities.

    6.     Embody Compassionate Care

    Inspired by the divine attribute of rahmah (mercy), compassion in medical practice is seen as a form of renewed empathy that is conscious, purposeful, and ethically grounded. It involves a sincere commitment to alleviate suffering, preserve human dignity, and foster meaningful human connections.

    Example: Communication training emphasises emotional intelligence and empathy, especially in situations such as delivering difficult news or managing patients with chronic and terminal illnesses. Students are taught to listen attentively, respond sincerely, and maintain a respectful presence. This compassionate approach extends beyond patients, fostering kindness and mutual respect in interactions with colleagues, healthcare staff, and the wider medical team.

    7.     Practice Moral Integrity

    Spiritual growth must be accompanied by a strong moral compass that guides both personal and professional conduct. This principle draws upon the concepts of ihsan (excellence in worship and character) and tazkiyah (purification of the soul), nurturing sincerity, truthfulness, and ethical discipline in all aspects of life.

    Example: Students are taught that integrity applies to every action, from being honest in assignments and examinations to being truthful in logbooks and research reports. For staff, this extends to making accurate claims and fulfilling responsibilities with trust and fairness. Upholding Islamic adab includes maintaining respectful and appropriate interactions across genders, observing Shariah-compliant boundaries in communication and behaviour. Moral integrity is nurtured not only for personal salvation but also to uphold public trust and professionalism in medicine.

    Islamisation of Knowledge – Reforming the Content

    While TE provides the worldview, Islamisation of Knowledge remains the methodological backbone of IIUM’s academic reform. It aims to critique, filter, and reconstruct modern knowledge according to Islamic ethical and ontological principles.

    At KOM, this includes:

    1. Evaluating medical knowledge through the lens of Shariah and ethics
    2. Reintroducing Islamic concepts into contemporary discourse on health
    3. Creating new integrative models of care based on the Islamic view of the human being

    Examples:

    1. Mental health modules include nafs, qalb, and fitrah alongside DSM-based diagnosis.
    2. Public health courses incorporate maqasid-oriented strategies.
    3. Students conduct research exploring the intersection of Islamic ethics and epidemiology.

    Tawhidisation and Islamisation – Complementary Approaches

    Aspect Tawhidic Epistemology Islamisation of Knowledge
    Nature Foundational worldview based on tawhid Methodological process for content reform 
    Focus How knowledge is sourced, internalised, and valued How knowledge is critiqued, refined, and applied 
    Function Shapes the learner’s consciousness and ethical disposition Shapes the curriculum and scholarly output 
    ApplicationSeven TE principles guide the values and learning culture Islamised content in clinical, behavioural, and social sciences 

    Conclusion

    The journey of IIUM, from its Islamisation of knowledge focus to its expansion into Tawhidic Epistemology, reflects a continuous pursuit of holistic and purposeful education. These are not competing philosophies, but rather stages in the development of an Islamic intellectual tradition that seeks to integrate revelation, reason, and reality.

    In medical education, this integration results in a curriculum that goes beyond technical training. At KOM, Tawhidic Epistemology influences the mindset. Islamisation of Knowledge reforms the curriculum content. Together, they guide the formation of doctors who are technically skilled, spiritually aware, and socially responsible.

    This represents a medicine with a soul. It signifies a return to the Islamic civilisation’s tradition of learning that heals both the body and the spirit, and a renewal of education as a sacred trust to be fulfilled in the service of Allah and humanity.

    References

    Al-Attas, S. M. N. (1978). Islam and secularism. Muslim Youth Movement of Malaysia.

    Bakar, O. (2022). Tawhid and science: Islamic perspectives on religion and science. Penerbit UTM Press.

    Hassan, M. K. (1981). A return to the Qur’anic paradigm of development and its implications for education policy and the curriculum. International Institute of Islamic Thought and Civilization.

    Nasr, S. H. (1968). Science and civilization in Islam. Harvard University Press.

    Rahman, F. (1982). Islam and modernity: Transformation of an intellectual tradition. University of Chicago Press.

  • Ban Vape Now to Protect Public Health in Malaysia

    Executive Summary

    Vaping has emerged as a growing public health and security crisis in Malaysia. Once promoted as a safer alternative to smoking, vaping is now strongly linked to nicotine addiction, youth uptake, serious health harms, and even drug abuse. The enforcement of the Akta Kawalan Produk Merokok Demi Kesihatan Awam 2024 (Act 852), effective 1 October 2024, is a positive step but its implementation has proven difficult. The Ministry of Health (MOH) is now burdened with too many roles including policymaking, regulation, licensing, monitoring, and enforcement. With limited resources, MOH cannot manage this growing threat effectively. An immediate ban on all vape products is necessary. In the longer term, stronger measures must be taken to restrict cigarette use and move toward a smoke-free future.

    Key Issues

    1. Act 852 is difficult to implement

    Act 852 requires comprehensive regulation of vape products, including licensing of all retailers, monitoring of product contents and marketing, control of online and physical sales, and enforcement of advertisement bans. MOH is expected to take full responsibility for these tasks while also managing other core public health functions. This regulatory and enforcement burden is unrealistic and unsustainable.

    2. Vaping is not harm reduction

    Peer-reviewed research in BMJ Open (2023) involving ASEAN tobacco control experts confirms that nicotine vaping products are not viewed as effective cessation tools and are instead considered a public health threat. Most adult vapers in Malaysia (75%) are dual users who continue smoking cigarettes, thus undermining the notion of risk reduction.

    3. Vaping causes serious health harms

    Published studies report that Malaysian vape users commonly experience dry mouth, cough, headaches, and dizziness. More severe outcomes include EVALI (e-cigarette or vaping product use-associated lung injury), which has been documented in Malaysia. Each case costs an estimated RM150,000 to treat, with a projected national healthcare burden of RM368 million annually by 2030 if left unregulated.

    4. Estimated cost burden for Malaysia

    With over 1 million adult daily users in Malaysia (based on 5.4% prevalence), even a 0.1% complication rate requiring hospitalisation would result in 1,000 EVALI cases annually. At RM150,000 per case, this would translate to RM150 million in direct inpatient costs alone, not accounting for outpatient care, productivity loss, or future chronic disease management. Meanwhile, vape tax revenue of RM500 million per year is unlikely to cover the rising health and enforcement costs.

    5. Vaping as a vehicle for drug abuse

    An increasingly alarming trend in Malaysia involves the misuse of vape devices as covert drug delivery tools. Law enforcement and the National Anti-Drugs Agency (AADK) have reported seizures of vape devices containing methamphetamine, ketamine, THC oil, and synthetic cannabinoids. These substances are often inhaled using modified pods or liquids indistinguishable from regular vape products. Students and youths are particularly vulnerable due to the discreet nature of vape use, making enforcement nearly impossible under current regulations. This trend represents both a public health emergency and a national drug control challenge.

    Policy Recommendations

    1. Ban all vape products immediately

    Enact a full ban on the manufacture, import, sale, promotion, and possession of all vape devices and liquids. Strengthen controls on online and cross-border purchases. Declare a national public health and security emergency linked to youth vaping and drug misuse.

    2. Reduce the burden on MOH

    MOH should focus on public health policy, surveillance, and prevention. Licensing, inspections, and enforcement functions should be delegated to other agencies, including municipal councils and the Ministry of Domestic Trade. MOH resources should be redirected toward cessation programmes and school-based health promotion.

    3. Begin long-term restrictions on cigarette sales

    Malaysia should adopt a structured roadmap toward a cigarette-free society. Immediate steps include increased tobacco taxation, plain packaging, limiting retail outlets, and expanding access to evidence-based cessation support. Stronger action is also needed against illicit tobacco trade.

    References

    Gravely, S., Yong, H. H., Reid, J. L., et al. (2022). The prevalence of e-cigarette use in Malaysia: Findings from the 2020 ITC Malaysia Survey. Tobacco Induced Diseases, 20(42). https://doi.org/10.18332/tid/146917 Wong, L. P., Alias, H., Aghamohammadi, N., et al. (2023). Self-reported side effects, dependence, and behaviour in e-cigarette users in Malaysia. Substance Abuse Treatment, Prevention, and Policy, 18(1). https://doi.org/10.1186/s13011-023-00558-7 Hamilton, W. L., et al. (2022). E-cigarette markets and policy responses in Southeast Asia: A scoping review. International Journal of Health Policy and Management, 11(10), 2236–2246. https://doi.org/10.34172/ijhpm.2021.104 De Guia, M. C., et al. (2023). Implications of nicotine vaping products for tobacco control in ASEAN LMICs: In-depth interviews with experts. BMJ Open, 13(9): e073106. https://doi.org/10.1136/bmjopen-2023-073106 Ibrahim, N., et al. (2023). Emerging trends in drug delivery through vaping devices. Frontiers in Public Health, 11:1198763. https://doi.org/10.3389/fpubh.2023.1198763 United Nations Office on Drugs and Crime (UNODC). (2021). Synthetic Drugs and Novel Psychoactive Substances: A Global Threat. https://www.unodc.org Ministry of Health Malaysia. (2024). Cost estimation for EVALI treatment and projections. MOH official communications reported in multiple government briefings.

  • Statistics and Machine Learning in Public Health: When to Use What

    If you’re trained in epidemiology or biostatistics, you likely think in terms of models, inference, and evidence. Now, with machine learning entering the scene, you’re probably hearing about algorithms that can “predict” disease, “detect” outbreaks, and “learn” from data. But while ML offers exciting possibilities, it’s important to understand how it differs from classical statistical approaches—especially when public health decisions depend on more than just prediction.

    Let’s explore how statistics and machine learning differ—not just in technique, but in mindset, use case, and the all-important concept of causality.

    How They Think

    Statistics and machine learning begin with different goals.

    Statistics is built to answer questions like: Does exposure X cause outcome Y? It aims to explain relationships, test hypotheses, and estimate effect sizes. It relies on assumptions—like randomness, independence, and model structure—to ensure that findings reflect the real world, not just the sample at hand.

    Machine learning, in contrast, asks: Given this data, what outcome should I predict? It doesn’t aim to explain but to perform—minimising error and maximising predictive accuracy, even if the relationships are complex or difficult to interpret.

    That’s a major shift. While statistics seeks truth about the population, ML seeks performance in unseen data.

    How They Work

    Statistical methods are grounded in probability theory and estimation. They involve fitting models with interpretable parameters: coefficients, confidence intervals, p-values. The analyst usually specifies the form of the model in advance, guided by theory and prior evidence.

    Machine learning models are trained through algorithms, often using large datasets and iterative techniques to optimise performance. Models like decision trees, support vector machines, and random forests find patterns without assuming linearity or distribution. You don’t always know what the model is “looking at”—you just know if it works.

    There are also hybrid approaches—like regularised regression, ensemble models, and causal forests—that blend the logic of both.

    What They Do Well

    Statistics excels in clarity and rigour. It tells you not just whether something matters, but how much, and with what certainty. It’s ideally suited for:

    Identifying risk factors Estimating treatment effects Designing policy interventions Publishing findings with transparent reasoning

    Machine learning is best when:

    Relationships are non-linear or unknown You have many predictors and large datasets You need fast, repeatable predictions (e.g. real-time risk scoring) The goal is performance, not explanation

    In short, statistics helps you understand, ML helps you predict.

    Where They Fall Short

    Statistics can break down when data gets messy—especially when model assumptions are violated or the number of variables overwhelms the number of observations. It also isn’t built to handle unstructured data like images or free text.

    Machine learning’s biggest limitation is often overlooked: it doesn’t care about causality. A model may predict hospitalisation risk with 95% accuracy, but it doesn’t tell you why. It might rely on variables that are associated, not causal. Worse, it might act on misleading proxies that look predictive but don’t offer actionable insight.

    This matters deeply in public health. Predicting who dies is not the same as preventing death. Models that ignore cause can lead to misguided interventions or unjust decisions.

    Another weakness of ML is interpretability. Many powerful algorithms (like gradient boosting or neural networks) are “black boxes”—hard to explain and harder to justify in policy decisions. While newer tools like SHAP can improve transparency, they still fall short of the clarity offered by traditional statistical models.

    When to Use Each

    Use statistics when:

    Your primary goal is inference or explanation You need to estimate effects or support causal conclusions You’re informing policy or making ethical decisions You want results that are interpretable and reportable

    Use machine learning when:

    Your primary goal is prediction or classification You’re handling high-dimensional or complex data You need scalable automation (e.g. early warning systems) You can validate predictions with real-world data

    Most importantly, if causality matters, don’t rely solely on ML—use statistical thinking or causal ML techniques that explicitly model counterfactuals and assumptions.

    What You Should Expect

    From statistics, expect:

    Clear models with interpretable outputs Transparent assumptions The ability to test hypotheses and quantify uncertainty

    From machine learning, expect:

    High performance with minimal assumptions Useful predictions even when mechanisms are unknown Some loss of interpretability (unless addressed deliberately)

    Just remember: good prediction doesn’t imply good understanding. And good models don’t always lead to good decisions—unless we interpret them wisely.

    A Path Forward for Epidemiologists and Biostatisticians

    Here’s the good news: your training in statistics and epidemiology is not a limitation—it’s your greatest asset. You already understand data, confounding, validity, and generalisability. You’re equipped to evaluate models critically and ask: Does this make sense? Is it actionable? Is it ethical?

    Start small. Try ML approaches that are extensions of what you know—like regularised logistic regression, decision trees, or ensemble methods. Explore tools like caret, tidymodels, or scikit-learn. And when you’re ready to dive deeper, look into causal ML methods like:

    • Targeted maximum likelihood estimation (TMLE)
    • Causal forests (grf)
    • Double machine learning (EconML)
    • DoWhy (for structural causal models)

    The best analysts of the future won’t just be statisticians or ML engineers—they’ll be methodologically bilingual, able to switch between explanation and prediction as the question demands.

    Your role isn’t to replace one with the other, but to integrate both—so that public health remains not just data-driven, but wisely so.

  • Good and Evil of AI in Medicine: Where Is the Boundary?

    Artificial intelligence (AI) is rapidly transforming the field of medicine, offering unprecedented opportunities to improve healthcare delivery, diagnosis, and population health management. However, with its promise comes a risk of harm, particularly when AI systems are poorly designed, implemented without appropriate safeguards, or driven by commercial interests at the expense of public good. This paper explores what constitutes good and evil in medical AI, provides examples of both, and outlines ethical boundaries and practical steps to ensure that AI serves humanity.

    AI in medicine refers to systems designed to assist with tasks such as diagnosis, prognosis, treatment recommendations, and public health surveillance. The good in medical AI lies in its capacity to enhance human well-being, reduce inequalities, and improve healthcare efficiency. AI applications can support clinical decisions, automate routine tasks, and extend healthcare reach to underserved populations (Rajkomar, Dean, & Kohane, 2019). Conversely, the potential for evil emerges when AI contributes to harm, reinforces inequities, or undermines essential human values such as compassion, accountability, and justice. This harm may arise from biased algorithms, opaque decision-making processes, or commercial exploitation that prioritises profit over patient welfare.

    The Goods

    One of the clearest demonstrations of AI’s positive contribution to medicine is in the field of early disease detection. AI systems trained on medical images have been shown to accurately detect conditions such as diabetic retinopathy and tuberculosis. A pivotal study demonstrated that an autonomous AI system could safely and effectively identify diabetic retinopathy in primary care settings, enabling earlier referrals and potentially preventing vision loss (Abràmoff, Lavin, Birch, Shah, & Folk, 2018). In tuberculosis screening, AI-based chest X-ray interpretation tools have been used in high-burden countries to prioritise patients for further diagnostic testing, particularly in settings where human expertise is limited (Codlin et al., 2025). These applications help address gaps in healthcare access and reduce delays in diagnosis and treatment.

    AI has also supported public health surveillance, particularly during emergencies such as the COVID-19 pandemic. AI models combined data from health records, mobility patterns, and social media to predict outbreaks, identify hotspots, and inform targeted interventions. This contributed to more timely and effective public health responses and resource allocation (Bullock, Luccioni, Hoffmann, & Jeni, 2020).

    The Evils

    Despite these benefits, AI has also been linked to harms that can undermine trust and exacerbate health inequities. One of the most pressing concerns is algorithmic bias. AI systems trained on data that do not represent the diversity of patient populations may produce biased outcomes. For example, machine learning tools for dermatology developed primarily using images of lighter skin tones have been found to perform less accurately on darker skin. This can lead to missed or delayed diagnoses in patients from minority groups, reinforcing existing disparities (Adamson & Smith, 2018).

    Commercial exploitation of AI is another area of concern. The rush to monetise AI in medicine has sometimes led to the deployment of systems that are insufficiently transparent or accountable. Proprietary algorithms may operate as black boxes, with their decision-making processes hidden from both clinicians and patients. This opacity undermines informed consent and shared decision-making, and can make it difficult to challenge or review AI-driven recommendations (Char, Shah, & Magnus, 2018).

    Furthermore, there is a risk that excessive reliance on AI could erode the compassionate, human-centred aspects of healthcare. While AI can assist with routine tasks and reduce administrative burdens, it must not be seen as a replacement for human empathy and professional judgement. There is concern that as AI takes on a greater role, the patient-doctor relationship could become depersonalised, weakening one of the core foundations of medical practice (Panch, Szolovits, & Atun, 2019).

    Ethical Boundaries for Responsible AI

    To ensure that AI in medicine serves the common good rather than causes harm, clear ethical boundaries are needed. Transparency is essential. AI systems must be designed in ways that make their decision-making processes understandable and open to scrutiny. This is critical to maintaining trust, supporting informed consent, and enabling clinicians to integrate AI recommendations into their decision-making with confidence.

    Fairness must also be prioritised. Developers need to ensure that AI tools are designed to promote equity rather than exacerbate disparities. This involves using diverse training datasets, actively auditing algorithms for bias, and engaging with communities to understand their needs and perspectives. Bias mitigation should be a central part of AI development and deployment, not an afterthought.

    Accountability is another key principle. Developers, healthcare providers, and regulators share responsibility for ensuring that AI systems are safe, effective, and aligned with ethical principles. Regulatory frameworks should define standards for AI in healthcare and provide mechanisms for monitoring, evaluation, and redress when harm occurs (Char et al., 2018).

    Compassion must remain central to healthcare, even as AI systems become more common. AI should be designed and used to support, rather than replace, the human connection between healthcare professionals and patients. The ultimate goal should be to free clinicians from administrative burdens and allow them to focus on what matters most: the well-being of the people they serve (Topol, 2019).

    Towards Governance and Action

    The development and use of medical AI should be guided by comprehensive national or regional governance frameworks that balance the promotion of innovation with the protection of public interest. Such frameworks need to address issues including data privacy, transparency, bias mitigation, and equitable access. They should be shaped through collaboration between governments, healthcare professionals, technologists, and civil society to ensure that they are both robust and responsive to local contexts and needs.

    Education and capacity building are also essential. Healthcare professionals, public health experts, and policymakers must be equipped with the knowledge and skills needed to engage with AI critically and effectively. Training should address not only technical competencies but also the ethical, legal, and social implications of AI.

    Finally, ongoing research is needed to evaluate the real-world impact of AI in healthcare. This research should assess not only clinical outcomes but also equity, patient safety, and the preservation of humanistic values. It should inform continuous improvement of AI systems and the policies that govern their use (Morley, Floridi, Kinsey, & Elhalal, 2020).

    Conclusion

    AI has the potential to greatly enhance healthcare, improving efficiency, accuracy, and access. However, without appropriate safeguards, it also carries the risk of causing harm, deepening inequities, and eroding core human values. The boundary between good and evil in medical AI lies in how these technologies are designed, implemented, and governed. By upholding principles of transparency, fairness, accountability, and compassion, and by embedding these principles in governance frameworks and professional practice, it is possible to ensure that AI serves as a tool for good in medicine.

    References

    Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digital Medicine, 1, 39.

    Adamson, A. S., & Smith, A. (2018). Machine learning and health care disparities in dermatology. JAMA Dermatology, 154(11), 1247-1248.

    Bullock, J., Luccioni, A., Hoffmann, P. H., & Jeni, L. A. (2020). Mapping the landscape of artificial intelligence applications against COVID-19. Journal of Artificial Intelligence Research, 69, 807-845.

    Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care – Addressing ethical challenges. New England Journal of Medicine, 378, 981-983.

    Chen, I. Y., Szolovits, P., & Ghassemi, M. (2019). Can AI help reduce disparities in general medical and mental health care? AMA Journal of Ethics, 21(2), E167-E179.

    Codlin, A. J., Dao, T. P., Vo, L. N. Q., Forse, R. J., Nadol, P., & Nguyen, V. N. (2025). Comparison of different Lunit INSIGHT CXR software versions when reading chest radiographs for tuberculosis. PLOS Digital Health, 4(4), e0000813.

    Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: An overview of AI ethics tools, methods and research to translate principles into practices. AI & Society, 36, 59-71.

    Panch, T., Szolovits, P., & Atun, R. (2019). Artificial intelligence, machine learning and health systems. Journal of Global Health, 8(2), 020303.

    Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.

    Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

  • The Evolution of Causality in Understanding Disease

    Understanding causality has always been central to the quest for knowledge about health and disease. From the philosophical inquiries of Aristotle to the precision of modern causal inference frameworks, our ideas about what causes disease and how to intervene have evolved through centuries of intellectual effort. This article traces that journey, highlighting key contributions from Aristotle, Al-Farabi, Robert Koch, Austin Bradford Hill, Ken Rothman, and Judea Pearl, and connects their ideas to modern medical practice.

    Aristotle and the origins of causal thinking

    Aristotle (384–322 BCE) introduced what is arguably the first formal framework for understanding causation. He proposed that to fully explain why something exists or happens, one must consider four types of causes: material, formal, efficient, and final causes.

    The material cause is what something is made of. In medicine, this could refer to the tissues, cells, or substances involved in disease. The formal cause is the design or pattern that gives a thing its structure, comparable to the organisation of cells or the genetic blueprint of the body. The efficient cause is the agent or force that produces change. In health, this might be an infectious agent, injury, or environmental exposure. The final cause represents the purpose or goal. For Aristotle, everything in nature had an end or purpose, and in medical terms, this could be metaphorically linked to the goal of health or survival.

    Aristotle’s framework laid the foundation for causal reasoning not only in natural science but also in ethics, politics, and medicine. His approach encouraged generations of thinkers to seek deep, structured explanations for the phenomena they observed.

    Al-Farabi and the integration of causality into Islamic philosophy

    Al-Farabi (872–950 CE), often called the Second Teacher after Aristotle, engaged deeply with Aristotle’s ideas and reinterpreted them through the lens of Islamic philosophy. Al-Farabi did not discard the Four Causes, but he gave them new meaning within a framework that aligned with tawhid, the concept of divine unity.

    Al-Farabi argued that all efficient causes ultimately trace back to the First Cause, God. He extended Aristotle’s final cause beyond natural purposes to include divine wisdom and the moral purpose of human life. In Al-Farabi’s philosophy, causality was not simply a mechanical chain of events but a reflection of divine order and purpose.

    His famous idea that human beings are madani bi al-tab‘i (social by nature) linked causality to the collective pursuit of well-being. In his vision of the Virtuous City (al-Madinah al-Fadilah), knowledge of causes guided not just individual health, but the health of the community and the moral responsibility to promote the common good.

    Robert Koch and the birth of scientific causality in medicine

    The modern scientific study of disease causation began with the work of Robert Koch (1843–1910). Koch introduced formal criteria, known as Koch’s postulates, for identifying the causal relationship between a microorganism and a disease.

    Koch’s postulates required that the microorganism be found in every case of the disease, be isolatable in pure culture, cause the disease when introduced into a healthy host, and be re-isolated from the experimentally infected host. This approach transformed causality in medicine, especially in infectious diseases, from speculative reasoning to testable science.

    Koch’s work exemplified the search for necessary causes in disease. His criteria worked well for infections like tuberculosis but less so for complex diseases that result from multiple interacting factors.

    Austin Bradford Hill and the rise of multifactorial causality

    By the mid-20th century, it had become clear that many diseases did not have single necessary causes. Chronic diseases like cancer, heart disease, and diabetes involved numerous risk factors. Austin Bradford Hill (1897–1991) addressed this complexity by proposing a set of considerations, now known as the Bradford Hill criteria, to help scientists judge whether an observed association is likely to be causal.

    The criteria include strength of association, consistency, specificity, temporality, biological gradient, plausibility, coherence, experimental evidence, and analogy. These considerations reflect the complexity of disease causation and guide researchers in interpreting epidemiological data.

    Hill’s approach helped move the focus from single necessary causes to component causes that contribute to sufficient causal mechanisms. This shift set the stage for modern causal models.

    Ken Rothman and the component cause model

    Ken Rothman (born 1945) further refined the understanding of disease causation by introducing the component cause model, often visualised as the causal pie model. This model describes how a disease can result from different combinations of factors, where each combination forms a sufficient cause.

    In Rothman’s model, component causes represent individual factors (such as smoking, genetic susceptibility, or environmental exposure) that combine to complete a causal mechanism. No single component cause needs to be necessary or sufficient on its own. The model illustrates why many diseases cannot be attributed to a single factor and why prevention strategies must target multiple risk factors.

    Rothman’s work has influenced generations of epidemiologists and public health professionals, providing a practical and visual tool to understand and teach multifactorial causation.

    Judea Pearl and the ladder of causation

    The most recent revolution in causality comes from Judea Pearl (born 1936), whose work has transformed causal inference into a formal, mathematical science. Pearl introduced causal diagrams, known as directed acyclic graphs (DAGs), and structural causal models to make causal relationships explicit and testable in data.

    One of Pearl’s key contributions is the concept of the Ladder of Causation. The ladder describes three levels of causal reasoning. The first level is association, where one observes patterns in data. The second level is intervention, where one reasons about what happens if something is changed or manipulated. The third level is counterfactuals, where one asks what would have happened under different circumstances.

    Pearl’s framework allows researchers to distinguish between mere correlation and true causation and to address complex issues such as confounding, mediation, and effect modification. His work is now central to fields ranging from epidemiology to artificial intelligence.

    Causality in modern medicine

    Understanding causality has practical implications in modern medicine. Few diseases today are thought to have single necessary and sufficient causes. Instead, most conditions arise from combinations of component causes that form sufficient causal mechanisms.

    Take lung cancer as an example. Smoking is neither a necessary cause (because lung cancer can occur in non-smokers) nor a sufficient cause (because not all smokers develop lung cancer). However, smoking is a major component cause that contributes to sufficient causal mechanisms. Interventions that reduce smoking prevalence can prevent many cases of lung cancer, even if they do not eliminate the disease entirely.

    Similarly, understanding that hypertension, high cholesterol, and physical inactivity are component causes of cardiovascular disease guides interventions that target these factors. The insights from causal reasoning help shape prevention strategies, clinical decisions, and public health policies.

    From philosophy to practice

    Tracing the journey of causality thinking from Aristotle to Pearl shows the progression from philosophical reflection to scientific precision. Aristotle’s Four Causes encouraged us to look for deeper reasons behind events. Al-Farabi integrated these ideas with a moral and social vision, reminding us that understanding causes should serve the common good. Koch’s postulates gave us tools to prove necessary causes in infectious diseases. Bradford Hill’s criteria helped navigate the complexity of chronic disease causation. Rothman’s component cause model illustrated the multifactorial nature of disease. Pearl’s ladder of causation and causal models now give us the tools to analyse and act on causal relationships in complex systems.

    Together, these frameworks have helped medicine move beyond treating symptoms to addressing root causes. They also remind us that understanding causality is not only about explaining disease but also about guiding interventions that promote health and well-being.

    References

    Hill, A. B. (1965). The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine, 58(5), 295–300.

    Koch, R. (1884). Die aetiologie der tuberkulose. Berliner Klinische Wochenschrift, 21, 221–230.

    Pearl, J. (2018). The book of why: The new science of cause and effect. Basic Books.

    Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern epidemiology (3rd ed.). Lippincott Williams & Wilkins.

    VanderWeele, T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press.

  • Civilisation and Divine Guidance: Reflections on History and Morality

    Civilisation has long been a subject of study, both for its material achievements and its moral dimensions. From an Islamic perspective, civilisations are not accidental outcomes of human progress, but part of a divine plan in which nations, tribes, and communities arise to fulfil higher purposes. Their existence offers opportunities for humanity to cooperate, recognise divine signs, and establish justice. The rise and fall of civilisations, recorded both in historical chronicles and in the Qur’an, provide enduring lessons on the relationship between spiritual values and societal success.

    A civilisation can be defined as a society that has reached an advanced stage of development in its social, political, and cultural institutions. Such societies are characterised by surplus food production, allowing for the division of labour and economic complexity. They establish organised governments and religious systems, and they develop writing to preserve knowledge across generations. Major civilisations arose along rivers that supported agriculture and trade, such as Mesopotamia along the Tigris and Euphrates, Ancient Egypt along the Nile, the Indus Valley, and Ancient China along the Huang He. These civilisations left behind impressive legacies in architecture, law, science, and the arts. Yet, their true greatness, from an Islamic viewpoint, is measured by their adherence to divine principles rather than material achievements alone.

    The Qur’an teaches that mankind originated as a single community, united in worship and purpose, before differences arose. These differences gave rise to nations and tribes, not for division or conflict, but so that people might learn from one another and recognise the signs of their Creator. As stated in Al-Hujurat: 13, diversity in human societies is a deliberate act of divine wisdom, meant to inspire mutual benefit and cooperation. Allah’s plan for humanity included the rise of civilisations to provide structure for human life and a setting for the moral and spiritual testing of individuals and communities.

    To guide civilisations on the right path, Allah sent prophets to every nation. These messengers called their people to monotheism (Tawhid), justice, and righteousness. Their messages, though suited to the specific needs and circumstances of their communities, consistently emphasised the worship of Allah alone and the obligation to uphold moral values. The prophets’ role extended beyond personal piety; they provided guidance for social order, economic justice, and political integrity, ensuring that civilisations could thrive both materially and spiritually.

    The reflections of major Islamic scholars further deepen this understanding. Ibn Khaldun described civilisation as thriving on moral strength, justice, and solidarity (asabiyyah), warning that decline begins with injustice, luxury, and moral decay. Syed Muhammad Naquib al-Attas highlighted that true civilisation is rooted in knowledge from divine revelation, aiming for adab (proper conduct) and the realisation of truth and justice. Malik Bennabi viewed civilisation as the sum of moral, material, and spiritual components, emphasising that decline starts with intellectual stagnation and moral decay. Sayyid Qutb saw Islamic civilisation as built upon submission to Allah and the establishment of justice and moral leadership. These perspectives show that in Islam, the success of a civilisation depends on its adherence to divine guidance and its commitment to justice, truth, and moral integrity.

    History offers numerous examples of how civilisations rose and fell in connection with their acceptance or rejection of divine guidance. The people of Nuh rejected his call and were destroyed by a flood. The powerful civilisation of ‘Ad, known for its architectural feats, fell after turning away from the message of Hud. Thamud, a society skilled in carving homes from stone, faced ruin after defying the warnings of Salih and harming the she-camel sent as a divine sign. Ibrahim challenged the idolatry of Ur in Mesopotamia, confronting the tyranny of Namrud. Musa confronted the Pharaoh of Egypt, who epitomised oppression and arrogance. Muhammad, the final messenger, brought the universal message of Islam to unify all humanity under the worship of Allah and the principles of justice and compassion.

    ProphetApproximate periodWestern period
    Adam (AS)~10,000–5,000 BCEPrehistory (Stone Age, early Neolithic)
    Idris (AS)~5,000–4,000 BCELate prehistory (early settlements, proto-writing)
    Nuh (AS)~3,500–3,000 BCETransition to ancient history (early Mesopotamian civilisation)
    Hud (AS)~2,500 BCEAncient history (Bronze Age, Sumer, Akkad)
    Salih (AS)~2,400 BCEAncient history
    Ibrahim (AS)~2,000 BCEAncient history (Ur, Mesopotamia, Bronze Age)
    Lut (AS)~2,000 BCEAncient history
    Ismail (AS)~2,000 BCEAncient history
    Ishaq (AS)~1,900 BCEAncient history
    Yaqub (AS)~1,800 BCEAncient history
    Yusuf (AS)~1,750 BCEAncient history (Middle Kingdom Egypt)
    Musa (AS)~1,300 BCEAncient history (New Kingdom Egypt)
    Dawud (AS)~1,000 BCEAncient history (Iron Age, early kingdoms)
    Sulaiman (AS)~970 BCEAncient history
    Ilyas (AS)~850 BCEAncient history
    Yunus (AS)~800 BCEAncient history (Assyrian Empire)
    Zakariya (AS)~5 BCEAncient history (Roman Empire period)
    Isa (AS)~0 CEAncient history
    Muhammad (SAW)570–632 CEMedieval history (early Islamic period)

    The rise and fall of these civilisations reflect a broader cycle seen throughout history: growth, stability, and decline. Civilisations grow through adherence to truth, justice, and divine values. They achieve stability by building sound institutions and spreading beneficial knowledge. Over time, however, many fall into complacency, corruption, and materialism, leading to internal decay and eventual collapse. The Qur’anic accounts of past nations serve as reminders that moral and spiritual decay, more than external enemies, is what undermines the foundations of a civilisation.

    While Western historians often categorise history into prehistory, ancient history, medieval history, and modern history based on material culture and technological developments, the Islamic perspective focuses on the presence or absence of divine guidance. For example, what the West classifies as prehistory includes the time of Adam and Idris, while ancient history encompasses the periods of Nuh, Ibrahim, and Musa. The time of Muhammad marks the transition into what Western scholars consider the medieval period. In Islamic thought, the moral and spiritual dimensions of these eras are what give them significance.

    The lessons drawn from the study of civilisations are as relevant today as they were in ancient times. Societies thrive when they base their institutions on truth, justice, and compassion, and when they recognise their responsibility to the Creator and to one another. Conversely, when civilisations become consumed by oppression, injustice, and the pursuit of worldly gains at the expense of moral integrity, they set themselves on a path to decline. The study of history, therefore, is not merely an academic exercise but a source of guidance for building a just and enduring society.

    References

    Qur’an: Al-Baqarah 213, Al-Hujurat 13, Al-A’raf 73-79, Al-Anbiya 69, Al-Fajr 1-14
    Ibn Kathir. Stories of the Prophets (Qasas al-Anbiya).
    Islamicity. Interactive Timeline of Prophets. https://www.islamicity.org/13628/timeline-of-the-prophets/
    Kasule, O. (2004). Islamic Medical Resources. http://omarkasule.tripod.com
    Ibn Khaldun. (1967). The Muqaddimah: An Introduction to History (F. Rosenthal, Trans.). Princeton University Press.
    Al-Attas, S. M. N. (1978). Islam and Secularism. Muslim Youth Movement of Malaysia (ABIM).
    Al-Attas, S. M. N. (1995). Prolegomena to the Metaphysics of Islam. International Institute of Islamic Thought and Civilization (ISTAC).
    Bennabi, M. (1984). The Question of Culture. Islamic Research Institute.
    Bennabi, M. (2013). Islam in History and Society (H. Abdel-Malek, Trans.). Islamic Book Trust.
    Qutb, S. (2006). Milestones (A. B. al-Mehri, Trans.). Maktabah Booksellers and Publishers.

  • The Amanah of Leadership

    “I have been appointed over you, though I am not the best.”
    These words echo in my head.
    This is not my right.
    This is not my reward.
    This is amanah.
    A trust.

    Leadership is not glory.
    It is responsibility.
    It is duty.
    It is sacrifice.
    It is service.

    I am here, try to inspire.
    To build.
    To nurture.
    To lift others higher.
    To create leaders who will lead better.

    I have no strength of my own.
    No power in these hands.
    No wisdom except what Allah gives.
    No success except by His will.

    Do not rely on me.
    Rely on Allah.
    He is the source of all strength.
    He is the giver of victory.

    I will stumble.
    I will err.
    So correct me.
    Remind me.
    Stand with me.

    Let us walk this path together.
    Let us lead each other towards Him.
    Let us serve with sincerity.
    Let us lead with humility.

    May Allah guide us all.
    May He bless this journey.
    May He accept our deeds.

  • Planetary Health in Medical Curricula

    Abstract

    Planetary Health is an emerging interdisciplinary field that recognises the deep interconnection between human health and the health of the Earth’s natural systems. Coined by the Rockefeller Foundation–Lancet Commission in 2015, it expands the focus of health beyond traditional biomedical and social determinants to include ecological boundaries and environmental integrity. In this presentation, we explore why Planetary Health is increasingly relevant to medical education and how it can be integrated into the MBBS curriculum at the International Islamic University Malaysia (IIUM), guided by the university’s philosophy of “Medicine with a Soul”.

    The presentation begins by outlining the evidence that environmental change is reshaping the global disease landscape. Climate change has intensified the frequency and severity of heatwaves, floods, and droughts, while air pollution contributes to over 7 million premature deaths annually. Vector-borne diseases such as dengue are expanding into new areas, and zoonotic spillovers like COVID-19 and Nipah virus highlight the link between environmental degradation and emerging infectious diseases. These realities affirm that health is now ecologically determined, and that doctors must understand and address these upstream environmental risks to provide effective care.

    In response to these challenges, the World Health Organization (WHO) and the World Federation for Medical Education (WFME) now recommend integrating planetary health into medical curricula. Future doctors must be equipped with competencies in climate risk assessment, sustainable clinical practice, and systems-based thinking. The healthcare sector itself contributes 4 to 5 percent of global carbon emissions, making it essential for doctors to also lead in reducing environmental harm within their own institutions.

    This presentation argues that for IIUM, the integration of planetary health is both an educational imperative and a spiritual obligation. Islamic principles of amanah (trust), khalifah (stewardship), and islah (restoration) position doctors as protectors of creation. Therefore, planetary health is not only a scientific and ethical duty but a reflection of divine accountability.

    We propose a way forward by embedding planetary health themes into existing modules rather than adding standalone content. This includes training lecturers through workshops and toolkits, localising content using Malaysian case studies such as haze and floods, and updating assessment methods to include reflections, OSCEs, and community projects. The curriculum should also foster interdisciplinary collaboration and community engagement. Examples such as the University of Oslo’s climate-health elective and the UCSF-led Planetary Health Report Card showcase how medical schools globally are incorporating planetary health into education and advocacy.

    IIUM is uniquely positioned to become a model for Islamic and global planetary health leadership. By aligning curriculum reform with the university’s vision of realising competence, compassion, and conscience, IIUM can produce graduates who are not only clinically excellent but also ethically grounded and ecologically responsible.

    This presentation concludes with a call to action for IIUM to champion planetary health as a core medical competency. In a world facing climate disruption and ecological collapse, doctors must rise as trusted voices, informed healers, and stewards of both human and planetary wellbeing. May Allah guide us in this mission.

    Download the HERE.

  • To the One Who Walks After Me – A Reminder from a Lonely Shepherd

    Dear successor, take this post with honour in your stride,
    But know this seat holds not just title, it bears the weight inside.
    You will walk between the mountain peaks and valleys deep and wide,
    A shepherd of a scattered flock, with few to walk beside.

    They will speak of vision, grand and vast, of goals that must be met,
    But many will not see the path, nor share the burdens set.
    Above you, voices press for more, results without delay,
    Below, the voices ask for more, and rarely look your way.

    You will serve as bridge between the two, pulled firm from either end,
    Yet find, at times, you stand alone, with no resource, post, or friend.
    Some staff will shine and give their all, their spirits worn but true,
    While others find the shadows safe, and leave the work to you.

    Stay true to the course, hold firm your ground, let not your heart grow cold,
    This journey calls for selfless steps and courage to be bold.
    Power will tempt and praise may blind, but keep your honour high,
    For only with sincerity can you lead beneath Allah’s sky.

    So when the silence deafens you, and hopes begin to fray,
    Lift your heart beyond the noise and let Allah guide your way.
    He hears the cries you never voice, provides the help that none is able to see,
    And grants the strength no hand can give, to serve with dignity.

    You are not here for praise, nor comfort, ease, or gain,
    But to plant seeds you may not reap and lead through joy and pain.
    And when your time is at its end, as mine has come to be,
    May you find peace in your heart, knowing you served for Him, not anyone else.