In 2026, many JMAI authors make outstanding contributions to our journal. Their articles published with us have received very well feedback in the field and stimulate a lot of discussions and new insights among the peers.
Hereby, we would like to highlight some of our outstanding authors who have been making immense efforts in their research fields, with a brief interview of their unique perspective and insightful view as authors.
Outstanding Authors (2026)
Sherry J. H. Feng, Auckland University of Technology, New Zealand
Maarten P. D. Schadd, Netherlands Organisation for Applied Scientific Research, The Netherlands
Cessie Valenie Anak Edwin, Universiti Malaysia Sarawak, Malaysia
Alexandros Sagkriotis, Real-World Evidence, UK
Bhavani Patel, Independent Reviewer, USA
Kendah Saif, King Abdulaziz University, Saudi Arabia
Mehtap Agirsoy, Rensselaer Polytechnic Institute, USA
Miriam Miima, Mama Lucy Kibaki Hospital, Kenya
Pengpeng Ma, The Environmental Molecular Medicine Lab, USA
Sahar Abdulkarim AlGhareeb, Imam Abdulrahman Bin Faisal University, Saudi Arabia
Sayeri Lala, Stanford University, USA
Benson A. Babu, Wyckoff Medical Center, USA
Terna Nomhwange, Global Health and Infectious Disease Institute, Nigeria
Yannis M. Paulus, Johns Hopkins University Wilmer Eye Institute, USA
Outstanding Author
Sherry J. H. Feng
Dr. Sherry Feng’s journey in AI began with building a simple machine learning model for a user playing rock–paper–scissors against a “smart” computer - an experience that first revealed to her the transformative power of artificial intelligence. This early spark led her to pursue the field more deeply, eventually becoming one of the youngest AI consultants at the United Nations, where she applied AI to global challenges. She later pursued a PhD specialising in explainable large language models, with a focus on statistical and computational geometry. This work was driven by a strong belief that AI systems must be transparent and interpretable to be truly trustworthy. Since then, she has gained experience across academia, industry (including Amazon), startups, and government. Notably, she contributed to AI policy development by supporting the New Zealand Prime Minister’s Chief Science Advisor. Most recently, she founded CustodianAI, an AI startup commercialising her research in large language models.
In Dr. Feng’s view, a good academic paper is efficient and to the point - it respects the reader's time while delivering its contribution with clarity and precision. Beyond that, she believes that a strong paper provides sufficient resources and detail for others to replicate its findings, as reproducibility is fundamental to scientific integrity. It should be grounded in robust, supportive evidence, and perhaps most importantly, it should open doors rather than close them - inspiring discussion, inviting critique, and encouraging others to push the field further.
According to Dr. Feng, avoiding bias starts with a genuine effort to understand all perspectives surrounding a topic, even those that challenge your own position. She rigorously critiques ideas, including her own, and embraces intellectual humility in her writing. She reminds herself that her contributions are not the definitive answer, but rather part of an ongoing conversation.
Lastly, Dr. Feng would like to encourage other academic authors, “It is normal to feel like your research is stagnant; this feeling is more common than many admit. What I have come to appreciate is that sometimes discovering a way that does not work is just as valuable as finding one that does. Even shifting the needle slightly in a field is meaningful progress. Keep going! The work you are doing matters more than you realise.”
(by Sasa Zhu, Brad Li)
Maarten P. D. Schadd
Maarten Schadd is a scientist in the Intelligence & Decision Support department at TNO, specializing in Artificial Intelligence (AI) for decision support systems that enhance military planning processes. He holds both an MSc and a PhD in Artificial Intelligence and is an expert in Monte Carlo techniques and optimization algorithms. Building on his experience with complex interacting systems, he now leads research on advanced predictive tools for operational decision support, which incorporate the use of biophysical models alongside AI techniques. His recent work includes designing a hypoxia classifier and applying human metabolic models geospatially to support decision-making processes. He is passionate about bridging theoretical AI research with practical, real-world applications that improve operational effectiveness and safety.
Maarten regards a good academic paper as one that combines rigorous methodology with clear and compelling communication. In his field, a paper should present innovative solutions or approaches that advance understanding, whether it is a novel optimization algorithm, a machine learning approach, or the integration of AI with biophysical models. Data and evidence must be robust, reproducible, and transparent, allowing others to build upon the work. Equally important is relevance: the study should address a real-world problem or provide actionable insights for decision support. Clear structure, precise language, and well-justified conclusions are essential. In essence, a strong paper demonstrates originality, credibility, and practical significance while making complex ideas accessible to its audience.
In Maarten’s view, one of the biggest challenges is finding a clear structure and storyline for the article. It is important not to be afraid of restructuring the entire paper if it feels like it is not working. Repetition is often a clear signal that the flow needs adjustment. As authors write, they also start uncovering weaknesses in the methodology, which can sometimes require rerunning experiments or refining analyses. That is why thorough documentation of data, code, and procedures is essential. It ensures they can efficiently revisit experiments, reproduce results, and maintain the integrity of the research. Balancing clarity, structure, and methodological rigor is often the trickiest, yet most crucial, part of academic writing.
Maarten believes that academic writing offers the unique opportunity to share ideas and results with the broader scientific community and contribute to the advancement of knowledge. It allows authors to connect with other researchers who read and build upon their work, fostering collaboration and intellectual exchange. Equally important, it provides a structured way to document scientific foundation, which not only strengthens the credibility of current research but also supports future endeavors, such as project proposals or funding applications. In essence, academic writing is both a means of communication and a tool for establishing lasting scientific impact.
(by Sasa Zhu, Brad Li)
Cessie Valenie Anak Edwin
Cessie Valenie Anak Edwin is a postgraduate student at the Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak. She holds a degree in Software Engineering and is currently pursuing a master’s degree with a focus on artificial intelligence. Her research centers on the application of AI techniques in the context of bronchial asthma, with particular interest in predictive modeling and data-driven healthcare solutions. Recently, her work has involved developing machine learning models to assess asthma risk, improve early detection, and support asthma management. Through her studies and research, she aims to contribute to the advancement of intelligent healthcare systems that enhance patient outcomes and support clinical decision-making.
Cessie believes that a good academic paper effectively communicates knowledge with clarity, precision, and intellectual rigor. It should be structured logically and coherently, allowing readers to easily extract key information and understand the development of ideas. Beyond readability, a strong paper demonstrates methodological soundness, critical analysis, and a clear linkage between research questions, methods, results, and conclusions. Importantly, it should be accessible to its intended audience without oversimplifying complex concepts. Ultimately, a high-quality academic paper contributes meaningfully to the advancement of its field by offering new insights, validating existing knowledge, or opening pathways for further research.
Cessie highlights that a researcher must first recognize common biases to effectively mitigate them in writing. Examples of biases include confirmation bias, sampling bias, language bias, or analytical bias. Avoiding bias in academic writing requires conscious awareness of its sources and the disciplined application of objective, evidence-based practices. Bias often arises from personal beliefs, cultural assumptions, selective use of data, or methodological limitations. Peer review and feedback also play a critical role, as external perspectives can identify overlooked assumptions or interpretive distortions. Ultimately, avoiding bias involves maintaining intellectual honesty, grounding arguments in verifiable evidence, and presenting findings in a balanced and transparent manner.
“Research serves as the foundation for addressing complex and evolving challenges within society as they form the essential building blocks within the broader body of knowledge. Scientific progress is inherently cumulative; small, well-executed studies, when combined over time, generate significant impact and drive meaningful advancements in the field. To fellow academic writers, continued dedication, intellectual curiosity, and perseverance are vital. The process of research can be demanding, but each rigorous inquiry, validated finding, and thoughtfully articulated paper contributes to a larger, collective effort to expand understanding and improve real-world outcomes. In this sense, every contribution—no matter how small—plays a meaningful role in shaping the future of scientific discovery,” says Cessie.
(by Sasa Zhu, Brad Li)
Alexandros Sagkriotis
Alexandros Sagkriotis is a leader in Real-World Evidence (RWE) with more than 32 years of experience in the pharmaceutical industry. He is the founder of Helios Academy Ltd, an independent initiative focused on evidence strategy, methodological innovation, and leadership development—guided by the principle “Where Science Meets Compassion.” His work spans oncology, ophthalmology, dermatology, and data science, with a particular emphasis on integrating real-world data, artificial intelligence, and regulatory frameworks into coherent evidence architectures. He has contributed to advancing RWE through publications, policy engagement, and strategic advisory roles aligned with EMA, FDA, and HTA expectations. His recent work explores the transformation of registries, the role of AI in evidence generation, and the ethical dimensions of data use. Alongside his scientific work, he is an accredited coach, supporting leaders in navigating complexity with integrity and clarity.
In Alexandros’s view, academic writing is essential because it creates a structured, transparent, and reproducible foundation for knowledge generation. In healthcare, where decisions affect patient outcomes and societal trust, evidence must be communicated with rigour and accountability. However, academic writing should do more than document—it should challenge. Too often, progress is slowed not by lack of data, but by entrenched thinking. There are clear examples where fixed mindsets can dismiss emerging fields such as artificial intelligence in healthcare, not because evidence is lacking, but because it does not fit established narratives. This resistance can suppress curiosity, delay innovation, and ultimately limit the development of solutions that enable personalised medicine. Academic writing gives researchers the responsibility—and the platform—to question these limitations. It allows them to be explicit about what is not working, to push boundaries, and to ensure that scientific progress serves all patients, not just established systems.
According to Alexandros, an author in scientific research must combine methodological rigour with ethical responsibility. This includes clear research questions, an appropriate study design, and transparent reporting of limitations. Integrity is critical—authors must avoid overstating findings, resist publication bias, and ensure that conclusions are driven by evidence rather than expectation.
Beyond technical excellence, authorship is an act of leadership. It requires a willingness to share knowledge, to help others, and to move the needle in how science is understood and applied. Writing should trigger discussion, critical thinking, and, at times, uncomfortable self-reflection. This demands courage, but also respect and humility. Emotional intelligence is essential, as readers may recognize themselves in what is written. Authorship ultimately requires persistence, resilience, and a clear purpose—contributing to improved science, better decisions, and more equitable patient outcomes.
Alexandros stresses that data sharing is important, but it should not be treated as an absolute principle—it is a responsibility that must be exercised with care. When done properly, it enhances transparency, enables reproducibility, and accelerates scientific progress, particularly in fields such as real-world evidence and artificial intelligence. However, one of the biggest challenges is not only access to data, but also what authors choose to share. Too often, they publish what aligns with our narratives and underreport less favourable findings. This publication bias limits learning. They need a shift in mindset—from celebrating only successes to also valuing failures, as these are often the most informative. In this sense, the goal is not simply to share data, but to share honest evidence. This requires governance, context, and integrity to ensure that data serve science with integrity—and ultimately lead to better, more equitable patient outcomes.
(by Sasa Zhu, Brad Li)
Bhavani Patel
Bhavani Patel is an R&D Scientist specializing in biocompatibility of medical devices at a leading global medical device manufacturer headquartered in the United States. She holds a master’s degree in medical biotechnology, and her work centers on the biological safety evaluation of medical devices in compliance with ISO 10993 and global regulatory standards. She is increasingly interested in seeing how real-world clinical data, post-market surveillance, and emerging digital tools, including AI, can improve how researchers evaluate and monitor device safety and performance of medical devices. Her recent research looks at how AI-enabled wearables can support earlier detection, continuous monitoring, and personalized clinical care. Her broader research interests bring together medical device safety, digital health, and artificial intelligence.
In Bhavani’s view, a good academic paper starts by addressing a question that actually matters to fellow researchers and the scientific community. Followed by a clear scope, reproducible methodology, and clear results interpreted honestly, with limitations included. Most papers are valued for thinking beyond the lab bench and asking what the findings mean for safer products or better clinical decisions. Clarity matters as much as rigor to her. Ideas in AI, biomaterials, or predictive healthcare are only useful if a multidisciplinary audience can actually follow them. A strong paper should also be in genuine conversation with the existing literature, acknowledging what came before, where it builds on that work, and where it pushes back, while giving the reader a real sense of what comes next.
Bhavani states that the best authors she has learned from are genuinely interested in the problem, not just in getting something published. She thinks intellectual honesty matters enormously. That includes reporting inconvenient results, being upfront about limitations, and fairly crediting the people whose work they have built on. A strong research article critically reads the literature, identifies real gaps, and offers something the reader can actually use. Patience is part of it, because strong papers rarely come together on the first draft. Working in a regulated industry has made her especially careful about precision. Every claim has to be anchored in evidence, and wording has to be chosen deliberately, particularly when readers may be making clinical or regulatory decisions based on it. In healthcare and AI, especially, authors carry a responsibility to think about the real-world consequences of their conclusions.
“I chose to publish in JMAI because its scope mapped almost perfectly onto what I was trying to say. There are not many journals focused specifically on medical AI, and that specificity made a real difference. The paper reached the scientific community, who are already thinking about where AI in healthcare is promising, where it is overhyped, and how to implement it responsibly. I also value that JMAI is open access and rigorously peer-reviewed. For a topic like AI-integrated wearables, where the audience genuinely cuts across clinicians, engineers, and regulatory scientists, having the work freely available felt important to me,” says Bhavani.
(by Sasa Zhu, Brad Li)
Kendah Saif
Kendah Saif is a Master’s student in Information Systems at King Abdulaziz University, with an academic focus on the application of machine learning in healthcare. Her research interests include predictive modeling and the use of data-driven approaches to support clinical decision-making. Her recent work explores modeling techniques for predicting outcomes in clinical contexts, with an emphasis on improving accuracy, interpretability, and practical applicability. Alongside her research, she has developed foundational experience in system analysis and information systems, including work in system design, data handling, and basic analytical processes. Kendah aims to continue developing her skills in data analytics and machine learning while contributing to practical and responsible applications of these technologies in real-world settings. Connect with her on LinkedIn.
Kendah thinks that a good academic paper is one that clearly defines a meaningful research problem and addresses it using a well-structured and rigorous methodology. It should provide original insights or contributions, whether theoretical or practical, and present results in a transparent and reproducible manner. Clarity, logical flow, and proper interpretation of findings are essential. Additionally, a strong paper connects its results to real-world applications or broader implications, making the research both relevant and impactful.
Kendah highlights that authors should ensure that their research question is clearly defined and supported by appropriate methods and data. Consistency between objectives, methodology, and results is crucial. They should also prioritize clarity in writing, avoid unnecessary complexity, and present their findings in a structured and logical way. Ethical considerations, such as proper citation, data privacy, and transparency, must always be maintained. Finally, authors should consider the expectations of their target journal and ensure their work contributes meaningfully to the existing literature.
Kendah is motivated by the impact that research can have on real-world problems, especially in healthcare. Knowing that her work could contribute, even in a small way, to improving clinical decisions or patient outcomes encourages her to continue despite the challenges. She is also motivated by the process of learning and discovery, as research allows her to explore new ideas, think critically, and continuously develop her skills. In addition, she finds value in the problem-solving aspect of research, where each step—from data analysis to interpreting results—offers an opportunity to better understand complex issues. Over time, this process has strengthened her interest in applying data-driven approaches to meaningful problems. Ultimately, the ability to contribute to knowledge while developing both technical and analytical skills is what keeps her engaged in academic writing.
(by Sasa Zhu, Brad Li)
Mehtap Agirsoy
Mehtap Agirsoy is a PhD Candidate in Mechanical Engineering at Rensselaer Polytechnic Institute (RPI), where she specializes in Explainable AI (XAI), Machine Learning (ML), and Quantum Machine Learning (QML) for precision medicine. Her research bridges the gap between physics-based modeling and AI-driven solutions, with a particular focus on building interpretable, end-to-end pipelines for medical imaging and clinical decision support. By integrating deep learning, predictive modeling, and Explainable AI, she aims to enhance reliability and clinical trust in high-stakes healthcare environments. Beyond her work in Quantum-Accelerated Consensus Protocols and Qiskit 2.x standards, she has extensive experience in computational fluid dynamics (CFD) and experimental fluid mechanics, applying these multidisciplinary tools to improve diagnostic accuracy in reproductive health and geriatric care.
JMAI: What do you regard as a good academic paper?
Dr. Agirsoy: A good academic paper must bridge the gap between technical complexity and clinical utility. In my field of Explainable AI, a "good" paper doesn't just present a high-performing model; it provides transparency that allows clinicians to trust and validate the results. It should offer a clear "master key" to unlocking complex data while remaining grounded in real-world constraints, such as those found in geriatric care or high-performance computing environments.
JMAI: How do you keep your writing current and provide fresh insights for research?
Dr. Agirsoy: I stay at the forefront by integrating emerging technologies like QML with traditional mechanical engineering competencies, such as Computational Fluid Dynamics (CFD) and physics-based modeling. I am currently in the Noisy Intermediate-Scale Quantum (NISQ) era, where quantum hardware is not yet fully applicable for all large-scale industrial tasks. I ensure my writing remains technically current by utilizing the latest Qiskit 2.x standards and 2026 hardware protocols. By focusing on hybrid classical-quantum pipelines, I provide insights that are practically applicable today while preparing for future scalability. This "ready-now" approach is further sharpened through participation in innovation competitions, where I pivot academic research into real-world solutions for sectors like geriatric healthcare and precision medicine, ensuring my work addresses current market needs while anticipating the next technological leap.
JMAI: Do you have an interesting story about your experience with academic writing?
Dr. Agirsoy: One of the most memorable moments in my recent writing was the shift to integrating QML into my research. I recall the steep learning curve of transitioning from classical AI models to the Qiskit 2.x standard and 2026 hardware operations. There was a specific "aha!" moment while drafting a proposal for a Quantum-Accelerated Consensus Protocol, where I realized how the HHL algorithm, typically a theoretical powerhouse, could practically solve trust diffusion in blockchain. In the quantum field, where theoretical hype often outpaces practical application, establishing a proof of reputation through rigorous, peer-reviewed technical writing is vital. This "aha!" moment served as a bridge between my foundational work in Mechanical Engineering and the frontier of quantum computing, providing the academic credibility needed to navigate these complex disciplines. It reminded me that the most interesting stories in academic writing happen at the intersection of two seemingly unrelated fields and that technical breakthroughs are most impactful when they move beyond theory into a proven, research-backed framework.
(by Sasa Zhu, Brad Li)
Miriam Miima
Dr. Miriam Miima is a Family Physician working at the intersection of clinical care, healthcare management, and digital innovation. She holds a Master’s degree in Family Medicine and a Fellowship in Geriatrics and Gerontology, and currently practices at Mama Lucy Kibaki Hospital. Her work focuses on designing and strengthening patient-centered, technology-enabled healthcare systems across the life course. She has a strong interest in digital health, healthcare governance, and the application of emerging technologies to improve system performance and patient outcomes in Sub-Saharan Africa. Her recent work has centered on developing governance frameworks to support the safe and effective development and deployment of artificial intelligence in healthcare, with institutional affiliation to Strathmore University. She is preparing to embark on her PhD journey at the same institution, where her research will focus on medical digital misinformation, with particular emphasis on its impact on healthcare systems, patient behavior, and trust in digital health ecosystems.
Dr. Miima asserts that academic writing is not just about publishing; it is a credible record of truth that drives scientific progress. When authors share reproducible knowledge open to critique and feedback, they harness collective intelligence that informs best practices, policy, and innovation. Academic writing transforms complex ideas from concepts to actions that can be challenged and iterated over time. Without academic writing, knowledge remains siloed and anecdotal; with it, they generate evidence, advance science, strengthen health care systems, and improve healthcare outcomes.
According to Dr. Miima, academic writing is very demanding, and the rewards are often delayed. However, this is a labor of love whose impact is bigger than each individual author. Every published paper contributes to knowledge systems that shape scientific practice and solve real-world problems. Perfection is not always the goal; the goal is clarity, integrity, and curiosity to embrace intellectual pushback for stronger ideas that stand to benefit real human lives. In a nutshell, keep writing: to inform, build, innovate, and improve the lives of the individual and communities around them.
Dr. Miima believes it is crucial for authors to share their research data. This should be anchored on ethical and contextualized data governance principles. Data sharing promotes transparency, reproducibility, and accelerates discovery. In resource-constrained areas, data sharing fosters collaboration while maximizing data utility across regions. Authors should share data whenever ethically and legally feasible to avoid duplication of effort. Responsible data sharing strengthens trust in research and significantly advances science, innovation, and discovery.
(by Sasa Zhu, Brad Li)
Pengpeng Ma
Dr. Pengpeng Ma is a Principal Investigator at The Environmental Molecular Medicine Lab, Inc. He received his Ph.D. from Peking Union Medical College in 2005 and completed his postdoctoral training at the University of Pennsylvania, where he developed a long-standing interest in understanding how molecular mechanisms influence human health at its earliest stages. His research has since expanded to incorporate artificial intelligence into clinical research. He is particularly interested in leveraging AI-driven approaches to accelerate early-stage drug discovery, especially in cancer therapy. By integrating molecular medicine with data-driven methodologies, his goal is to bridge basic research and clinical application, ultimately contributing to more precise and effective therapeutic strategies.
Dr. Ma thinks that academic writing plays a fundamental role in science as it serves as the primary means of communicating ideas, discoveries, and evidence to the broader scientific community. It not only allows researchers to share their findings in a structured and rigorous manner but also ensures transparency, reproducibility, and critical evaluation. Through academic writing, scientific knowledge becomes cumulative—each study builds upon prior work, advancing the field forward. Beyond communication, academic writing also shapes how scientists think. It requires clarity, precision, and logical reasoning, helping researchers refine their hypotheses and interpretations. In his view, it is not just a way to report results, but an essential part of the scientific process itself. Particularly in interdisciplinary areas such as AI in clinical research, effective writing is crucial for bridging different domains and translating complex concepts into actionable knowledge.
From Dr. Ma’s perspective, selecting appropriate evidence for synthesis requires a clearly defined research question, as this determines the relevance and scope of included studies. He prioritizes high-quality, peer-reviewed literature and carefully evaluates study design, sample size, methodology, and potential bias. It is also important to include diverse yet comparable sources to ensure a balanced and comprehensive synthesis, particularly in interdisciplinary fields. During analysis, authors must remain objective and transparent by clearly documenting inclusion criteria and acknowledging limitations. Consistency in evaluating and comparing findings across studies is essential. In his experience, effective evidence synthesis goes beyond summarization—it involves identifying patterns, contradictions, and knowledge gaps that can guide future research. Maintaining critical thinking throughout the process ensures that conclusions are both reliable and scientifically meaningful.
Dr. Ma believes that reporting guidelines such as STROBE, CONSORT, PRISMA, STARD, and CARE are essential in scientific manuscript preparation. These frameworks provide a standardized structure that enhances clarity, transparency, and reproducibility, ensuring that key elements of study design, methodology, and results are thoroughly and consistently reported. This allows readers and reviewers to properly evaluate the robustness and validity of the work. From his perspective, these guidelines also function as an important discipline, guiding the writing process and reducing the risk of incomplete reporting. In complex and interdisciplinary fields such as AI in clinical research, adherence to such standards is particularly critical for ensuring interpretability and scientific rigor. Ultimately, these guidelines strengthen the credibility, reliability, and long-term impact of published research.
(by Sasa Zhu, Brad Li)
Sahar Abdulkarim AlGhareeb
Sahar Abdulkarim Al-Ghareeb is a Lecturer in the College of Nursing at Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia, and a PhD candidate in the College of Nursing at King Saud University, Riyadh, Saudi Arabia. Her scholarly interests center on review methodologies and quantitative research, with a particular focus on oncology, women’s health, and patient safety. More broadly, she is committed to advancing the field of nursing research by generating and disseminating evidence to inform clinical practice and health policy. Her recent publications have addressed the use of artificial intelligence in cancer diagnosis and the mental and psychological aspects of breast cancer survivorship, specifically examining self-compassion, mindfulness, and perceived stress. She has also conducted psychometric analyses of the Self-Compassion Scale and analyzed the construct of self-compassion. In addition, her work includes studies on nurses’ adherence to pressure injury prevention protocols and on preventing patient falls.
In Sahar’s view, a high-quality academic paper should clearly articulate the researcher’s objectives. It must present a well-defined research aim, accompanied by feasible, measurable objectives, and a clearly stated analysis plan. All components of the study should be logically integrated, ensuring a coherent progression from the title through to the conclusion. The abstract should provide sufficient detail to accurately represent the paper’s overall content. Furthermore, the researcher should avoid overly complex language and highly specialized terminology whenever possible, so that individuals outside the immediate field can readily comprehend the work. Presenting selected content as graphs, figures, or tables can enhance clarity and facilitate visualization, making it easier for readers to grasp the meaning of the text. Consequently, unnecessary repetition of the same information across multiple sections, including visual illustrations, should be avoided.
In Sahar’s view, authors should cultivate key competencies, such as identifying research needs in their field through a critical review of the literature, recognizing research gaps, capitalizing on their methodological and conceptual strengths, and building effective research teams to enhance the overall quality of their work. In addition, they should develop advanced academic writing skills, including precise use of language, accurate grammar, and proper punctuation. The capacity to interpret reviewers’ comments and communicate responses effectively to the scholarly community further improves the quality of the manuscript. Authors should also possess strong editing and self-revision skills and engage with all components of the research process to deepen their knowledge, refine their skills, and broaden their experience. A fundamental requirement is adherence to ethical guidelines and the consistent observance of deadlines throughout the research process, peer review, and journal submission of revisions.
“An interesting experience occurred during my PhD while working on my first original study as the primary and corresponding author,” shares Sahar, “As a non-native English speaker, I found it challenging to present the study components in an appropriate academic format. I was required to submit the first-round revision while simultaneously preparing for my comprehensive examination. Managing both tasks in a limited time frame was stressful. However, I decided that revising the manuscript and responding to reviewers’ comments would reinforce my research knowledge and support my exam preparation. This decision reduced my anxiety and contributed to my strong performance and positive evaluation by the exam committee. The paper received a major revision decision, requiring substantial changes to the title, objectives, analysis plan, and results. This experience taught me that active research practice enhances skills more effectively than studying alone. Also, as a non-English author, it is fine to ask for assistance with proofreading.”
(by Sasa Zhu, Brad Li)
Sayeri Lala
Sayeri Lala, PhD, is a Postdoctoral Scholar at Stanford University in the Quantitative Sciences Unit and Department of Neurosurgery. Throughout her career, she has utilized AI to enhance clinical workflows, including data acquisition, trial design, and long-term patient monitoring. She earned her PhD in Electrical and Computer Engineering from Princeton (Neuroscience minor) and her SB and MEng in Electrical Engineering and Computer Science from MIT, where she trained convolutional neural networks to mitigate artifacts in fetal brain MRI. Her PhD work combined deep learning and causal inference to improve randomized controlled trials, yielding peer-reviewed publications. She also completed AI/ML internships at Apple, modeling health outcomes from wearables. At Stanford, she develops AI-based predictive tools to transform clinical decision-making in neurosurgery and cancer, integrating real-world patient signals with multimodal records to model long-term outcomes beyond episodic assessments, enabling earlier, personalized predictions of recovery, survival, and disease recurrence.
In Dr. Lala’s view, academic writing disseminates findings so the field can move forward efficiently. A good paper constructs a narrative — problem, significance, takeaways — supported by figures, headings, a clear title, and an abstract. Method details are essential for reproducibility, and keywords help readers discover the work. Writing also sharpens thinking: drafting forces rigor that exposes weak logic, and peer review further validates and refines the contribution.
Speaking of allocating time for writing, Dr. Lala states the easiest win is baking writing into the project plan from the start — aligning drafts with grant or other milestones so the paper is not an afterthought. A figure-first approach helps: locking in the main figures early often clarifies the story before prose is written. LLMs can assist with proofreading, experimental work (e.g., coding), freeing time for pursuing more experiments and crafting the narrative.
In addition, Dr. Lala expresses that institutional review board (IRB) approval ensures research respects participants’ autonomy, balances risks against benefits, and distributes both fairly. It protects the researcher, since federal regulations (e.g., HIPAA) require review, and institutions can terminate noncompliant studies. Skipping it renders findings unpublishable and unfundable, exposes the investigator legally, and erodes public trust in science.
(by Sasa Zhu, Brad Li)
Benson Babu
Dr. Benson A. Babu, MD, MBA, FACP, is an academic hospitalist, educator, and researcher with appointments at Wyckoff Medical Center, Mount Sinai Health System, and New York Medical College. His academic work focuses on the integration of artificial intelligence into clinical medicine, with interests spanning diagnostic imaging, predictive modeling, digital pathology, workflow automation, and physician-centered technologies that improve healthcare delivery and patient outcomes. Dr. Babu has authored numerous peer-reviewed publications and books and has delivered invited lectures nationally and internationally on healthcare AI, innovation, and medical technology. He also serves as a reviewer for several medical and artificial intelligence journals. In addition to his clinical and academic responsibilities, he is actively involved in AI innovation initiatives and healthcare technology entrepreneurship, with a particular interest in practical and ethical applications of AI in medicine and medical education. Follow him on LinkedIn.
One of the biggest challenges in academic writing, according to Dr. Babu, is translating complex ideas into clear, concise, and meaningful communication. Many researchers struggle with balancing scientific rigor with readability. Time constraints are another major issue, especially for physicians and scientists managing clinical, teaching, and administrative responsibilities. In addition, selecting the right study design, organizing data effectively, and responding to peer-review feedback can be demanding. In rapidly evolving fields like healthcare AI, staying current with literature while ensuring accuracy and ethical considerations adds another layer of complexity. Persistence, collaboration, and continuous refinement are essential parts of the writing process.
Writing papers requires intentional structure and consistency. As a hospitalist and educator, Dr. Babu’s clinical responsibilities are significant, so he approaches writing as a longitudinal process rather than waiting for large blocks of free time. He often dedicates smaller focused sessions during early mornings, evenings, or weekends to outlining ideas, reviewing literature, or editing manuscripts. Collaboration is also critical — working with motivated co-authors, residents, and students allows projects to progress efficiently. Importantly, he tries to align his research with questions he encounters in clinical practice or healthcare innovation, which makes the work both practical and intellectually rewarding. AI tools and workflow automation have also helped improve efficiency in literature review, organization, and manuscript preparation.
“Academic writing is fascinating because it transforms ideas into knowledge that can influence patient care, education, and future research. It allows clinicians and scientists to contribute beyond individual patient encounters and become part of a larger global conversation,” says Dr. Babu, “I especially enjoy the intersection of medicine and artificial intelligence, where new discoveries can rapidly reshape healthcare delivery and decision-making. Writing also forces deeper thinking — it challenges us to critically analyze evidence, identify gaps, and communicate solutions clearly. Perhaps most rewarding is the ability to inspire collaboration and innovation while mentoring students and younger physicians who will shape the future of medicine.”
(by Brad Li, Masaki Lo)
Terna Nomhwange
Dr. Terna Nomhwange is a Nigerian-trained medical doctor and global health specialist with advanced qualifications in tropical medicine, international health, and health systems reform from the London School of Hygiene & Tropical Medicine, the Royal College of Physicians, the Harvard School of Public Health, and the World Bank Institute. He is a Fellow of the Institute of Management Consultants and an International Practitioner of the UK Faculty of Public Health. With over 20 years of experience, his career has spanned working in public clinical and community health settings as well as with the WHO at national, regional and global levels. His work spans infectious diseases, vaccination, disease control, and global health policy, with a growing focus on how emerging technologies, innovative tools, as well as diplomacy can strengthen the interface between health management, geopolitics, and sustainability. Dr. Nomhwange is committed to advancing ethical, equitable, and context‑appropriate solutions that improve access to essential health services and drive better outcomes for populations in low‑ and middle‑income countries, particularly across the Global South. Follow him on LinkedIn.
Dr. Nomhwange believes that the most important skill for authors is an inquisitive mind—the willingness to question every piece of information. Curiosity and critical inquiry form the foundation of meaningful scholarship, driving the validation or expansion of existing knowledge. To achieve this, authors must clearly define their areas of interest from the outset. This guides continuous learning, deeper engagement with field developments, pursuit of high-quality evidence, and fruitful collaborations. Integrity and transparency are the cornerstone of every author’s work and the broader academic community—a red line that must never be crossed. Ultimately, authorship is not merely about writing; it is about cultivating a mindset that seeks understanding, challenges assumptions, and contributes to collective progress. By staying informed, collaborating, and remaining intellectually curious, authors can produce rigorous and impactful evidence.
Dr. Nomhwange emphasizes that recognizing and clearly articulating limitations is fundamental in academic writing. Authors must openly outline potential biases affecting their study’s methods, results, and conclusions. This transparency helps readers interpret findings in the proper context. A well-designed, transparent methods section is essential. It must detail how the study was planned, implemented, and monitored. Researchers should stay faithful to the original question, report results exactly as found (positive or negative), and avoid overgeneralization. Any deviation risks introducing bias. Bias is best minimized through collaboration. Teamwork brings diverse perspectives that help identify overlooked biases and strengthen the publication’s integrity. As researchers gain experience, they become better at recognizing and addressing these challenges. This ongoing commitment to rigor enhances the credibility of current research and builds stronger evidence for future generations.
“My motivation (to engage in academic writing) is rooted in a deep commitment to contributing meaningfully to the global body of knowledge. I am particularly driven by the urgent need to address equity gaps in research and to ensure that context‑specific evidence is generated for low‑ and middle‑income countries. Without such evidence, health interventions risk being misaligned with local realities, ultimately limiting their impact,” says Dr. Nomhwange, “I believe that new tools and innovations, whether technological, methodological, or policy‑driven, must be evaluated and implemented using the right evidence: evidence that reflects the lived experiences, systems, and constraints of the communities they are meant to serve. Producing this kind of knowledge requires time, discipline, and sustained effort, but it is essential. In the long run, this is what we owe our children and future generations: a commitment to learning, writing, and improving. Knowledge generation is the foundation on which better, fairer, and more sustainable health systems will be built.”
(by Brad Li, Masaki Lo)
Yannis M. Paulus
Dr. Yannis M. Paulus, MD, FACS, FARVO, FASRS, is an academic vitreoretinal surgeon and clinician scientist that directs an active, multidisciplinary lab applying optics, photonics, biomedical engineering, regenerative medicine, biodesign, and nanoparticles to develop novel retinal imaging and therapies. He is the Jonas Friedenwald Professor and Associate Professor, Department of Ophthalmology and Department of Biomedical Engineering at Johns Hopkins University Wilmer Eye Institute. He serves as an inventor on 11 patent applications; has more than 250 peer-reviewed publications in leading journals such as Nature Communications, Nature Nanotechnology, Advanced Materials, Light: Science & Applications, ACS Nano, Science Advances, Ophthalmology, Lancet, and JAMA Ophthalmology; and has started 4 companies. He has received numerous fellowships and awards from international organizations, including the ARVO/Alcon Early Career Clinician-Scientist Research Award, American Academy of Ophthalmology Achievement Award, Senior Member Institute of Electrical and Electronics Engineers, Heed Ophthalmic Foundation, and Evangelos Gragoudas Award from the Macula Society. Learn more about him here, and follow him on LinkedIn.
Dr. Paulus regards a strong academic paper as one that rigorously applies the scientific method to test an important hypothesis capable of transforming how we see and interact with the world. He identifies a common challenge in academic writing as ensuring that the data speak for themselves without allowing personal biases to influence the interpretation. In his view, this requires rigorous application of the scientific method, detailed methods that enable any group to replicate the results, and full data disclosure to minimize interpretive bias.
“Academic writing is fascinating because it deepens our understanding of the world, enables rigorous hypothesis testing, and can ultimately improve lives through this greater insight,” says Dr. Paulus.
(by Brad Li, Masaki Lo)