In 2025, 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 (2025)

Qianhui Lu, Abbott Laboratories, USA

Charbel Alhelou, ArcaScience, France

Dharambir Mahto, Sriya.AI, USA

Hannah Ong, The Ohio State University, USA

Pedro Angelo Basei de Paula, Federal University of Paraná, Brazil

Wahid Ullah, Riphah International University, Pakistan

Nino Gvajaia, Tbilisi State Medical University, USA

Outstanding Author

Qianhui Lu

Qianhui Lu is a Staff AI Engineer with Abbott Laboratories spearheading Data and Innovation mission critical projects. Her curiosity and passion for AI sparked during her undergraduate years when she met a PhD candidate specializing in data mining and machine learning. Since then, she has sharpened her expertise in AI and machine learning through roles as a Machine Learning Engineer, AI Fellow, Data Scientist, and AI Engineer. She earned a bachelor's degree in engineering from Penn State University, followed by a master's in Innovation Management from Brown University, where she worked on computer vision algorithms for Augmented and Virtual Reality. She also holds a master's in Data Analytics from San Jose State University, where she honed her natural language processing (NLP) skills. Her career includes research in multi-modal semantics extraction and developing NLP tools for the legal industry. At Abbott, she harnesses cutting-edge technologies such as machine learning and AI to enhance health care solutions. She is dedicated to integrating AI technologies into tools and solutions that make a positive impact on people's lives.

JMAI: What do you regard as a good academic paper?

Qianhui: A stellar academic paper in the dynamic field of Artificial Intelligence (AI) and health care addresses significant and relevant issues, resonating with current challenges or advancements. It introduces fresh, innovative ideas or solutions, contributing new knowledge that pushes the boundaries of what's known. In my opinion, data and evidence are the backbone of any good paper. They should be robust, reliable, and convincingly support the results. The analysis should be thorough, with interpretations and conclusions that logically follow from the data presented. The potential impact or real-world application of the paper is another key factor. A great paper has the power to influence future research, policy, or practice, making a real difference in the field. In addition, the methodology must be rock-solid, clearly described, and well-justified, ensuring credibility and replicability. A comprehensive literature review situates the research within the broader context, highlighting unique contributions. Accurate and up-to-date references and citations are essential, showing the paper's foundation in established research. Clarity and organization are crucial as well, ensuring that the paper is a pleasure to read with a logical flow. In essence, a good academic paper blends robust data, significant impact, relevance, originality, clarity, solid methodology, thorough analysis, comprehensive literature review, and ethical integrity.

JMAI: What are the most commonly encountered difficulties in academic writing?

Qianhui: One of the biggest hurdles in academic writing is making complex ideas clear and engaging—like turning an outstanding LLM or complicated medical diagnostic algorithm into a user-friendly app. Balancing originality with thorough research is another tricky task—think of it as adding your unique AI implementation or medical breakthrough to a well-established protocol or heavily regulated space. Time management can also be a challenge as authors are usually juggling multiple projects, with research, writing, and revisions. Furthermore, handling feedback can be tough, but it's like fine-tuning a machine learning model or refining a treatment plan—sometimes it takes quite a bit of tweaking to get it just right. Despite these challenges, the process is incredibly rewarding.

JMAI: What is fascinating about academic writing?

Qianhui: Academic writing is like embarking on a thrilling adventure where one gets to be both a detective and an innovator. Academic writing allows us to dive deep into the unknown, explore uncharted territories, and contribute to the ever-evolving landscape of AI and health care. It's a platform where creativity meets rigor, where new hypothesis is tested and every result analyzed brings us closer to solving real-world problems. Plus, there's the thrill of seeing our work inspire others, spark new ideas, and drive future research. In essence, academic writing is a blend of passion, curiosity, and dedication. It's about pushing the boundaries of what's possible and making a tangible impact on the world.

(by Brad Li, Masaki Lo)

Charbel Alhelou

Charbel Alhelou is a Doctor of Pharmacy with master’s degrees in Clinical and Preclinical Pharmacology, Research in Pharmacoepidemiology, and Business Administration. He has contributed to various research projects, including a study on the impact of SGLT2 inhibitors on atrial fibrillation. With four years of experience in community pharmacy, he has developed a deep understanding of patient needs, effective communication, and the value of diverse perspectives in healthcare. Currently, as a Product Manager at ArcaScience, he focuses on leveraging AI to tackle key challenges in the pharmaceutical industry. He has contributed to various AI-driven healthcare projects, including the detection and classification of clinical endpoints, the development of a chatbot for hidradenitis suppurativa patients, a model for the detection of the publications type, and the creation of a drug-drug interaction (DDI) detection algorithm. Over the past 18 months, he has led the Benefit-Risk Assessment Project, integrating advanced technologies and prioritizing patient-centric approaches to enhance drug development.

From Alhelou’s perspectives, academic writing serves as a means of transmitting knowledge, while scientific writing provides a platform for sharing discoveries. To him, a researcher’s findings must be clearly understandable and well-documented to be considered valid evidence. Transmitting and publishing evidence is crucial in healthcare, as it leads to better evidence-based decision-making, ultimately improving patient health. In the field of AI, scientific writing is still in its early stages, making it essential to share even small improvements and findings. He believes this collaborative approach helps researchers and practitioners maximize the potential of AI while addressing ethical considerations and optimizing time management.

Dr. Alhelou reckons that transparency is crucial in academic writing—not just for conveying results, but also for clearly outlining the conditions under which those results were obtained. One of the main challenges in both science and AI is generalization. In his view, the more data are contextualized, the more researchers advance science without introducing bias.

Lastly, Dr. Alhelou emphasizes that science now needs a more comprehensive approach—one that includes all perspectives, failures, and every piece of information, no matter how small. This broader transparency is essential for making scientific research more effective in improving human lives.

(by Sasa Zhu, Brad Li)

Dharambir Mahto

Dharambir Mahto is a dedicated researcher specializing in artificial intelligence and machine learning applications. His primary research focuses on the comparative study of AI2-SXI versus traditional ML/DL models in predicting patient readmissions, aiming to enhance predictive accuracy and improve patient outcomes. With a strong background in data science and healthcare analytics, his recent work—conducted in collaboration with Sriya.AI—investigates the efficiency, interpretability, and scalability of AI2-SXI compared to conventional models. He is passionate about bridging the gap between advanced AI techniques and practical clinical applications, optimizing healthcare predictions, reducing hospital readmissions, and supporting evidence-based medical practices. Through his research, he strives to ensure that technology-driven solutions are both effective and ethically sound, further advancing AI’s role in healthcare.

From Dharambir’s perspective, a well-written academic paper includes several key elements. It should start with a clear and concise title that reflects the main focus of the study. The abstract should provide a brief summary of the research, highlighting the objectives, methods, key findings, and conclusions. A strong introduction sets the stage by explaining the research problem, its significance, and the study's objectives. The literature review should provide background information, discussing previous research and how the current study fits into the existing body of knowledge. A well-detailed methodology section ensures transparency by explaining how the research was conducted. The results section presents findings clearly, often supported by tables or figures, while the discussion interprets these results in relation to existing studies. Finally, the conclusion summarizes key takeaways and suggests potential directions for future research. Proper citations and references maintain academic integrity, and clear, concise writing makes the paper engaging and easy to understand.

Dharambir emphasizes that authors must consider several key factors when preparing their writing. First, they should clearly define their research question and ensure their study contributes something valuable to their field. The structure and organization of the paper should follow the guidelines of the target journal, including formatting, referencing style, and word limits. Authors must provide a thorough literature review to establish context and justify their research. The methodology should be detailed and transparent to allow replication. Data analysis must be accurate, and results should be presented clearly with appropriate figures and tables. In the discussion, authors should interpret their findings objectively and acknowledge limitations. Proper citations and references are crucial to avoid plagiarism. Additionally, maintaining clarity and conciseness in writing enhances readability. Before submission, authors should carefully proofread and edit their work, possibly seeking feedback from colleagues or mentors. Finally, they should ensure they comply with ethical standards, including authorship criteria, conflict of interest disclosures, and ethical approval for studies involving human or animal subjects.

“To all academic writers dedicated to advancing scientific progress—your work is invaluable. Every paper you write contributes to the collective knowledge of humanity, driving innovation and shaping the future. The journey of research may be challenging, but your persistence, curiosity, and dedication make a real difference. Keep questioning, keep exploring, and keep sharing your insights with the world. Your efforts inspire others and pave the way for new discoveries. Stay motivated, embrace the process, and never underestimate the impact of your contributions,” says Dharambir.

(by Sasa Zhu, Brad Li)

Hannah Ong

Hannah Ong trains at The Ohio State University College of Medicine and has a specialized interest in artificial intelligence (AI) applications in medicine and interdisciplinary education. Her research focuses on integrating machine learning, leadership development, and hands-on AI training for pre-college students interested in medicine, engineering, and technology. She is the co-founder of the LeadingAI program, a pioneering initiative that trains students in AI and reinforcement learning through AWS DeepRacer, fostering leadership and technical skills. The program has produced multiple globally ranked competitors in AI-driven racing and has been instrumental in developing a structured AI curriculum. She is passionate about bridging AI and medicine, leveraging emerging technologies to enhance education, research, and patient care. She hopes to continue to advocate for the integration of AI education in early academic settings to cultivate the next generation of leaders in technology and healthcare.

According to Hannah, a strong academic paper should include: a clear research objective – define the significance of the study and its impact on the field; comprehensive literature review – contextualize the work by building upon existing research; robust methodology – ensure clarity and reproducibility in research design; data-driven analysis – use well-structured figures, tables, and statistical models to support findings; critical discussion and interpretation – address limitations, future directions, and practical applications; concise and engaging writing – maintain clarity and precision while presenting complex ideas, and ethical research practices – transparency in data handling, authorship, and conflict disclosures is key.

Hannah emphasizes that authors should bear in mind the following elements when they prepare to write. First, target the right audience, tailoring the writing for medical, AI, or interdisciplinary journals accordingly. Seek peer feedback, involving domain experts to enhance rigor and perspective. She adds that authors should ensure reproducibility, accurate citations, and ethical considerations in writing. Well-designed figures and tables can enhance comprehension as well. Furthermore, iterative drafting and peer reviews significantly also improve clarity and impact.

“Academic writing remains one of the most influential ways to advance scientific progress, bridging innovative ideas with practical applications. Whether you are examining the integration of AI in healthcare or exploring leadership in education, your work enriches a growing repository of knowledge that makes a tangible impact.

While the challenges of peer review, funding constraints, and interdisciplinary dialogue can be demanding, they also offer essential opportunities for professional and personal growth. For those working at the intersection of AI and medicine, the potential for breakthroughs—from personalized medicine to improved diagnostic tools—is especially significant. I encourage you to embrace curiosity, actively seek mentorship, and remain dedicated to innovation. Your research is not only advancing your field but also shaping the future of science and technology,” says Hannah.

(by Sasa Zhu, Brad Li)

Pedro Angelo Basei de Paula

Pedro Angelo Basei de Paula is a sixth-year medical student at the Federal University of Paraná (UFPR). He has been contributing to research with AI-powered solutions for the automatic structuring of medical consultations with Voa Health, as well as analyzing datasets with AI in mental health and clinical communication. He is also preparing for a research experience in Canada, where he will contribute to an AI-driven eHealth platform that integrates emotional recognition and behaviour change strategies into medical practice. He is deeply interested in artificial intelligence, psychiatry, healthcare innovation, piano playing, and the profound questions surrounding the Universe and consciousness. With a background in engineering, he is committed to enhancing decision-making, documentation quality, and patient interaction while exploring deeper dimensions of intelligence and existence. Connect with him on LinkedIn and Instagram.

JMAI: What do you regard as a good academic paper?

Pedro Angelo: I consider a good academic paper as one that addresses a relevant research question, presents a clear and structured methodology, and contributes original insights into interdisciplinary fields. It should be well-supported by evidence, with a solid theoretical foundation and transparent data analysis. Clarity in writing is essential, and complex ideas should be communicated effectively without unnecessary jargon. Additionally, a strong paper acknowledges its limitations and discusses the broader implications of its findings. Finally, it should invite further discussion and exploration, adding value to the scientific community rather than merely presenting data.

JMAI: How do you avoid biases in one’s writing?

Pedro Angelo: Avoiding biases in academic writing involves rigorous methodology, critical self-awareness, and full transparency. A key principle is recognizing that science is inherently falsifiable, meaning our conclusions are always provisional and open to revision when new evidence emerges. This humility in knowledge encourages us to admit that we can be wrong and to embrace criticism as an opportunity to grow. Using objective, data-driven language and avoiding unfounded assumptions helps maintain balance, while considering multiple perspectives in the literature review ensures a comprehensive discussion. Employing structured methodologies, such as randomized controlled trials or blinded analyses, minimizes subjective influence. In addition, a robust peer-review process and collaboration with diverse research teams serve to identify and correct implicit biases, ultimately leading to more reliable and evolving scientific insights.

JMAI: Would you like to say a few words to other academic writers?

Pedro Angelo: Scientific progress is built on the collective effort of those who dare to question, explore, and refine knowledge. Writing an academic paper is not just about publishing results, it’s about contributing to a global conversation, where each piece of research connects with past discoveries and inspires future innovation. Challenges and setbacks are part of the journey but should be seen as steps toward deeper understanding rather than obstacles. Every well-structured study, no matter how small, has the potential to shape the future of science. To those dedicating themselves to this path: keep pushing forward, stay curious, and remember that even incremental progress can have a lasting impact.

(by Sasa Zhu, Brad Li)

Wahid Ullah

Wahid Ullah is a Lecturer in Medical Laboratory Sciences at Riphah International University, Islamabad, Pakistan. He holds an M.Phil Degree in Medical Laboratory Sciences from the University of Haripur and has extensive experience in medical diagnostics, molecular biology, and genetics. His research focuses on artificial intelligence, molecular diagnostics, and disease biomarkers. He has contributed to multiple peer-reviewed publications, including systematic reviews and studies on hematological and infectious diseases. His master's research involved the molecular characterization of SARS-CoV-2 strains, reflecting his expertise in virology and genomic analysis. He has previously worked as a Research Assistant at the Institute of Biomedical and Genetic Engineering and as a Medical Laboratory Technologist. With a strong foundation in molecular and genetics, he remains committed to interdisciplinary research. He will soon begin his PhD studies, further advancing his expertise in biomedical sciences. Connect with him on LinkedIn.

Dr. Ullah thinks a good academic paper is well-structured, evidence-based, and contributes meaningful insights to its field. It should have a clear research question, a well-defined methodology, and a logical flow that guides the reader through the study's objectives, findings, and implications. High-quality papers are rooted in rigorous research, incorporating a comprehensive literature review to contextualize researchers’ findings within existing knowledge. They should also present data transparently, ensuring reproducibility and credibility. Clarity in writing is essential, as complex ideas should be communicated concisely without ambiguity. Furthermore, a strong paper acknowledges its limitations, paving the way for future research. Ethical considerations, proper citations, and adherence to journal guidelines are also key elements that ensure scholarly integrity. Ultimately, a good paper not only advances knowledge but also inspires further inquiry and discussion within the academic community.

In Dr. Ullah’s view, researchers should maintain objectivity by using credible, peer-reviewed sources and presenting a balanced perspective to avoid bias in academic writing. He believes neutral language, methodological transparency, and clear inclusion criteria help minimize bias. Moreover, acknowledging limitations, seeking diverse feedback, and being aware of cognitive biases further enhance fairness and accuracy. Striving for intellectual honesty ensures the integrity of scholarly work.

Lastly, Dr. Ullah would like to say a few words to all academic writers, “Your dedication fuels scientific progress. Every study, no matter how small, contributes to knowledge and innovation. Stay committed, embrace challenges, and keep pushing boundaries. Your work shapes the future and inspires others. Keep going!”

(by Sasa Zhu, Brad Li)

Nino Gvajaia

Dr. Nino Gvajaia is a recent graduate of the American MD Program at Tbilisi State Medical University. She is dedicated to medical education, clinical research, and healthcare innovation. She is a Histology Lecturer and Basic Life Support Instructor at Tbilisi State Medical University and Ken Walker International University. She gained hands-on experience through clinical rotations and an observership at Emory University Hospitals in Hematology-Oncology and Neurology, as well as working as a Junior Doctor in the ICU department. She also attended the GAIN Summer School, exploring AI modeling of human cognition and emotion. Her research focuses on AI in medical diagnostics, including a study comparing GPT-4’s accuracy to physicians. Additionally, she has published work on biomarkers for early sepsis detection. Actively engaged in medical conferences, she has helped organize multiple events in Georgia. Beyond medicine, she is passionate about sculpting, painting, and writing.

In Dr. Gvajaia’s view, academic writing is more than just putting words on paper—it helps develop critical thinking, clear communication, and analytical skills. Effective writing is essential for publishing research, securing grants, and presenting work at conferences, as it significantly influences professional opportunities. Strong writing is also key for publishing research, securing grants, and presenting work at conferences, shaping professional opportunities. Beyond that, it teaches research skills, objective thinking, and ethical writing practices, ensuring credibility. Most importantly, academic writing connects individuals with the broader scholarly community, giving them a voice in important conversations and helping drive progress in their fields.

To make writing more critical, Dr. Gvajaia thinks it is essential to move beyond stating facts and actively engage with ideas. This means questioning assumptions, considering different perspectives, and backing arguments with strong evidence rather than opinions. Instead of simply summarizing sources, a critical writer examines their strengths, limitations, and relevance to the topic. A clear structure helps build a well-supported argument, while precise and objective language ensures clarity. Addressing counterarguments adds depth and demonstrates awareness of different viewpoints. Ultimately, critical writing is about thinking deeply, analyzing information carefully, and making well-reasoned arguments.

“One of the most memorable moments in my academic journey was working on our research project, Artificial Intelligence in the Medical Field: Diagnostic Capabilities of GPT-4 in Comparison with Physicians. What started as a standard study quickly grew into something much bigger than we had anticipated,” sharesDr. Gvajaia, “As we analyzed the results, we realized that GPT-4 was performing unexpectedly well, even matching human doctors in some diagnoses. The implications were significant—could AI actually support or even surpass physicians in certain areas? News of our research spread quickly, and before we knew it, several major TV channels in Georgia picked up the story. We were invited for interviews where we discussed not only our study but also the role of AI in medicine and our team’s future plans. The public reaction was a mix of excitement and concern, with discussions ranging from AI’s potential in healthcare to the possibility of it replacing doctors. Seeing our research spark debates beyond academic circles and reach mainstream media was an eye-opening experience. It reinforced the idea that academic writing isn’t just about publishing papers—it can drive conversations, shape perspectives, and influence the future of medicine.”

(by Sasa Zhu, Brad Li)