Warm greetings from the new editorial board of Kerala Journal of Ophthalmology! As the new Editor-in-Chief of Kerala Journal of Ophthalmology, it is with great pleasure and pride that I present the inaugural edition under our leadership. Over the years, Kerala Journal of Ophthalmology has distinguished itself through exemplary scientific content, robust author engagement, and a wide readership. These achievements are the result of the dedication and hard work of previous editors, supported by committed editorial board members, reviewers, and authors. Standing on the shoulders of these giants, I am inspired to build upon their legacy and advance Kerala Journal of Ophthalmology as a beacon of scientific rigor, academic curiosity, and a vital bridge between research and clinical practice in Ophthalmology. This edition of Kerala Journal of Ophthalmology brings together perspectives from various subspecialties, including Cataract, Pediatric Ophthalmology, Retina, Glaucoma, Cornea, Uvea, Oculoplasty, and Community Ophthalmology. A highlight of this issue is the review article on contemporary concepts in esotropia authored by the renowned strabismologist, Dr. Kenneth W. Wright. This edition also features an insightful interview with Dr. Steven C. Foster, a pioneering ophthalmologist who has redefined the field of Ocular Immunology and Uveitis. The guest editorials on Artificial Intelligence (AI) and Robotics in Ophthalmology, as well as the ocular-gut axis, introduce the latest perspectives for scholarly discussion. Additionally, we present a selection of review articles, original research articles, case reports, and thought-provoking clinical scenarios for practitioners. At a time when clinical care and research are undergoing a paradigm shift with the advent of AI, I would like to emphasize the importance of “Reimagining Humanness in Ophthalmology” through this editorial. Ophthalmology, with its strong tradition of pattern recognition, stands out as a particularly promising specialty for AI-driven algorithmic experimentation and innovation. Today, AI has evolved from proof-of-concept to clinical deployment within Ophthalmology. Deep learning algorithms have transformed the performance, scale, and reach of ophthalmic care. AI has revolutionized diabetic retinopathy management through portable diagnostic devices and AI-integrated software;1,2 enabled lesion quantification in age-related macular degeneration using fundus photography;3 facilitated glaucoma management via efficient progression-tracking algorithms;4 and improved biometric predictions in refractive and cataract5 surgeries, among other advances. Autonomous diagnostic platforms have entered primary care settings, and healthcare systems are increasingly viewing AI technology as a dependable and scalable tool for addressing workforce shortages, resource allocation, prognostic modeling, and therapeutic decision-making.6 Medical research and publishing have also been transformed by the widespread adoption of Large Language Models. The frontiers of robotics-guided precision surgery in Ophthalmology are rapidly expanding, mirroring developments in other surgical specialties. While technological innovations disrupt established standards and introduce new paradigms, they also present a range of challenges. Since the invention of the first ophthalmoscope by Hermann von Helmholtz in 1851, ophthalmologists have relied on increasingly sophisticated instruments to explore the intricacies of the eye. Today, high-power condensing lenses, slit lamps, ophthalmoscopes, intraocular pressure measurement devices, and newer diagnostic technologies such as optical coherence tomography and automated perimetry are indispensable in modern ophthalmic practice. The comprehensive array of surgical equipment—from basic operating microscopes to phacoemulsification machines and LASER delivery devices—underscores the specialty’s reliance on technology. This technological sophistication distinguishes Ophthalmology as a uniquely “equipment-based” and arguably more “technical” medical specialty. Nevertheless, despite this dependence on advanced machinery, we have succeeded in maintaining the human touch, consistently fostering direct, and empathetic relationships with our patients. Limits of Algorithmic Competence With the advent of large language models (LLMs) capable of collecting, collating, and even generating research papers at the click of a button, perceptions of clinical care and research are undergoing a profound transformation. The painstaking search for literature, analysis, and manuscript preparation is increasingly facilitated by AI-driven tools, allowing the scientific community to redefine productivity and efficiency. Nevertheless, significant questions persist regarding the legitimacy of AI-generated content, as these models are prone to occasional inaccuracies or “hallucinations,” potentially misleading research endeavors and diminishing the intellectual satisfaction derived from critical thinking. Algorithmic accuracy cannot substitute for clinical wisdom. While algorithms excel at recognizing patterns and statistical correlations, clinical acumen encompasses understanding patient suffering within the broader context of social, economic, and cultural backgrounds. Tasks such as conveying information about progressive visual loss due to advanced glaucomatous optic neuropathy, counseling young adults, and their families on the implications of inherited retinitis pigmentosa, or reassuring patients with incidentally detected pituitary adenomas require empathy, compassion, and relational skills that are uniquely human—qualities that cannot be replicated by even the most advanced machine learning systems. Reinventing Clinical Wisdom in the Artificial Intelligence Era The integration of AI into clinical care has the potential to create conditions that enable more human-centered care. By automating repetitive image grading, streamlining investigative results, assisting with risk stratification, systematizing documentation, and regulating custom-designed workflows, AI can free clinicians to devote more time to shared decision-making, patient communication, and interdisciplinary collaboration—thereby fostering the empathetic physician–patient relationship. Achieving this vision requires a deliberate redesign of clinical workflows, as well as training programs that equip ophthalmologists not only with digital literacy in AI interpretation, but also with enhanced competencies in communication, ethics, and systems thinking. Equity and the Ethical Imperative Machine learning is inherently dependent upon the data used to generate its algorithms. Unequal representation of racial, ethnic, geographic, or socioeconomic groups can compromise the generalizability of these algorithms in specific clinical settings.6–8 Clinicians must remain vigilant against the infiltration of such “unconscious bias” in patient care. It is important to recognize that such flaws in the data provided to AI systems can exacerbate global healthcare disparities, rather than promote integration. Transparency in data reporting, inclusivity in sample selection, and ongoing evaluation of post-deployment performance should be maintained as standard practice. In this context, the frontiers of humanness extend well beyond bedside manners, encompassing distributive justice, equitable principles, and the sharing of the benefits of scientific innovations with all, regardless of racial, geographic, social, or economic divides. AI Stewardship for the Future of Eye Care High-performance medicine envisions a synergistic convergence of human and artificial intelligence rather than substitution.9 In this context, AI cannot replace human wisdom, but it can optimize systems to become more efficient and proactive, allowing the time and energy saved through this collaboration to be devoted to deepening empathetic human engagement. AI will undoubtedly remain an integral component of ophthalmic practice; the central challenge for practitioners is the diligent exercise of AI stewardship to enhance care delivery. It is essential to ensure that AI systems are transparent, equitable, and rigorously validated, that clinical wisdom prevails over algorithmic output, and that the patient–physician bond is strengthened rather than diminished. Ophthalmology, as a specialty historically defined by technological sophistication, is well positioned to lead responsibly by anchoring AI deployment in compassion, justice, and patient partnership. By embracing cutting-edge technology while incorporating robust value-based safeguards, we can reimagine a model of care that remains profoundly humane. In this bold vision for the future of eye care, artificial intelligence cannot replace the human touch, but it has the potential to create space for it. As times change and disruptive technologies usher in a new world order in Eye care, Science, and academics, it is critical not to lose sight of the human element. As a community dedicated to treating eye diseases, we cannot afford to relinquish this core value. Even at the pinnacle of AI-integrated technology, visual impairment is not experienced as pixels, gigabytes, or probabilities, but as fear, uncertainty, and hopelessness. Therefore, it is imperative to reimagine how we perceive and preserve humanness in eye care. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
Sanitha Sathyan (Thu,) studied this question.