Background: Treatment of vitiligo is still a challenge in dermatology. Literature is sparse on the definitive clinical role of basic fibroblast growth factor (bFGF) in vitiligo patients. Aims: We decided to generate a consensus in an attempt to answer some critical questions related to the management of vitiligo and the role of bFGF. Materials and Methods: A Delphi method among 21 experts across India was conducted. A consensus (agreement was 75% or greater) was taken on 27 statements on the prevalence, epidemiology, and treatment of vitiligo and the role of bFGF in the management of vitiligo. The consensus process was completed after two rounds. Results: Topical corticosteroid therapy is the first-line therapy for vitiligo; however, its adverse effects are widely known, especially in sensitive areas. Topical calcineurin inhibitors are preferred in stable vitiligo of the face, neck, genitals, or intertriginous regions as an alternative to topical corticosteroids. Topical bFGF is a relatively newer therapy with a promising role in stable vitiligo. bFGF is safe and effective in inducing repigmentation of vitiligo lesions. Combination therapy of bFGF with other topical therapies, phototherapy, and surgical procedures can be beneficial in patients of vitiligo. Conclusion: This consensus would complement the currently available literature on bFGF and help the practitioner to recognize the unmet need in the treatment of vitiligo.
OBJECTIVE: Artificial intelligence (AI) seems to be bridging the gap between the acquisition of data and its meaningful interpretation. These approaches, have shown outstanding capabilities, outperforming most classification and regression methods to date and the ability to automatically learn the most suitable data representation for the task at hand and present it for better correlation. This article tries to sensitize the practising radiation oncologists to understand where the potential role of AI lies and what further can be achieved with it. METHODS AND MATERIALS: Contemporary literature was searched and the available literature was sorted and an attempt at writing a comprehensive non-systematic review was made. RESULTS: The article addresses various areas in oncology, especially in the field of radiation oncology, where the work based on AI has been done. Whether it's the screening modalities, or diagnosis or the prognostic assays, AI has come with more accurately defining results and survival of patients. Various steps and protocols in radiation oncology are now using AI-based methods, like in the steps of planning, segmentation and delivery of radiation. Benefit of AI across all the platforms of health sector may lead to a more refined and personalized medicine in near future. CONCLUSION: AI with the use of machine learning and artificial neural networks has come up with faster and more accurate solutions for the problems faced by oncologist. The uses of AI,are likely to get increased exponentially . However, concerns regarding demographic discrepancies in relation to patients, disease and their natural history and reports of manipulation of AI, the ultimate responsibility will rest on the treating physicians.
