ABSTRACT
Background: The pursuit of understanding facial beauty has been the subject of scientific interest since time immemorial. How beauty is associated with other perceived attributes that affect human interaction remains elusive. This article aims to explore how facial attractiveness correlates with health, happiness, femininity, and perceived age. We review the existing literature and report an empirical study using expert raters. Methods: A peer-reviewed database of 2870 aesthetic female faces with a global ethnic distribution was created. Twenty-one raters were asked to score frontal images on the attributes of health, happiness, femininity, perceived age, and attractiveness, on a Likert scale of 0-100. Results: Pearson correlation coefficients ("r") were calculated to correlate attributes, with multiple regression analyses and P values calculated. Strong positive correlation was found between attractiveness and health (râ =â 0.61, P < 0.05), attractiveness and femininity (râ =â 0.7, P < 0.05), and health and femininity (râ =â 0.57, P < 0.05); medium positive correlation between health and happiness (râ =â 0.31, P < 0.05); and small positive correlation between happiness and femininity (râ =â 0.21, P < 0.05). A neutral relationship was observed between perceived age and happiness (0.01, P = 0.75), and medium negative correlation between perceived age and attractiveness (-0.32, P < 0.05), health (-0.36, P < 0.05), and femininity (-0.31, P < 0.05). Conclusions: Our study illustrates a positive correlation between the positive attributes of health, happiness, femininity and attractiveness, with a negative correlation of all characteristics with increasing perceived age. This provides insight into the complexity of human interaction and provides a holistic view of attraction as being a gateway to the reflexive perception of other attributes. The implications encourage an aesthetic focus on facial reconstruction.
ABSTRACT
BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT), a large language model by OpenAI, and Bard, Google's artificial intelligence (AI) chatbot, have been evaluated in various contexts. This study aims to assess these models' proficiency in the part 1 Fellowship of the Royal College of Ophthalmologists (FRCOphth) Multiple Choice Question (MCQ) examination, highlighting their potential in medical education. METHODS: Both models were tested on a sample question bank for the part 1 FRCOphth MCQ exam. Their performances were compared with historical human performance on the exam, focusing on the ability to comprehend, retain and apply information related to ophthalmology. We also tested it on the book 'MCQs for FRCOpth part 1', and assessed its performance across subjects. RESULTS: ChatGPT demonstrated a strong performance, surpassing historical human pass marks and examination performance, while Bard underperformed. The comparison indicates the potential of certain AI models to match, and even exceed, human standards in such tasks. CONCLUSION: The results demonstrate the potential of AI models, such as ChatGPT, in processing and applying medical knowledge at a postgraduate level. However, performance varied among different models, highlighting the importance of appropriate AI selection. The study underlines the potential for AI applications in medical education and the necessity for further investigation into their strengths and limitations.
ABSTRACT
The excellent work that is being performed in medical science advances is to be admired and applauded. In each case the quest is for perfection and to bring the task in hand to its final solution. Along the way there are milestones being passed that may be overlooked, as to their particular merits, because the eyes are focused all the time on the ultimate goal. The conference highlights so many areas of interest and endeavour some of which parallel, duplicate, overlap and/or compliment others.
