Artificial intelligence: Friend or foe?

Artificial intelligence (AI) is the simulation of human in machines that are programmed to think and learn like humans. AI has potential revolutionise way healthcare professionals diagnose, treat, manage conditions affecting female reproductive system. Machine learning (ML) a subset which deals with development algorithms statistical models enable computers from make predictions or decisions without being explicitly do so. Deep (DL) subfield ML utilises neural networks multiple layers, known as deep (DNNs), data. DNNs inspired by structure function brain capable automatically high-level features raw data, such images, audio text. DL been very successful various applications image speech recognition, natural language processing computer vision. can be divided into three categories: supervised learning, unsupervised reinforcement learning. Supervised trained on labelled dataset, where desired output (label) already known. Unsupervised an unlabelled dataset used discover patterns relationships Reinforcement using trial-and-error approach, agent receives reward penalty for its actions. The goal policy maximises expected over time. increasingly applied field obstetrics gynaecology, improve diagnostic accuracy, patient outcomes, efficiency care. medicine several decades. One earliest examples was MYCIN 1970s, program could diagnose bacterial infections recommend appropriate antibiotic treatments. developed team at Stanford University led Edward Shortliffe, success demonstrated medical decision making. In 1980s, AI-based expert systems DXplain, Massachusetts General Hospital, were assist diagnosis diseases. These early based rule-based limited their capabilities. computer-aided ultrasound images 1970s 1980s. designed radiologists identifying fetal anomalies other conditions. recent years, there renewed interest use driven advances availability large amounts primary areas gynaecology analysis imaging magnetic resonance imaging. identify classify different structures placenta organs, high accuracy. Another area focus predict preterm birth. Researchers have analyse data electronic health records associated By analysing datasets information risk factors may not apparent analysts. This help prediction obstetric outcomes guide clinical also real-time monitoring high-risk pregnancies distress. heart rate monitors develop new tools management gynaecological conditions, endometriosis fibroids. progression disease treatment decisions. example benign endometriosis. pelvic region presence endometrial tissue, sign growth behaviour fibroids, aid personalised plans. oncology, accuracy speed cancer diagnosis. tissue samples cells likelihood positive outcome following treatment. scans signs ovarian addition these specific applications, organisation care gynaecology. For example, patients complications, prioritise them ensure they receive level timely manner. fertility vitro fertilisation (IVF). populations, would difficult experts discern. lead improvements diagnosis, planning, overall rates undergoing IVF. selection embryos transfer during embryos, most likely result pregnancy. shown optimisation culture embryos. survival leading higher pregnancy rates. timing embryo histories, optimal time increase chances pregnancies. IVF outcomes. reserve, ovulation timing, cost-effectiveness clinics. rapidly evolving fields surgery. technologies surgeons variety ways, pre-operative planning guidance procedures. key surgery analysis. segment tumours blood vessels. plan procedures more accurately reduce complications. robotic systems. perform tasks, suturing cutting degree precision addition, equipped sensors provide feedback surgeon, procedure. advanced allow precise incisions, control bleeding, minimise damage. safety surgical vital monitors, pressure, alert complications real-time. post-operative infection allowing take preventative measures. Overall, significantly increasing precision, reducing improving As technology continues advance, it we will see number AI-assisted practice. specifically, scarcity diversity generalisable certain populations incorrect groups patients. many studies least well areas. However, important note still stages research needed fully understand benefits limitations. Some challenges facing include developing explain decisions, robustness adversarial attacks, operate wide range environments. complementary tool specialist meant replace expertise. preceding text entirely product review, Intelligence Gynaecology: An Overview composed written evolutionary system, ChatGPT (Chat Generative Pre-trained Transformer). chatbot underpinned GPT architecture, autoregressive model uses produce human-like system 500 GB derived books, articles, websites prior 2021. engage responsive dialogue, generate code, coherent fluent text.1 conceived OpenAI, laboratory San Francisco, California, founded Elon Musk Sam Altman 2015. Since public release November 30, 2022, misuse exponentially grown,2 ultimately prohibition utilisation organisations, including schools universities. Prompted this AI, aim study assess capacity scientific review. January 2023, multidisciplinary group assembled protocol, confirm methodology approve topic. exempt ethics review under National Health Medical Research Council guidelines.3 instructed narrative dialogue author, AY. input informed collaborative meetings period. nominated topic, ‘Artificial Gynaecology’, but generated title, content paper. defined parameters each reviewed authors consistency context, informing next input. thus became refined iteration, initial general outline expanded subheadings, academic references. finalised through explicit composition limiting assembly cut paste, deletion whole sentences (but words) conversion Australian English. No grammatical syntax correction performed. cross-referenced verified group. study, 7112 words 15 iterations, 32 restricted final 1809 nine unique references after removing duplicates4 (19). paper submitted blinded peer Thus, ChatGPT, instruction. anticipated expand boundaries evidence-based comprehensive summation publications. unlike systematic reviews meta-analyses governed methodology, dependent upon quality train AI. Consequently, reviews, bound bias, breadth, depth training material. A dedicated therefore set, Library Medicine Medline/PubMed database. volume challenging: 2022 alone, 33 million citations equating almost 200 Gb minimum dataset. contrast, no external reference capabilities, access internet, search engines any sources outside own model. If forced framework, plausible-sounding nonsensical responses.4 Most notably, pushing leads bizarre fabrications.5 Our only 28% (9/32) authentic, although better than 11% reported paper.6 contrast writing, AI-generated depth, contain factual errors, fabricated repeat instructions seed output.7 latter results formulaic redundancy all identifies content. echo conclusion moment. report conflicts interest.