A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges.
Hussain A YounisTaiseer Abdalla Elfadil EisaMaged NasserThaeer Mueen SahibAmeen A NoorOsamah Mohammed AlyasiriSani SalisuIsraa M HayderHameed AbdulKareem YounisPublished in: Diagnostics (Basel, Switzerland) (2024)
Artificial intelligence (AI) has emerged as a transformative force in various sectors, including medicine and healthcare. Large language models like ChatGPT showcase AI's potential by generating human-like text through prompts. ChatGPT's adaptability holds promise for reshaping medical practices, improving patient care, and enhancing interactions among healthcare professionals, patients, and data. In pandemic management, ChatGPT rapidly disseminates vital information. It serves as a virtual assistant in surgical consultations, aids dental practices, simplifies medical education, and aids in disease diagnosis. A total of 82 papers were categorised into eight major areas, which are G1: treatment and medicine, G2: buildings and equipment, G3: parts of the human body and areas of the disease, G4: patients, G5: citizens, G6: cellular imaging, radiology, pulse and medical images, G7: doctors and nurses, and G8: tools, devices and administration. Balancing AI's role with human judgment remains a challenge. A systematic literature review using the PRISMA approach explored AI's transformative potential in healthcare, highlighting ChatGPT's versatile applications, limitations, motivation, and challenges. In conclusion, ChatGPT's diverse medical applications demonstrate its potential for innovation, serving as a valuable resource for students, academics, and researchers in healthcare. Additionally, this study serves as a guide, assisting students, academics, and researchers in the field of medicine and healthcare alike.
Keyphrases
- artificial intelligence
- healthcare
- big data
- deep learning
- machine learning
- endothelial cells
- end stage renal disease
- peritoneal dialysis
- chronic kidney disease
- ejection fraction
- induced pluripotent stem cells
- sars cov
- prognostic factors
- mental health
- autism spectrum disorder
- medical education
- systematic review
- convolutional neural network
- patient reported outcomes
- coronavirus disease
- high school
- combination therapy
- human health
- health insurance
- oral health
- fluorescence imaging