Table 1.

Characteristics of included study

Author	Year	Title	Research Objective	Study Results	Future Directions
Kirill Dyagilev [58]	2015	Learning (Predictive) Risk Scores in the Presence of Censoring due to Interventions	Proposed ranking-based framework for disease severity score learning that can track changes in the severity, especially when there is therapy-related censoring.	It was shown using simulated datasets that DSSL outperforms other approaches in terms of generalization to changes in treatment patterns.	Examine the use of active learning in domains were, obtaining a large number of clinical comparison pairs.
Sarah A. Graham [59]	2019	Artificial Intelligence for Mental Health and Mental Illnesses: An Overview	Using artificial intelligence to classify, predict, or identify mental health disorders.	Shown high accuracy and the potential of using machine learning algorithms in mental health care and customize treatments based on certain traits.	Investigate how AI and human intelligence may collaborate to ensure accuracy and decrease potential errors.
Hongfeng Li [60]	2021	Skin disease diagnosis with deep learning: a review	Provide a conceptual analysis and in-depth assessment of current deep learning detection models.	Determined the difficulties and current uses in the diagnosis of skin diseases.	Integration of multimodal data sources, such as clinical information and genetic data, to enhance skin disease diagnosis with DL.
Michael Tran Duong [61]	2019	Artificial intelligence for precision education in radiology	AI framework to enhance radiology education by tailoring instruction to individual.	Guided decision-making can enhance individual trainees’ decision-making skills, increasing the quantity and quality of case training.	Explore the full potential of AI in enhancing radiology education.
Bradley J. Erickson [62]	2017	Machine Learning for Medical Imaging	Explore the use of machine learning in medical imaging to detect trends and aid in medical diagnosis.	Machine learning has shown comparable accuracy to medical practitioners in recognizing multiple conditions from medical photos.	Continued research into DL approaches in medical imaging to optimize the benefits of feature detection.
Moor [23]	2023	Foundation models for generalist medical artificial intelligence	Enable GMAI models to interpret various medical modalities and produce advanced medical outputs.	Generalist medical AI algorithms are suggested to carry out various medical tasks with minimum task-driven data labelled.	Explore the potential of GMAI models in solving tasks with limited data.
Zeeshan Ahmed [36]	2020	Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine	The main aim of this paper is to analyze and explore various AI and ML methodologies, and viewpoints in healthcare to promote scientific solutions.	The use of precision medicine can enhance standard symptom-driven medical care by enabling previous interventions.	More research is required to develop academic solutions, opening the path for a new data-centric era of innovation in healthcare.
Stein JD [63]	2019	Evaluation of an algorithm for identifying ocular conditions in electronic health record data	The aim of this project is to design an algorithm that could examine both structured and unstructured EHR data for exfoliation syndrome (XFS).	The algorithm accurately identified the majority of patients with and without XFS among 122,339 eye care recipients, demonstrating the potential for similar algorithms to identify other ocular disorders and improve research applying big data.	Further research should focus on enhancing the methodology to detect additional ocular disorders besides XFS in EHR data.
Wong [64]	2018	Machine learning classifies cancer	Improve Cancer Diagnosis through Machine Learning.	Machine learning algorithms are effectively used to identify cancer types, assisting doctors in predicting diseases such as breast, lung, and liver cancer based on medical records and improve the treatment of patients.	Explore the potential of utilizing DNA methylation changes for accurate tumor classification.
Chunyu Wu [65]	2023	The future application of artificial intelligence and telemedicine in the retina: a perspective	The primary objective of the study is to examine the existing research and use of AI, telemedicine, and home monitoring devices in retinal conditions, and propose a future model for incorporating AI and digital technology in this field.	AI-based image analysis has been extended to DR, AMD and ROP showing promising results in the diagnosis and monitoring of these conditions.	The study further suggests deploying digital health solutions with technology and technical needs, such as smartphone-based fundus photography utilized in an automated AI DR screening programme in India, that has shown great accuracy in detecting DR.