Table 1.

Promising Technologies with Key Contributions and Challenges in Healthcare.

Promising Technologies	Known Methodology	Key Contributions	Key Challenges
Machine Learning14–20	Convolutional Neural Networks Transfer Learning Recurrent Neural Networks Graph Convolutional Networks Self-Supervised Learning	Early Diagnosis and Classification of Diseases Extrapolative Investigation for Patient Consequences Enhancing the effectiveness of treatment and reducing side effects Improve the procedures	Protecting the confidentiality and integrity of healthcare data. Legislative and ethical issues. Challenging to validate and generalize on a diversity of sample datasets.
Blockchain1,21–23	Secure Data Sharing Immutable Patient Records Interoperability Data Monetization and Inducements Supply Chain Management	Preserves the confidentiality and accuracy. Support trustworthy interoperability to exchange data. Provide better control over their health data. Provide transparent supply chain management.	Encounter scalability challenges while handling a substantial amount of healthcare data. Legacy healthcare systems are not compatible. Ensuring commitment to healthcare regulations and data protection laws creates substantial challenges.
IoT24–26	Integration with Electronic Health Records Remote Monitoring Devices IoT-enabled Imaging Devices	Monitor patients remotely and arbitrate rapidly when required. Patients can vigorously contribute to their care. Monitor and enhance compliance with medication.	Vital for preserving sensitive data and restricting unwanted access. Healthcare systems may be overloaded by the massive quantity of data.
Computer Vision14–16	Segmentation and Reconstruction Classification and Analysis Image-Guided Interventions Detection and Diagnosis Virtual Histology	Enables detection of medical disorders. Assist in diagnosis by examining visual data to find abnormalities. Emphasizes minimally invasive treatments. Monitor patients in real-time by visual indicators.	It is imperative to put strong security measures on visual data. May demand substantial computational resources. Big challenge to trusting computer vision technologies.