The Transformative Impact of Artificial Intelligence and Robotics in Healthcare: Applications, Challenges, and Ethical Implications
This academic literature review examines the current applications, challenges, and ethical implications of artificial intelligence and robotics in healthcare. It analyzes advancements in medical imaging, surgical robotics, precision medicine, and social assistive robots while highlighting persistent challenges such as data privacy, algorithm transparency, and the need for professional training. The paper concludes that while these technologies have transformative potential to enhance healthcare delivery, ongoing collaboration among stakeholders is crucial.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, healthcare professionals, and policymakers seeking an overview of AI and robotics applications, challenges, and ethical considerations in medicine.
What the document contains
The document covers current applications of artificial intelligence and robotics in healthcare, including medical imaging, robotic surgery, rehabilitation, precision medicine, and elderly care. It details the methodologies used, benefits, operational challenges, and ethical implications associated with these technologies.
The document does not provide binding regulatory frameworks, legal enforcement mechanisms, or formal compliance directives for AI deployment in medical institutions.
- Artificial intelligence and robotics significantly enhance diagnostic precision, operational efficiency, and patient outcomes in healthcare.
- Robotic-assisted surgical procedures demonstrate advantages such as reduced blood loss and shorter recovery times despite high equipment costs.
- Socially assistive robots provide valuable support for elderly care, rehabilitation, and patients with functional impairments.
- Persistent challenges include data privacy, algorithmic black-box opacity, dataset imbalances, and the necessity of specialized clinician training.
· Abstract · Introduction · 1. Robotics in Medicine · 2. Social Robots and Patient Welfare · 3. Robotics in Surgical Procedures · 4. Artificial Intelligence in Medical Imaging Analysis · 5. Precision Medicine and Artificial Intelligence · 6. Discussion · 7. Challenges and unresolved concerns in medical image evaluation · 8. Conclusions
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
2/5Clear responsibility for AI outcomes
Consequently, AI should be seen as an adjunct to, rather than a substitute for, human decision-making
5. Precision Medicine and Artificial Intelligence
screening score · 2026-10-23
Fairness
2/5Non-discrimination and equitable treatment
A significant difficulty is the substantial imbalance of datasets.
7. Challenges and unresolved concerns in medical image evaluation
screening score · 2026-10-23
Human Dignity
1/5Respect for the inherent worth of persons
Robots have the potential to transform end-of-life care and assist humans in maintaining their independence for an extended period.
6. Discussion
screening score · 2026-10-23
Human Oversight
2/5Humans remain in control of consequential decisions
The integration of the deep learning model's predictions with human expert diagnoses enhances model accuracy and substantially decreases the human mistake rate
7. Challenges and unresolved concerns in medical image evaluation
screening score · 2026-10-23
Privacy
2/5Protection of personal data and private life
Ethical considerations, including patient confidentiality, and legal issues about data use, endure.
5. Precision Medicine and Artificial Intelligence
screening score · 2026-10-23
Transparency
2/5Openness about how AI systems work and are used
A prevalent disadvantage of all deep learning techniques is their deficiency in transparency and interpretability.
8. Conclusions
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
N/A
screening score · 2026-10-23
Impact Assessment
2/5Required assessment of an AI system's risks/impacts before or during use
Algorithms must undergo clinical validation and justification.
6. Discussion
screening score · 2026-10-23