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- •Contents
- •Introduction
- •AI Applications in Diagnostic Test Analysis
- •Enhancing Diagnostic Accuracy
- •Predictive Analytics
- •Conclusion
- •Introduction to Revolutionizing Medicine with AI: The Power of Medical Image Analysis
- •Setting the Stage: The Role of Medical Imaging in Modern Healthcare
- •The AI Revolution: How AI is Transforming Medical Image Analysis
- •The Scope of this Chapter: A Preview of AI Applications in Medical Image Analysis
- •Fundamentals of Medical Imaging
- •An Overview of Medical Imaging Modalities
- •Importance of Image Quality and Resolution
- •Challenges in Traditional Image Interpretation
- •Understanding AI in Healthcare
- •AI’s Evolution in Medical Image Analysis
- •Early Applications
- •AI Applications in Analyzing Medical Images, EHR Data, and Diagnostic Tests
- •Medical Imaging Analysis
- •Deep Learning in Medical Imaging
- •Improving Radiology Workflow
- •AI Applications in EHR Data Analysis
- •Unlocking the Potential of EHRs
- •Natural Language Processing in EHR Analysis
- •The Emergence of Deep Learning
- •Convolutional Neural Networks (CNNs): Revolutionizing Image Analysis
- •Unprecedented Accuracy
- •Diverse Modalities
- •Key Advantages of AI-Driven Medical Imaging
- •Enhanced Accuracy: Advancing Precision in Diagnosis
- •Improved Efficiency: Accelerating Healthcare Workflows with AI
- •Consistency
- •Workflow Optimization: Streamlining Healthcare Processes with AI
- •Augmented Decision-Making: Enhancing Clinical Expertise with AI
- •Deep Learning in Medical Image Analysis: Harnessing the Power of AI
- •The Essence of Deep Learning
- •CNNs: A Paradigm Shift in Image Processing
- •Transfer Learning: Maximizing Insights with Pretrained Models
- •Understanding Transfer Learning
- •Examples of Transfer Learning
- •Case Studies
- •Detecting Diabetic Retinopathy with Deep Learning
- •Improving Brain Tumor Segmentation with Deep Learning
- •Applications of AI in Medical Imaging: Pioneering Advances in Healthcare
- •Early Disease Detection: A Lifesaving Triumph
- •Cancer Diagnosis and Staging: Precision Beyond Measure
- •Cardiovascular Disease Risk Assessment: A Heartfelt Approach
- •Radiology Workflow Optimization: Empowering Radiologists and Enhancing Patient Care
- •Accelerating Image Interpretation: Speeding Up Diagnostic Insights
- •Reducing Radiologist Workload: Augmenting Expertise, Not Replacing It
- •Deep Learning in Medical Imaging
- •Improving Radiology Workflow
- •EHR Data Analysis
- •Diagnostic Test Analysis
- •Benefits of AI in Accurate Diagnosis
- •Reduced Diagnostic Errors
- •Guidelines for Validating and Implementing AI Tools
- •Rigorous Validation
- •Collaborative Integration
- •Ethical and Responsible Use
- •References
- •What is Telemedicine?
- •Historical Context and Evolution
- •Early Foundations (1860s–1950s)
- •Initial Experiments (1960s–1970s)
- •Technological Advancements (1980s–1990s)
- •Mainstream Adoption (2000s–2010s)
- •Maturation and Expansion (2020s)
- •Regulatory Changes that Facilitated Telemedicine
- •The Ryan Haight Act Amendment
- •Reimbursement Policies
- •The CONNECT for Health Act
- •Public Health Emergency Waivers
- •Health Insurance Portability and Accountability Act Enforcement Discretion
- •Licensure Flexibility
- •Cross-State Practice
- •Bridging Healthcare Gaps
- •The Role of Internet Availability
- •Impact of the Digital Divide on Telemedicine Access
- •Telemedicine’s Reach and Impact on Patient Outcomes
- •Best Practices for Telemedicine Appointments and Follow-Up
- •Training Healthcare Professionals for Telemedicine
- •Standardization across Healthcare Systems
- •Privacy and Security in Telemedicine
- •The Importance of Privacy and Security in Telemedicine
- •Best Practices for Ensuring Privacy and Security
- •Incorporating Feedback Mechanisms
- •Continuous Follow-Ups and Reinforcement
- •Fostering a Community of Users
- •Global Perspective
- •Developed Countries: Expansion and Integration
- •Emerging Economies: Bridging Gaps
- •Low-Income Countries: Overcoming Challenges
- •Unique Models: Innovation and Adaptation
- •Challenges and Considerations
- •Conclusion
- •References
- •Ethical Considerations
- •Equitable Access to Telemedicine
- •Care Continuity in Telemedicine
- •Technological Innovations and Future Directions
- •Impact of Telemedicine on the Healthcare Workforce
- •Creation of New Roles in the Healthcare Workforce
- •Need for Different Skills
- •Reshaping the Healthcare Delivery Model
- •Patient Education and Support
- •Intuitive Onboarding Processes
- •Accessible Educational Materials
- •Personalized Training Sessions
- •Support Hotlines and Help Desks
- •Autonomous vs Semiautonomous Robots
- •Medical Robots by Application
- •Surgical Robots
- •Surgical Robots and AI
- •Challenges and Concerns
- •Internet of Things and Medical Robotics
- •Rehabilitation Robots
- •Telepresence Robots
- •Telesurgery Robots
- •Current Landscape of Medical Robotics
- •Prevalent Robotic Systems in Healthcare
- •Pharmacy Robots
- •Adoption Rates and Geographical Distribution
- •Integration with Other Technologies
- •Impact of RAS on Hospital Efficiency and Patient Safety
- •Reduced Length of Hospital Stay
- •Improved Surgical Precision and Reduced Complications
- •Enhanced Surgeon Performance and Ergonomics
- •Patient Satisfaction and Hospital Reputation
- •Training and Skill Development
- •Cost Implications
- •Challenges and Limitations
- •Conclusion
- •Workforce Implications
- •Job Redefinition and Role Shifts
- •Medical Education and Training
- •Training and Skill Development
- •Workforce Efficiency and Productivity
- •Job Creation and Loss
- •Ethical and Legal Considerations
- •Patient–Provider Interaction
- •Economic Impact
- •Conclusion
- •References
- •Introduction
- •Wearable Monitors
- •mHealth Apps: Definition and Scope
- •Wearable Monitor Versus mHealth App
- •Evolution and Rapid Growth of mHealth in Patient Care
- •The Potential of mHealth in Transforming Healthcare Delivery
- •Categories of mHealth Apps
- •Care Coordination Apps
- •Medication Management Apps
- •Chronic Disease Management Apps
- •Benefits of mHealth Apps for Patient Engagement and Shared Decision-Making
- •Enhanced Patient Engagement
- •How mHealth Apps Support Informed and Collaborative Decision-Making
- •Ensuring Clinical Validity
- •User-Friendly Design
- •What Type of Device Should a Patient Get?
- •Conclusion
- •References
- •Introduction to Genomics and Personalized Medicine
- •Genomics: Scope and Basic Concepts
- •Epigenomics
- •Milestones in Genomics
- •Integration of Genomics into Medical Practice
- •The Transformative Potential of Genomics
- •Overview of Advancements
- •Future Prospects in Healthcare
- •Cancer Genomics
- •Genomic Testing and Therapy Selection
- •Understanding Genomic Tests
- •Types of Genomic Tests
- •Genomic Testing: Mechanisms and Technologies Involved
- •Genomics in Therapy Choices
- •Role in Drug Selection and Dosage
- •Impact on Treatment Efficacy
- •Avoiding Adverse Reactions through Genomics
- •Predictive Role of Genomic Testing
- •Mechanisms of Adverse Drug Reactions
- •Genomic Predictors of Drug Response
- •Tailoring Treatments for Improved Outcomes
- •Personalized Treatment Approaches
- •Genomic Tailoring in Practice
- •Outcome Analysis
- •Standard vs Personalized Treatment
- •Long-term Benefits and Challenges
- •Current Barriers and Disparities to Equitable Access to Validated Pharmacogenomic Testing
- •Socioeconomic Factors
- •Healthcare System Limitations
- •Ethical, Legal, and Social Implications of Genomic Medicine
- •Ethical Challenges
- •Legal Considerations in Genomics in Clinical Medicine
- •Regulatory Frameworks
- •Patient Rights and Protections
- •Social Implications in Genomics in Clinical Medicine
- •Public Perception and Education
- •Impact on Healthcare Equity
- •Conclusion
- •References
- •Introduction
- •Materials Used in OOC Construction
- •Techniques in Microfabrication and Microfluidics
- •Integration of Sensors and Readout Mechanisms
- •Examples of OOC Models
- •Lung-on-a-Chip
- •Heart-on-a-Chip
- •Liver-on-a-Chip
- •Brain-on-a-Chip
- •Gut-on-a-Chip
- •Historical Context and Development
- •Advantages of OOC over Traditional Models
- •Comparison with Animal Models and 2D Cell Cultures
- •Increased Physiological Relevance
- •Improved Toxicity Testing
- •Potential for Personalized Medicine
- •Current Advances in OOC Technology
- •Multiorgan Integration
- •Use of Human iPSCs
- •High-Throughput Drug Screening
- •Sensor Integration and Automation
- •Disease Modeling
- •Cancer Research
- •Neurodegenerative Disorders
- •Infectious Diseases
- •Vascularization and Fluid Dynamics
- •Biomaterial Developments
- •Regulatory and Ethical Focus
- •Challenges and Limitations of OOC Technology
- •Technical and Fabrication Challenges
- •Biological Challenges
- •Regulatory and Adoption Hurdles
- •Conclusion
- •References
- •Introduction
- •Brief Overview of 3D Organ Printing
- •The Significance of this Technology in Modern Medicine
- •Understanding 3D Printing Technology
- •Basic Principles of 3D Printing
- •Evolution from Manufacturing to Bioprinting
- •Fundamental Components: Bioinks, Printers, and Scaffolds
- •Bioinks
- •Bioprinters
- •Scaffolds
- •The Science behind 3D Organ Printing
- •Cell Biology in Organ Printing: Stem Cells and Differentiated Cells
- •Process of Creating Bioinks
- •Applications in Medicine
- •Tissue Repair and Regeneration: Skin, Bone, and Cartilage
- •Organ Transplantation: Kidneys, Liver, and Heart
- •Kidneys and Bioprinting
- •Liver
- •Heart
- •Personalized Medicine: Patient-Specific Implants and Grafts
- •Current State of the Art in Bioprinting
- •Ethical and Regulatory Considerations
- •Conclusion
- •References
- •Conclusion
- •Index

110 New Medical Technology in Patient Care: A Physician’s Guide
Cost Implications
It’s important to note that the initial investment in RAS technology is
significant, and the cost-effectiveness of these systems is still a subject of
ongoing research. While there are efficiency gains, they need to be balanced against the high costs of acquiring and maintaining robotic surgery
systems.
Challenges and Limitations
Despite these advancements, there are challenges in the widespread adoption of medical robots.
specialized training for healthcare professionals, and potential technological failures are significant considerations. Additionally, the integration of
these systems into existing healthcare workflows can be complex.
103
The high cost of these technologies, the need for
Conclusion
In conclusion, medical robots have brought about substantial improvements in hospital efficiency and patient safety. Their applications in surgery, pharmacy, and patient monitoring have demonstrated tangible
benefits. However, addressing the challenges related to cost, training, and
integration is essential for maximizing their potential. As technology continues to advance, medical robots will likely become even more integral
to healthcare delivery.
Workforce Implications
The integration of medical robots into healthcare settings has significant
implications for the workforce. These implications are multifaceted,
affecting various aspects of healthcare delivery, workforce dynamics, and
skill requirements.
Job Redefinition and Role Shifts
The introduction of robotic technology in medical procedures is significantly redefining job roles and responsibilities within the healthcare

Robots in Medicine 111
sector. This transformation is especially pronounced in areas where tasks
traditionally performed manually are becoming automated, leading to
multifaceted and profound implications.
The introduction of robots in medical procedures and care can redefine job roles.
104
In the realm of surgery, the role of the surgeon is undergoing a notable transition. Surgeons are moving away from direct manual
intervention to roles that often involve controlling and guiding robotic
systems. This shift necessitates the development of a new skill set, including proficiency in robotic systems and an in-depth understanding of their
operation and limitations. Additionally, the surgeon’s role is becoming
more analytical, with a focus on interpreting data provided by robotic
systems to make informed decisions during procedures.
Robotic systems are also bringing about advancements in surgical precision and control, enabling surgeons to perform complex procedures with
greater accuracy. This enhancement is paving the way for the development
of new, minimally invasive surgical techniques that were previously unfeasible with traditional methods. Consequently, surgeons must continuously
update their skills to effectively leverage these new techniques.
The roles of nurses and technicians are evolving alongside these technological advancements.
105,106
They are increasingly taking on responsibilities in the setup, operation, and maintenance of robotic systems, which
requires additional training. Moreover, their roles in patient care are shifting, with a greater focus on preoperative and postoperative care as robots
handle more tasks during the operation.
This technological shift in surgery also necessitates a more interdisci-
plinary approach to healthcare.
107
Healthcare professionals are finding
themselves collaborating more closely with engineers, robotic technicians, and data scientists. This collaboration is essential for the smooth
operation and integration of robotic technology in medical procedures. It
requires healthcare professionals to develop not only communication
skills but also an understanding of technical language to effectively work
alongside these new team members.
For instance, tasks that were traditionally manual may become automated, leading to a shift in the roles and responsibilities of healthcare
professionals. Surgeons, nurses, and technicians may need to adapt to
working alongside robots, focusing more on monitoring and supervisory
108
roles.

112 New Medical Technology in Patient Care: A Physician’s Guide
Medical Education and Training
The integration of robotic technology is also influencing medical education and training.
109
Curricula are adapting to include training in robotic
systems, encompassing both technical operation and decision-making
aided by robotic technology. As technology evolves rapidly, continuous
professional development becomes crucial for healthcare professionals to
keep pace with these advancements.
110
With these changes in roles and responsibilities come new ethical and
legal considerations, particularly regarding liability in robotic surgery.
Healthcare professionals must understand these aspects and how they
impact their practice. Despite the increased use of technology, the importance of patient interaction remains paramount. Healthcare professionals
must find a balance between their technical responsibilities and patient
care, ensuring that the human element of medicine is preserved.
Training and Skill Development
There is a growing need for specialized training in robotics technology.
Healthcare professionals will need to acquire new skills to operate, interpret, and interact with robotic systems effectively.
112–114
This requirement
spans from technical know-how to understanding the integration of robotics in patient care.
111
Workforce Efficiency and Productivity
Robots can enhance efficiency and productivity in healthcare settings by
performing tasks more quickly and accurately than humans in certain
scenarios.
115
This can lead to a more efficient allocation of human
resources, where healthcare professionals can focus on patient care
aspects that require human judgment and empathy.
Job Creation and Loss
The adoption of robotics in healthcare can lead to job creation in areas
such as robotic maintenance, programming, and system management.
However, it may also lead to job displacement in roles that become
116

Robots in Medicine 113
automated.
105
Researchers at MIT, who have studied the effect of robots
on employment, state, “Our evidence shows that robots increase productivity. They are very important for continued growth of firms, but at the
same time they destroy jobs, and they reduce labor demand.”
117
The net
impact on employment will depend on how the healthcare sector adapts
and integrates these technologies.
Ethical and Legal Considerations
With the rise of medical robots, there will be an increased need for ethical
guidelines and legal frameworks addressing issues such as patient safety,
data security, and liability in cases of malfunctions or errors.
118
This will
require collaboration between healthcare professionals, legal experts, and
policymakers.
Patient–Provider Interaction
The use of robots might alter the dynamics of patient–provider interactions. Assistive social robots are used to in the care of the elderly,
while helpful are not a substitute for personal care. In her book Alone
Together: Why We Expect More from Technology and Less from Each
120
Other,
Sherry Turkle argues that social interactions with robots provide
superficial emotional comfort and an ersatz intimacy. This substitute for
human interaction not only deepens a patient’s loneliness but also tends to
infantilize them. Healthcare professionals will need to balance the technological aspects of care with the human elements, ensuring that patient
communication and empathy are not compromised.
119
and
Economic Impact
The integration of medical robots into healthcare settings marks a pivotal
shift toward advanced care delivery. The initial investment in these systems, while substantial and impacting budget allocations, is often offset
by long-term benefits such as improved efficiency, reduced error rates,
and enhanced patient outcomes.
Capital expenditure for medical robots, including surgical robots,
rehabilitation robots, or pharmacy automation systems, is significant.

114 New Medical Technology in Patient Care: A Physician’s Guide
This cost encompasses the robot itself, necessary infrastructure modifications, training for healthcare professionals, and maintenance contracts.
Implementing robotic technology also requires comprehensive training
for medical staff, which can be both time consuming and expensive.
Moreover, integrating these systems into existing workflows may demand
significant organizational change management.
Over time, the benefits become more apparent. Robots can operate
continuously, performing tasks more quickly and accurately than humans
in certain contexts. For instance, pharmacy robots can dispense medications rapidly with high accuracy, reducing waiting times and allowing
pharmacists to focus on more critical tasks. In surgical applications,
robots provide high precision, leading to fewer complications and shorter
hospital stays.
This enhanced efficiency and accuracy lead to better patient throughput. Hospitals can treat more patients with the same or even fewer
resources, improving the utilization of hospital beds and facilities.
Additionally, once implemented, robotic systems can be updated and
scaled, potentially extending their utility beyond their initial scope. This
adaptability can lead to sustained improvements in hospital operations and
patient care.
Conclusion
In summary, the workforce implications of medical robots are complex
and encompass changes in job roles, skill requirements, efficiency,
employment patterns, ethical considerations, patient interactions, and economic impacts. These changes necessitate a proactive approach in workforce planning, training, and policy development to fully leverage the
benefits of robotics in healthcare.
References
1. Kurup G. CyberKnife: a new paradigm in radiotherapy. J Med Phys. 2010
Apr;35(2):63–64. doi:10.4103/0971-6203.62194. PMID: 20589114;
PMCID: PMC2884306.

Robots in Medicine 115
2. Adler JR Jr, Chang SD, Murphy MJ, Doty J, Geis P, Hancock SL. The
Cyberknife: a frameless robotic system for radiosurgery. Stereotact Funct
Neurosurg. 1997;69(1-4 Pt 2):124–128. doi:10.1159/000099863. PMID:
9711744.
3. Robotic-Assisted Surgery with da Vinci Systems. Intuitive Surgical.
2024. https://www.intuitive.com/en-us/patients/da-vinci-robotic-surgery.
Accessed 2023 Dec 12.
4. Sheetz KH, Claflin J, Dimick JB. Trends in the adoption of robotic surgery
for common surgical procedures. JAMA Netw Open. 2020;3(1):e1918911.
doi:10.1001/jamanetworkopen.2019.18911.
5. Shyr BU, Shyr BS, Chen SC, Chang IW, Shyr YM, Wang SE. Operative
results and patient satisfaction after robotic pancreaticoduodenectomy.
Asian J Surg. 2020;43(4):519–525. ISSN 1015–9584. doi:10.1016/j.
asjsur.2019.08.012.
6. Medical Advisory Secretariat. Robotic-assisted minimally invasive surgery
for gynecologic and urologic oncology: an evidence-based analysis. Ont
Health Technol Assess Ser. 2010;10(27):1–118.
7. Hussain A, Malik A, Halim MU, Ali AM. The use of robotics in surgery:
a review. Int J Clin Pract. 2014;68:1376–1382.
8. We Believe Minimally Invasive Care Is Life-Enhancing Care. Intuitive.
2021. https://www.intuitive.com/en/about-us/company. Accessed 2023
Dec 30.
9. Strattner. Why Strattner? 2020. http://www.strattner.com.br. Accessed 2023
Dec 30.
10. Intuitive Surgical. Annual Report 2014. Sunnyvale, CA: Intuitive Surgical,
Inc.; 2014.
11. Wong SW, Ang ZH, Yang PF, Crowe P. Robotic colorectal surgery
and ergonomics. J Robotic Surg. 2022;16:241–246. doi:10.1007/
s11701-021-01240-5.
12. Prasad SM, Prasad SM, Maniar HS, Chu C, Schuessler RB, Damiano RJ Jr.
Surgical robotics: impact of motion scaling on task performance. J Am Coll
Surg. 2004 Dec;199(6):863–868. doi:10.1016/j.jamcollsurg.2004.08.027.
PMID: 15555968.
13. Wu S-Y, Chang C-L, Chen C-I, Huang C-C. Comparison of acute
and chronic surgical complications following robot-assisted, laparoscopic,
and traditional open radical prostatectomy among men in Taiwan. JAMA
Netw Open. 2021 Aug 25;4(8):e2120156. doi:10.1001/jamanetworkopen.
2021.20156.

116 New Medical Technology in Patient Care: A Physician’s Guide
14. Maertens V, Stefan S, Rutgers M, Siddiqi N, Khan JS. Oncological outcomes of open, laparoscopic and robotic colectomy in patients with transverse colon cancer. Tech Coloproctol. 2022 Oct;26(10):821–830.
doi:10.1007/s10151-022-02650-9. Epub 2022 Jul 8. PMID: 35804251.
15. McAlpine K, Forster AJ, Breau RH, McIsaac D, Tufts J, Mallick R, et al.
Robotic surgery improves transfusion rate and perioperative outcomes
using a broad implementation process and multiple surgeon learning
curves. Can Urol Assoc J. 2019 Jun;13(6):184–189. doi:10.5489/cuaj.5527.
PMID: 30407153; PMCID: PMC6570603.
16. Plerhoples TA, Hernandez-Boussard T, Wren SM. The aching surgeon: a
survey of physical discomfort and symptoms following open, laparoscopic,
and robotic surgery. J Robot Surg. 2012;6(1):65–72.
17. Lee G, Lee M, Clanton T, Marohn M. Comprehensive Assessment of
Skill-Related Physical and Cognitive Ergonomics Associated with
Robotic and Traditional Laparoscopic Surgeries. Los Angeles: Sages.
https://www.sages.org/meetings/annual-meeting/abstracts-archive/comprehensive-assessment-of-skill-related-physical-and-cognitive-ergonomicsassociated-with-robotic-and-traditional-laparoscopic-surgeries/. Accessed
2024 Dec 18.
18. Long E, Kew F. Patient satisfaction with robotic surgery. J Robot Surg.
2018 Sep;12(3):493–499. doi:10.1007/s11701-017-0772-3. Epub 2017 Dec
29. PMID: 29288373.
19. Ljungqvist O, Scott M, Fearon KC. Enhanced recovery after surgery:
a review. JAMA Surg. 2017;152:292–298.
20. Darr C, Cheufou D, Weinreich G, Hachenberg T, Aigner C, Kampe S.
Robotic thoracic surgery results in shorter hospital stay and lower postoperative pain compared to open thoracotomy: a matched pairs analysis. Surg
Endosc. 2017 Oct;31(10):4126–4130. doi:10.1007/s00464-017-5464-6.
Epub 2017 Mar 8. PMID: 28275918.
21. Sun Y, Xu H, Li Z, Han J, Song W, Wang J, et al. Robotic versus laparoscopic low anterior resection for rectal cancer: a meta-analysis. World
J Surg Oncol. 2016;14:61.
22. Sucandy I, Rayman S, Lai EC, Tang CN, Chong Y, Efanov M, et al.
International robotic, laparoscopic liver resection study group investigators.
Robotic versus laparoscopic left and extended left hepatectomy: an international multicenter study propensity score-matched analysis. Ann Surg
Oncol. 2022;29(13):8398–8406.
23. Kalata S, Thumma JR, Norton EC, Dimick JB, Sheetz KH. Comparative
safety of robotic-assisted vs laparoscopic cholecystectomy. JAMA Surg.
2023;158(12):1303–1310. doi:10.1001/jamasurg.2023.4389.

Robots in Medicine 117
24. Jaffray B. Minimally invasive surgery. Arch Dis Child. 2005;90(5):
537–542.
25. Catto JWF, Khetrapal P, Ricciardi F, Ambler G, Williams NR, Al-Hammouri T,
et al. Effect of robot-assisted radical cystectomy with intracorporeal urinary
diversion vs open radical cystectomy on 90-day morbidity and mortality
among patients with bladder cancer: a randomized clinical trial. JAMA.
2022;327(21):2092–2103. doi:10.1001/jama.2022.7393.
26. Ng A, Sanaiha Y, Bakhtiyar S, Ebrahimian S, Branche C, Benharash P.
National analysis of cost disparities in robotic-assisted virus laparoscopic
abdominal operations. Surgery. 2023;173:1340–1345.
27. Intuitive Surgical. Annual Report. Sunnyvale, CA: Intuitive Surgical, Inc.;
2020.
28. What Makes da Vinci Robotic Surgical Systems Unique? Intuitive Surgical.
2024. https://www.intuitive.com/en-us/products-and-services/da-vinci#:~:
text=What%20makes%20da%20Vinci%20robotic%20surgical%20systems%20unique%3F&text=For%20nearly%20three%20decades%2C%20
Da,%2Dreviewed%20in%2034%2C000%2B%20articles. Accessed 2023
Dec 12.
29. Tam V, Rogers DE, Al-Abbas A, Borrebach J, Dunn SA, Zureikat AH, et al.
Robotic inguinal hernia repair: a large health system’s experience with the
first 300 cases and review of the literature. J Surg Res. 2019;235:98–104.
doi:10.1016/j.jss.2018.09.070.
30. AlphaGo. Google DeepMind. https://deepmind.google/technologies/
alphago/. Accessed 2023 Dec 17.
31. Alaska Airlines. 2021. https://news.alaskaair.com/newsroom/alaska-airlines-and-airspace-intelligence-announce-first-of-its-kind-partnership-tooptimize-air-traffic-flow-with-artificial-intelligence-and-machine-learning/. Accessed 2023 Dec 17.
32. Setting the Standard in Infrastructure Solutions. Utilities One. 2024. https://
utilitiesone.com/satellite-based-remote-surgery-advancing-medical-carefrom-space. Accessed 2023 Dec 17.
33. Pakkasjärvi N, Luthra T, Anand S. Artificial intelligence in surgical learning. Surgeries. 2023;4(1):86–97. doi:10.3390/surgeries4010010.
34. Exploring New Advancements in Robotics. Intuitive. 2024. https://www.
intuitive.com/en-us/about-us/newsroom/exploring-new-advancements-inrobotics. Accessed 2023 Dec 17.
35. Shademan A, Decker RS, Opfermann JD, Leonard S, Krieger A, Kim PC.
Supervised autonomous robotic soft tissue surgery. Sci Transl Med.
2016 May 4;8(337):337ra64. doi:10.1126/scitranslmed.aad9398. PMID:
27147588.

118 New Medical Technology in Patient Care: A Physician’s Guide
36. Amodei D, Olah C, Steinhardt J, Christiano P, Schulman J, Mané D.
Concrete problems in AI safety. arXiv preprint arXiv:1606.06565; 2016.
37. Alemzadeh H, Raman J, Leveson N, Kalbarczyk Z, Iyer RK. Adverse
events in robotic surgery: a retrospective study of 14 years of FDA data.
PLoS ONE. 2016;11(4):e0151470. doi:10.1371/journal.pone.0151470.
38. Ferrarese A, Pozzi G, Borghi F, Marano A, Delbon P, Amato B, et al.
Malfunctions of robotic system in surgery: role and responsibility of surgeon in legal point of view. Open Med. 2016;11(1):286–291.
39. Bokhari MB, Patel CB, Ramos-Valadez DI, Ragupathi M, Haas EM.
Learning curve for robotic-assisted laparoscopic colorectal surgery. Surg
Endosc. 2011;25(3):855–860.
40. Gkegkes ID, Mamais IA, Iavazzo C. Robotics in general surgery: a systematic cost assessment. J Minim Access Surg. 2017 Oct–Dec;13(4):243–255.
doi:10.4103/0972-9941.195565. PMID: 28000648; PMCID: PMC5607789.
41. Feldstein J, Schwander B, Roberts M, Coussons H. Cost of ownership
assessment for a da Vinci robot based on US real-world data. Int J Med
Robot. 2019 Oct;15(5):e2023. doi:10.1002/rcs.2023. Epub 2019 Jul 15.
PMID: 31215714.
42. Vijayakumar A, Abdel-Rasoul M, Hekmat R, Merritt RE, D’Souza DM,
Jackson GP, et al. National learning curves among robotic thoracic surgeons
in the United States: quantifying the impact of procedural experience on
efficiency and productivity gains. J Thorac Cardiovasc Surg. 2024;167:869–
879. doi:10.1016/j.jtcvs.2023.07.051. Epub 2023 Aug 9. PMID: 37562675.
43. Wright JD, Ananth CV, Lewin SN, Burke WM, Lu YS, Neugut AI, et al.
Robotically assisted vs laparoscopic hysterectomy among women with
benign gynecologic disease. JAMA. 2013;309(7):689–698. doi:10.1001/
jama.2013.186.
44. Jeong IG, Khandwala YS, Kim JH, Han DH, Li S, Wang Y, et al. Association
of robotic-assisted vs laparoscopic radical nephrectomy with perioperative
outcomes and health care costs, 2003 to 2015. JAMA. 2017;318(16):1561–
1568. doi:10.1001/jama.2017.14586.
45. Barbash GI, Glied SA. New technology and health care costs — the case of
robot-assisted surgery. New Engl J Med. 2010;363(8):701–704.
46. Wright JD, Ananth CV, Tergas AI, Herzog TJ, Burke WM, Lewin SN, et al.
An economic analysis of robotically assisted hysterectomy. Obstet Gynecol.
2014;123(5):1038–1048.
47. Melamed A, Margul DJ, Chen L, Keating NL, Del Carmen MG, Yan J, et al.
Survival after minimally invasive radical hysterectomy for early-stage

Robots in Medicine 119
cervical cancer. N Engl J Med. 2018;379(20):1905–1914. doi:10.1056/
NEJMoa1804923.
48. Ramirez PT, Frumovitz M, Pareja R, Lopez A, Vieira M, Ribeiro R,
et al. Minimally invasive versus abdominal radical hysterectomy for
cervical cancer. N Engl J Med. 2018;379(20):1895–1904. doi:10.1056/
NEJMoa1806395.
49. Daniel GW, Rubens EK, McClellan M. Coverage with evidence development for Medicare beneficiaries: challenges and next steps. JAMA Intern
Med. 2013;173(14):1281–1282. doi:10.1001/jamainternmed.2013.6793.
50. Kammüller F. Combining secure system design with risk assessment for
IoT healthcare systems. IEEE International Conference on Pervasive
Computing and Communications Workshops (PerCom Workshops).
2019;961–966. https://doi.org/10.1109/PERCOMW.2019.8730776.
51. Balakrishna S, Thirumaran M, Solanki VK. IoT sensor data integration in
healthcare using semantics and machine learning approaches. A Handbook
of Internet of Things in Biomedical and Cyber-Physical System. Cham:
Springer; 2020;275–300.
52. Zabczyk C, Blakey JD. The effect of connected “smart” inhalers on medication adherence. Front Med Technol. 2021 Aug 18;3:657321. doi:10.3389/
fmedt.2021.657321. PMID: 35047916; PMCID: PMC8757760.
53. Okamura AM, Simone C, O’leary MD. Force modeling for needle insertion
into soft tissue. IEEE Trans Biomed Eng. 2004;51(10):1707–1716. doi:
10.1109/tbme.2004.831542.
54. Pradhan B, Bharti D, Chakravarty S, Ray SS, Voinova VV, Bonartsev AP,
et al. Internet of things and robotics in transforming current-day healthcare
services. J Healthc Eng. 2021 May 26;2021:9999504. doi:10.1155/2021/
9999504. PMID: 34104368; PMCID: PMC8158416.
55. Gatouillat A, Badr Y, Massot B, Sejdic E. Internet of medical things:
a review of recent contributions dealing with cyber-physical systems in
medicine. IEEE Internet Things J. 2018;5(5):3810–3822. doi:10.1109/
jiot.2018.2849014.
56. Nasiri S, Sadoughi F, Tadayon MH, Dehnad A. Security requirements of
internet of things-based healthcare system: a survey study. Acta Inform
Med. 2019 Dec;27(4):253–258. doi:10.5455/aim.2019.27.253-258. PMID:
32055092; PMCID: PMC7004290.
57. Calabrò RS, Cacciola A, Bertè F, Manuli A, Leo A, Bramanti A, et al.
Robotic gait rehabilitation and substitution devices in neurological disorders: where are we now? Neurol Sci. 2016;37(4):503–514.
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