HOW DOES ARTIFICIAL INTELLIGENCE IMPACT ON SURGERY IN THE WORLD?

Acknowledgements

I would like to thank my GPPW teacher Ainagul Kenzhegalievna, for giving me knowledge and advice in doing research works. Her feedbacks and evaluation of chapters of this research helped me to,finally, finish this paper. Additionally, I would like to mention people who agreed to give interview on this topic. Without mentioned people it would be impossible to ever release this work.

Abstract. Today, artificial intelligence (AI) is widely used in everyday life around the world and is also being implemented in most of the spheres (Chowdhary,2020). Medicine is one of the areas, where AI is being implemented with a high rate. Specifically, even tthough Kazakhstan is placed 72nd among 181 coountries in AI readiness index chart that was built on the base of statistics acquired by Rogerson et al. in 2020, it has surgical clinics with implemented AI. This research aims to investigate the power of AI in surgery sphere and how it affects on it. Results showed that AI helps surgeon to concentrate on their primmary work, like doing surgeries, by completing secondary work like data analyzis and collecting, so overally making their job easier.

Аннотация. В нынешнее время искусственный интеллект (ИИ) широко применяется в повседневной жизни, и постепенно внедряется в другие сферы деятельности (Chowdhary,2020). Медицина является одной из сфер в которой внедрение ИИ происходит с высокой скоростью. Например, даже если Казахстан занимал 72 место из 181 стран по индексу показывающий готовность страны к внедрению ИИ по Роджерсноу в 2020 году, в стране присутствуют хирургические отделения, в которых внедрен ИИ. Данное исследование нацелено исследовать силу ИИ в хирургии и то как оно влияет на данную сферу. Результаты показали, что ИИ помогает хирургам сконцентрироваться на своей основной работе как проведение операций, выполняя второстепенную работу, такую ​​как анализ и сбор данных, что в целом упрощает их работу.

Аңдатпа. Қазіргі таңда жасанды интеллект(ЖИ) күнделікті өмірде өте жиі қолданылып, бөгде салаларға да белсенді енгізіліп жатыр (Chowdhary,2020). Медицина саласында ЖИ енгізілуі, басқа салалармен салыстырғанда қарқындырақ жүреді. Мысалы, Қазақстан ЖИ енгізуіне дайындық деңгейін көрсететін статистикада 181 мемлекет ішінен 72 орында орналасса да, Қазақстанда ЖИ енгізілген медициналық мекемелер бар. Осы жұмыстын мақсаты- ЖИ хирургияға әсерін зерттеу және ЖИ потенциалы мен күшін анықтау.

Keywords: Artificial intelligence, implementation, medicine, surgery

Ключевые слова: Искусственный интеллект, внедрение, медицина, хирургия

Кілтті сөздер: Жасанды интеллект, еңгізу, медицина, хирургия

Table 1.

AI Declaration Form

Introduction

Relevance:

Artificial intelligence (AI) technology is becoming prevalent in many areas of everyday life. The healthcare industry is concerned by it even though its widespread use is still limited. (Etienne et al.,2020). Artificial Intelligence implementation will reduce amount of surgery mistakes that are made every year. According to NHS Resolution, released on March of 2021, between the financial years 2010/11 and 2019/2020 there was totally 10,027 claims made against the NHS for surgery-related mistakes. It is, therefore, important for surgeons to have a foundation of knowledge of AI to understand how it may impact health care and to consider ways in which they may interact with this technology (Hashimoto et al.,2018).

Background information:

Artificial intelligence (AI) is a suite of technologies that uses adaptive predictive power, autonomous learning, and complex algorithms. AI can: recognize patterns; anticipate future events; make good decisions; learn from its mistakes; assist clinical decision-making; and uncover relevant information from data (Etienne et al.,2020).  The healthcare industry makes up only a small part of AI usage. However, AI technologies have advanced rapidly in many fields, their implementation in patient care settings has yet to become widespread. As government and investors are increasing budget funds for development of AI, the future of AI seems bright and full of innovative perspectives (Jianxing He et al.,2019). Surgeons are affected by recent advances in AI technologies and should consider the new possibilities that could impact their daily practice. AI has shown promising results in thoracic surgery, and related fields such as radiology, pathology, or respiratory medicine, which affect patient management from the pre-operative period to surgery and follow-up (Etienne et al.,2020).

Description of the issue & evidence that the issue exists:

There are a lot of surgical mistakes made nowadays because of human factor. For instance, statistics released by NHS (National Health Service) England that was published on May 2023 indicate that between 1 April 2022 and 31 March 2023, there were 169 instances of surgery being conducted at the wrong site on a patient’s body. This included 24 instances of incorrect skin lesions being removed, eight instances of injections being administered into the wrong eye, four instances of botulinum injection being administered to the wrong site. Regarding diagnosis, machine learning systems can now diagnose and identify the vertebral level of compression fractures with a sensitivity of 95.7% compared to a board-certified radiologist with ten years of experience. Furthermore, the grade of height loss and fracture morphology were determined with agreement of 68% and 95%, respectively, compared to radiologist assessment (Burns et al., 2017). In disease management, machine learning can guide the management of patients by providing a patient-specific predicted rate of postoperative complications following lumbar fusion surgery. Kim et al (2018) first trained their artificial neural network model using 70% of a dataset of 22 629 patients, and then assessed their model using the remaining 30% of their dataset. They found that neural networks were more successful than a patient’s American Society of Anesthesiologists classification at predicting events such as venous thromboembolism and wound complications. As such, it can be implied that a physician, such as a junior radiologist, or an institution, such as a low-volume spinal center, would benefit from the knowledge transfer facilitated by artificial intelligence solutions. That said, problem of human factor affecting other ones lives undoubtedly exists.

Motivational factors:

I chose this topic because of the following reasons:

1)This topic is associated with my future profession

2)Through this topic I can challenge knowledge that I already have and improve my understanding of AI.

3) There are a lot of research gaps on this topic.

Purpose of this research paper:

This research paper aims to provide information about using subfields of AI like machine learnaning. In addition,  this research paper shows readers that their vision on medicine and especially surgery is outdated and there are methods nowadays that reduce risks of surgeries. Also, this research paper shows not only advantages of AI usage, but also its flaws.

Hypothesis and explanation:

1)Why AI is the most revolutionary and groundbreaking technology that was implemented in surgery?

This question aims to show what ensures the success of Artificial intelligence.

2)Is Kazakhstan ready for AI implementation in surgery?

This question aims to investigate if our country is fully ready for AI in surgery.

Literature review

The problem of implementing artificial intelligence in surgery is very complex and should be viewed from different sides, because it has advantages and disadvantages as well.

On the one hand AI can be used to expand our capabilities in surgeries. According to Pakkasjärvi et al. (2023), AI can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. In addition to that AI can automate processes that require human like intelligence. AI systems can perform tasks like speech recognition, visual perception, pattern-recognition, decision-making, and language processing. By leveraging machine learning algorithms, AI systems can offer new opportunities for enhancing both the efficiency and effectiveness of surgical procedures, particularly regarding training of minimally invasive surgery. As AI continues to advance, it is likely to play an increasingly significant role in the field of surgical learning. Machine learning which is powered by artificial intelligence also can be used in surgeries, to analyze patterns in large sets of data and statistics. According to Hashimoto et al. (2018) algorithms of machine learning can be used to calculate predictions at accuracy levels thought to be unattainable with conventional statistics. For example, by analyzing patterns of diagnostic and therapeutic data (including surgical resection) in the Surveillance, Epidemiology and End Results (SEER) cancer registry and comparing data to Medicare claims, ensemble of ML (Machine Learning) with neural networks, and lasso regression was able to predict patient lung cancer staging by using International Classification of Diseases (ICD) data alone with 93% sensitivity, 92% specificity, and 93% accuracy, outperforming a decision tree approach (Decision tree analysis involves visually outlining the potential outcomes and consequences of a complex decision. These trees are used for analyzing quantitative information) based on clinical guidelines alone (53% sensitivity, 89% specificity, and 72% accuracy). Other important application of AI in surgery includes Artificial neural networks (ANN). ANN is a subfield of machine learning and is inspired by biological nervous systems. Neural networks process signals in layers of simple computational units (neurons); connections between neurons are then parameterized via weights that change as the network learns different input–output maps corresponding to tasks such as pattern/image recognition and data classification. As Hashimoto et al. (2018) mentions, clinically, ANNs have significantly outperformed more traditional risk prediction approaches. For example, an ANN’s sensitivity (89%) and specificity (96%) outperformed APACHE II sensitivity (80%) and specificity (85%) for prediction of pancreatitis 76 hours after admission. By using clinical variables such as patient history, medications, blood pressure, and length of stay, ANNs, in combination with other ML approaches, have yielded predictions of in-hospital mortality after open abdominal aortic aneurysm repair with sensitivity of 87%, specificity of 96.1%, and accuracy of 95.4%. There is also advanced computer vision technology that works on AI and machine learning principles that can identify pathological zones. For example, the Smart Tissue Autonomous Robot (STAR) developed by Johns Hopkins University was equipped with algorithms that allowed it to match or outperform human surgeons in autonomous ex vivo and in vivo bowel anastomosis in animal models ( Shademan et al., 2016).

On the other hand, AI has its own limitations and drawbacks that should be considered along with its advantages. While AI changes the role of doctors as medical communicators, it is feared to objectify patients and may be liable to miss subtle nuances in patient–doctor communication (Assis-Hassid et al.,2015). Further, AI is far from ready for independent operation, requiring lifelong guidance for proper medical application (Van der Niet et al., 2021). AI may also currently lack the ability to detect conversational cues, which may help in guiding communication to individual levels suited for best delivering the information to each patient in a personalized way (Rampton et al., 2020). Artificial intelligence needs large amounts of data for machine-learning to enable robust decision-making. In some surgical specialties, the number of specific diagnoses is limited, hampering the application of machine-learning in given situations (Pakkasjärvi et al., 2023). Also, the outputs of ML and other AI analyses are limited by the types and accuracy of available data. Systematic biases in clinical data collection can affect the type of patterns AI recognizes or the predictions it may make, and this can especially affect women and racial minorities due to long-standing underrepresentation in clinical trial and patient registry populations (Hashimoto et al., 2018). For now, AI sometimes is not able to identify couple diagnoses simultaneously from data provided. For example, a publicly available National Institutes of Health (NIH) dataset of chest x-rays and reports has been utilized to generate AI capable of generating diagnoses of chest x-rays. However, an in-depth analysis of the dataset by Oakden-Rayner revealed that some of the results may have been from improperly labeled data. Most of the x-rays labeled as pneumothorax also had a chest tube present, raising concern that the network was identifying chest tubes rather than pneumothoraxes as intended (Hashimoto et al.,2018).

To conclude, to properly use AI in surgeries we should make it more individual for every patient, so it can have different approach for different people. Also, technologies like machine learning, computer vision, artificial neural networks, natural language processing should be developed further to analyze provided set of data better and apply better analyzes of minor groups and races.

Methods

Research Design:

The current study used qualitative research design.Qualitative research designs are those, which have appropriate characteristics. Here are the most important of them:

1)Researcher as a key instrument-Qualitative researchers mostly collect data by themselves through reading appropriate literature, observing behaviours or by interviewing participants

2)Multiple sources of data: Qualitative researchers usually gather data from multiple sources like interviews, observations, documents, audiovisual information rather than gathering information from one source

3)Inductive and Deductive data analysis: In qualitative research patterns, categories and themes are built from the bottom up by organizing data into increasingly more abstract units of information

(Creswell, 2014)

In this research face-to-face interview method was used for data collecting process. Interview allows getting open ended answers to the questions from people in group of interest.

Why this method was chosen?

Interviews allow to gather more detailed answers. Especially, face-to-face interviews offer flexibility in asking questions. Interviews allow to get data from selectively chosen people, so answers provided are more precise and accurate. (Maxwell, 2013)

Sampling:

Population that was used for sampling consisted of surgeons. Using of face-to-face interview method limits representativeness to sampling units (surgeons) meeting selection criteria (sampling units should be available offline). In our case list of hospital workers and general information on Kazakhstan’s hospitals’ webpages were used as sampling frame since they arrange access to our sampling units.

In this research Quota sampling was used. In quota sampling population is split into non-overlapping (not having the same criteria) subgroups. Afterwards interviewer searches for sampling units that fit the required quota criteria (Sapsford & Jupp, 2006). Two quota criteria were used for this research:1) surgeons who have more than 3 years of working experience with AI according to their profession; 2) surgeons that recently got introduced to AI and have less than a year of experience of working with it.

Such quota criteria may provide information not only about advantages and disadvantages of AI in surgery but also about getting used to AI and challenges that come consequently.

Participants:

There are two interview participants. One of them is surgeon, who has experience of working with and without AI technologies, so his responses are crucial, because he can compare both methods of working which provide us with diverse response. The second participant is surgeon who has worked most of his life without AI but have started using it last year. The age range of participants is 29-40, which is ideal age group for our interview, because there is balance between enormous experience and knowledge of AI software.

Data collection process:

Data collection process involved an interview with both participants. Interviews were taken in real life and recorded on phone recorder. Interviews’ duration was approximately 15-20 minutes. Each participant answered 6 questions according to the topic. Here are the questions asked:

Questions that were asked from surgeons:

1. Can you describe your overall experience with using AI in surgical procedures?
2. What differences have you noticed in surgeries performed with AI assistance compared to those without?
3. How did you first become involved with using AI in surgery?
4. In your opinion, what are the ethical implications of AI potentially making critical decisions during surgery?
5. What advice would you give to other surgeons or medical facilities considering the adoption of AI technologies?
6. How do you perceive the future of AI in surgery? What advancements do you anticipate or hope to see?

Limitations:

1. Subjectivity: Interviews entail a high subjectivity level and, in most cases, solely depend on the respondent's perceptions and experiences, which do not always provide a clear and accurate idea of broader trends or opinions. Such subjectivity would skew the research results, especially in a field as specialized as AI in surgery. (Maxwell, 2013).

2. Sample Size and Scalability: One of the drawbacks is that interviews are conducted with a smaller sample because data are collected in-depth. This may, however, limit the generalization of the results to the broader population of surgical practitioners using AI. (Maxwell, 2013).

3. Time-Consuming: Interviewing and transcription are some of the time-consuming processes of this method, which might not be very welcome in the fast-paced environments of surgical units where practitioners' time is limited. This is one of the weaknesses of this method. (Berg & Lune, 2012).

Overcoming Limitations:

1.Reducing Subjectivity:

This could be verified by using several methods of data collection, such as observation and surveys working in conjunction with the interviews, using cross-verification from different sources to balance out the subjective bias inherent in individual data collection methods. (Maxwell, 2013).

2. Increasing sample size:

By choosing different methods of sampling and different approaches various type of population can be achieved, so responses’ variety will change as well (Berg & Lune, 2012).

3. Managing Time Constraints:

Advanced Scheduling and Time Management: Schedule the interviews well in advance, taking into consideration the availability of the participants, especially in busy environments like surgical units. Use efficient recording tools with transcription services to record the interviews, therefore considerably speeding up the data processing phase (Berg & Lune, 2012).

Results

Current state of AI used in surgery in Kazakhstan:

Nowadays, Artificial Intelligence is not so popular in Kazakhstan. That is proven by statistics released by “oxfordinsigths.com”. These statistics include 39 indicators across 10 dimensions, which make up 3 pillars: Government, Technology Sector, and Data & Infrastructure. According to AI readiness index, in 2023 Kazakhstan took 73 places among 193 countries.  That can also be proved by interviewee A’s response:

“As I know, AI is implemented only in 10% of our country’s hospitals. Those hospitals as you may guess are in Almaty and Astana. It is really a major problem because working process becomes much faster after AI implementation. I think it is not developed because of financial problems, since high quality artificial intelligence cost accordingly to its name.”  Even though on January 31, 2017, the President of Kazakhstan announced five priority points for the third stage of the country’s modernization, where the first point of the program envisages accelerated technological modernization, which included increase the productivity of labor through widespread introduction of automation, robotics, AI, and exchange of “big data”, its results are still barely seen. Consequently, it may be concluded that AI is developing in our country but not with rate that was planned. And if artificial intelligence develops slowly among ordinary users, its full implementation in labor, especially surgery, is not close.

How beneficial is using AI in surgical tasks?

According to Pakkasjärvi et al. (2023), AI can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. For example, the Smart Tissue Autonomous Robot (STAR) developed by Johns Hopkins University was equipped with algorithms that allowed it to match or outperform human surgeons in autonomous ex vivo and in vivo bowel anastomosis in animal models ( Shademan et al., 2016).  Pakkasjärvi’s data and Shademan’s data correlate with this research’s findings. Both intervieewes A and B mentioned that AI helps to analyze big amount of data and set right diagnoses through identifying right zones in x-ray screens.Also, respondent A stated that, usage of AI became routine and made life much easier. It can be concluded that AI makes surgeon’s work associated with data collection and analyzing much easier and faster. In addition to that, interviewee A said that using AI may be addictive:

“AI is very useful tool in hands of an experienced surgeon, but I think it should not be allowed for rookies to use artificial intelligence for the sake of keeping critical thinking ability. AI should be viewed as a timekeeper and not as your problem solver. Surgeon should be introduced to AI only when he or she is already able to do, what AI is going to do for him or her.”

Ethical considerations of AI usage in surgery

A study conducted by Satapathy et al. (2023) shows that there is a risk of bias in the data used to train AI algorithms. This bias could come from the type of surgeries that are being analyzed, the demographic of the patients, or even the surgeon’s experience. Therefore, it is important to note which data is used to train AI. Different types of surgeries should be used for different AI models and for different tasks. That said, AI that is used for neurosurgery should not be used for analyzing data from cardiac surgeries. In addition to that, interviewee A noted that AI is not able to make crucial decision in terms of surgery, because one’s life cannot be in the hand of machine. To conclude, AI is not fully ideal, and cannot make critical decisions.

Conclusion

1) Why AI is the most revolutionary and groundbreaking technology that was implemented in surgery?

There is a popular opinion that AI is the mist revolutionary thing that was implemented in almost all spheres in last decade. This research attempted to prove this statement. Firstly, according to secondary sources, as Etienne et al. stated, AI has shown promising results in thoracic surgery, and related fields such as radiology, pathology, or respiratory medicine, which affect patient management from the pre-operative period to surgery and follow-up. Specifically, Kim et al (2018) first trained their artificial neural network model using 70% of a dataset of 22 629 patients, and then assessed their model using the remaining 30% of their dataset. They found that neural networks were more successful than a patient’s American Society of Anesthesiologists classification at predicting events such as venous thromboembolism and wound complications. As such, it can be implied that a physician, such as a junior radiologist, or an institution, such as a low-volume spinal center, would benefit from the knowledge transfer facilitated by artificial intelligence solutions. In addition to that, according to primary source, both interviewees stated the fact that artificial intelligence made their routine and work much easier by predicting right diagnoses and sorting large amount of data. By looking at these examples, it may definitely be stated that Artificial intelligence and its types like neural networks and machine learning system are the most beneficial technology that has been implemented in surgery sphere in last decade.

2) Is Kazakhstan ready for AI implementation in surgery?

Nowadays, Artificial Intelligence is not so popular in Kazakhstan. It can be seen by statistics that  “oxfordinsigths.com” released . These statistics include 39 indicators across 10 dimensions, which make up 3 pillars: Government, Technology Sector, and Data & Infrastructure. According to AI readiness index, in 2023 Kazakhstan took 73 place among 193 countries. Also, as primary research shows, interviewee A said that AI is implemented only in 10% of hospitals in our country. Both interviewees mentioned that Artificial intelligent is not developing at peak rate because of  insufficient funding from government. Nevertheless, Kazakhstan’s president in 2017 stated that AI’s development should be one of the country’s main priority in technology field. Eventually, it can be concluded that our country does its best to promote and develop AI, yet it cannot be said that it is fully ready for AI implementation, especially in surgery, for now.

How does Artificial Intelligence impact on surgery in the Kazakhstan?

Artificial intelligence’s impact is not so strong on surgery in Kazakhstan nowadays, because AI is not fully developed and promoted in the country. Indeed, its effect is greatly seen in hospitals where its used, but in terms of whole country it can be said that its full impact will be seen in the future.

Evaluation and further research:

This research fully investigated Artificial intelligence impact on surgery in Kazakhstan and its development rate with causes and consequences. In the future methods of popularizing AI in surgery may be developed and researched even deeper. Also, future research on this topic would be able to really change current situation in the country. This research tried to evaluate current situation of AI development from different perspectives including government, patients, and surgeons themselves. Additionally, it would be beneficial to investigate different causes of slow development of AI in surgery independently from each other to develop better strategies of further AI promotion in the country.

Приложение

Appendix

Key 

I=Interviewee 

R=Interviewer 

1)Cardiac surgeon (CS) 

I: Hello, before we start, I should tell you that you may skip questions that you do not want to answer and can stop the interview at any moment. 

R: Ok, I got it 

I: Do you have any other suggestions or offers before we start? 

R: No, we can start. 

I: Ok then let’s start with the first question. Can you tell a little bit about your job? 

R: I work as a cardiac surgeon almost for seven years. I worked in various hospitals in cities like Almaty, Astana, Pavlodar. Even though I work as surgeon now, I started my career as a nurse. 

I: Can you describe your overall experience with using AI in surgical procedures? 

R: I was introduced to AI when I was working in Astana not so long ago, because it was not implemented in neither Pavlodar nor Almaty. If I am not mistaken it happened in the beginning of 2023. At that time, I had a trouble with identifying pathogenic zone on my patient’s x-ray screen. One of my colleagues that I worked with at that time advised me to use Artificial intelligence, that was tested at that time in Astana, to solve my problem. At first try I did not get any successive results, but after the second run, I eventually identified the zone, and set the right diagnose for that patient. After that I started using AI in my everyday routine. 

I: That is really great story. So, what differences have you noticed in surgeries performed with AI assistance compared to those without? 

R: AI is not directly implemented in our job, like it is not used while doing operations, it is only used for secondary work with data. As I mentioned before AI helps in analyzing screenings and in sorting large amount of information. Also, AI is better in noticing various patterns in comparison with humans. As I know AI is used directly in neurosurgeons, but I did not delve that much into it and can’t guarantee that. What I wanted to say is that in my specialty AI is not used during surgeries. I:How did you first become involved with using AI in surgery? 

R: As I mentioned before, it happened occasionally. 

I: In your opinion, what are the ethical implications of AI potentially making critical decisions during surgery? 

R:I think it is not right to let AI make critical decision during surgery, because one’s life should not be in machines’ hands. That said, it is important that last decision is always made by human. 

I: What advice would you give to other surgeons or medical facilities considering the adoption of AI technologies? 

R: I would definitely recommend implementing AI technologies in all medical faculties, because it makes every surgeon’s life much easier and saves a lot of time. But there may arise a problem that young surgeons would use it inaproppriately. AI is very useful tool in hands of an experienced surgeon, but I think it should not be allowed for rookies to use artificial intelligence for the sake of keeping critical thinking ability. AI should be viewed as a timekeeper and not as your problem solver. Surgeon should be introduced to AI only when he or she is already able to do, what AI is going to do for him or she. 

I: How do you perceive the future of AI in surgery? What advancements do you anticipate or hope to see 

R: I just hope that there will be more hospitals with implemented AI.As I know, AI is implemented only in 10% of our country’s hospitals. Those hospitals as you may guess are in Almaty and Astana. It is really a major problem because working process becomes much faster after AI implementation. I think it is not developed because of financial problems, since high quality artificial intelligence cost accordingly to its name 

I:Ok, thank you for your interview, i hope you liked the questions. 

2)Neurosurgery

I: Hello, before we start, I should tell you that you may skip questions that you do not want to answer and can stop the interview at any moment. 

R: Ok, I got it 

I: Can you describe your overall experience with using AI in surgical procedures? 

R: In general, AI became much more relevant in last years, but it is not used that much in surgery. We hear about AI in surgery mostly from meetings. So, since AI is used rarely in our field, I can’t fully describe my experience on that. 

I: So, what differences have you noticed in surgeries performed with AI assistance compared to those without? 

R: If AI implements more in our field, it would be easier for us and our worklife, because it helps to automate our work. Specifically, there are Ais that help to set the right procedure or investigate one’s spinal cord. Overall, I think it is easier to work with AI than without. 

I: How did you first become involved with using AI in surgery? 

R: At first I heard about artificial intellegence from surgeons overseas. At that time I was shocked by the progress of the modern technology. Also if AI is everyday thing abroad, it is something new in our country. 

I: In your opinion, what are the ethical implications of AI potentially making critical decisions during surgery? 

R:For now i think it is better to give a human the right of making critical decision, but in next 50-80 years AI should make the last decision, because the margin error will be almost zero. 

I: What advice would you give to other surgeons or medical facilities considering the adoption of AI technologies? 

R: Of course. Even for now our country cant fully afford such technologies, I would definitely recommend their implementation. 

I: How do you perceive the future of AI in surgery? What advancements do you anticipate or hope to see 

R: I think its affordability will rise, because there are a lot of young minds that are going to popularise AI in the future