The Application of AI Technology in the Medical Field, and Its

Future Direction

Haoyu Yan

College of Engineering, University of California, Davis, U.S.A.

Keywords: Artificial Intelligence, Surgical Robots, Application, Medical Field.

Abstract: Artificial Intelligence (AI) has become a major development direction in the medical field over the past decade.

It has introduced smarter, more accurate, and more efficient technologies into medicine, and has made

unprecedented progress in risk control and patient satisfaction. This paper studies the latest innovations in AI-

driven surgical technology, including robotic surgical systems, AI-based assistance systems, AI-powered

medical imaging, and AI-integrated conduction devices (for rehabilitation monitoring), which are respectively

reflected in the clinical application of preoperative planning, intraoperative effects, and postoperative care,

and are supported by medical data from review studies in the medical industry. This paper also provides cases

of clinical applications of AI technologies and explores their impact on modern medical systems. At last, it

also examines the ethical and safety considerations, as well as innovation challenges and future directions of

AI technology in medical care. However, for new technologies in the developing stage, there are always pros

and cons. AI-integrated medical technology is also facing challenges in multiple dimensions, such as data

security, ethics, personal privacy, safety, and technical security.

1 INTRODUCTION

The application of AI technology in the medical field

began with the Da Vinci system in the early 21st

century. The da Vinci Surgical System, developed by

Intuitive Surgical, is a groundbreaking robotic-

assisted surgical platform that has revolutionized

minimally invasive surgery (MIS) since it was

approved by the FDA in 2000 (DiMaio, 2011).

Although the system was not powered by AI in its

first generation, AI has made significant progress in

medicine in the 21st century. AI-powered surgical

robots approved by the United States government

have accomplished several surgical procedures and

have helped to perform countless extremely difficult

surgeries on patients in many countries around the

world. This major achievement reflects the

integration of advanced robotics and human

expertise. AI-based medical imaging and AI-powered

health monitoring devices have also been integrated

into modern medical care. These AI-driven medical

devices have helped many patients achieve more

effective and precise treatments. Today, AI-driven

medical devices provide support from preoperative

https://orcid.org/0009-0000-5999-3383

preparation, and intraoperative surgical support, to

postoperative recovery and other stages. AI

technology is changing the medical level of

traditional medicine by analyzing patient data,

learning, and simulation, helping doctors achieve

"impossible" medical tasks.

2 THE IMPACT OF AI

TECHNOLOGY ON

PREOPERATIVE

PREPARATION

In the 21st century, AI technology has been

extensively used in the medical field. It provides

technical support for doctors to analyze patients'

conditions and determine treatment plans before

surgery (Martin, 2017). For example, the application

of AI in medical imaging is one of the most

significant directions of AI technology in medicine.

These AI technologies can help doctors process high-

intensity and repetitive image reading and improve

the quality and work efficiency of the imaging

Yan, H.

The Application of AI Technology in the Medical Field, and Its Future Direction.

DOI: 10.5220/0013698400004670

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Data Science and Engineering (ICDSE 2025), pages 409-413

ISBN: 978-989-758-765-8

409

department. At present, the AI for analyzing many

organs, such as the heart, bones, head, neck, and

lungs, has been very mature and can achieve high-

precision auxiliary diagnosis of multiple diseases. In

addition, with the continuous advancement of

technology, medical imaging AI of other organs of

the body is also under development, and some

products have gone through FDA and obtained

medical device registration certificates in the United

States.

Tools such as Surgery-Risk Calculator use AI

machine learning in medicine to assess patients'

physical risk profiles and optimize clinical judgments

to help surgeons make wise decisions (University of

Florida, 2022). This can help doctors assess and

analyze patients' physical conditions and risks before

surgery to greatly reduce the unexpected

complications that patients may encounter after

surgery.

AI algorithms enhance preoperative diagnosis by

analyzing radiological images (CT, MRI) with

remarkable precision. For example, a study published

in Nature Medicine in 2023 showed that its self-

developed deep learning method PANDA achieved a

sensitivity of 92.9% for detecting pancreatic tumors

in CT scans, which was 6.3% higher than the average

performance of radiologists (Cao, 2023). Such tools

enable surgeons to detect and diagnose lesions with

extremely high accuracy, helping doctors to draw

conclusions and ultimately find the right medical plan

for their patients.

IBM Watson Health applies AI technology in a

wide range of medical fields, including but not

limited to oncology, cardiology, neuroscience, and

diabetes management (Mishra, 2024). In in field of

oncology, it can provide doctors with personalized

treatment plans by analyzing patients' genomic data

and clinical history (Mishra, 2024). In the fields of

cardiology and neuroscience, by analyzing patients'

electrocardiograms, brain scans and other data, the

platform can detect potential conditions early and

make intervention recommendations (Mishra, 2024).

In addition, in terms of diabetes management, through

real-time monitoring and analysis of blood sugar data,

the platform can provide patients with reasonable diet

and lifestyle recommendations to better control their

condition. IBM Watson Health's AI-driven platform

can predict surgical risks by analyzing electronic

health records (EHRs) (Mishra, 2024).

AI systems integrated with preoperative imaging

(e.g., fluorescence-guided surgery) can provide real-

time feedback. Developed by Associate Professor

Eiman Azim and his team at the Salk Institute in the

United States, GlowTrack technology is a non-

invasive motion-tracking technique that uses

fluorescent dye markers to train AI (Butler, 2023).

This technology was published in Nature

Communications in September 2023, and it has broad

application prospects in biology, robotics, medicine

and other fields (Butler, 2023). For example, in

medical and biological research, GlowTrack can help

doctors and scientists better understand the

movement patterns of animals and humans, thereby

revealing how the brain controls behavior (Butler,

2023). This may help study movement disorders such

as amyotrophic lateral sclerosis (ALS) and

Parkinson's disease. In the medical field, GlowTrack

can be used to monitor and analyze patients'

movements, thereby helping doctors diagnose and

treat various movement disorders (Butler, 2023). The

medical applications of this technology are

expanding, especially in the fields of neurosurgery

and orthopedics, where it is expected to considerably

improve the surgical success rate.

3 THE ROLE OF AI

TECHNOLOGY IN SURGERY

Take the surgical robot in the da Vinci system as an

example. The da Vinci surgical robot is an advanced

AI-integrated surgical technique that can help doctors

perform minimally invasive surgery, including a

variety of surgeries on multiple parts of the human

body (DiMaio, 2011). It utilizes high-precision

robotic arms and cameras to help with the surgery,

which embodies a better visual field and achieves

more precise operations. The da Vinci surgical robot

is expected to reduce surgical trauma and recovery

time, improving surgical safety and effectiveness

(Iftikhar, 2024). It reflects the synergy between AI

technology and medicine in surgery. Newer iterations

and auxiliary technologies have begun to use AI to

magnify the functions of the da Vinci surgical robot,

for example: the latest advances in ML algorithms

and computing power enable AI to analyze complex

data sets, more accurately predict outcomes, and even

assist in real-time decision-making. The da Vinci

system surgical robot can assist doctors in completing

extremely difficult surgeries, which not only have

high requirements on accuracy, but also have strict

demands on error rates. The following paragraphs

elaborate on the working principles of da Vinci

system and other surgical robotic systems and how

the integration of AI has enhanced the utility of them:

AI-driven machine learning to improve precision.

The da Vinci system uses learning algorithms to

ICDSE 2025 - The International Conference on Data Science and Engineering

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analyze successfully performed surgeries in the past

and conduct learning simulations to improve robot

movements and precision.

AI-automated surgical assistance assists surgeons

with fine suturing, tissue manipulation, and real-time

feedback to improve surgical outcomes (Iftikhar,

2024).

AI-driven simulation environments provide

surgeons with training opportunities for difficult

surgeries and improve their proficiency in the da

Vinci system.

AI processes surgical videos and sensor data,

optimizes surgical data, and provides postoperative

observations, thereby enhancing the success rate of

subsequent surgeries.

Effective applications of da Vinci surgical robots

in various surgical scenarios. For example, AI-

assisted robotic systems can be used in minimally

invasive heart surgery to shorten recovery time and

reduce surgical risks. AI robots can improve the

precision of complex brain surgeries and reduce the

risk of damage to surrounding tissues. AI-driven

robotic arms assist in orthopedic joint replacements,

which improves alignment and reduces postoperative

complications.

During surgery, by filtering hand tremors and

providing 3D views, AI assists robotic systems which

improve precision. AI also analyzes real-time medical

images to guide surgeons, such as detecting nerves to

avoid damage, as seen in neurological exams. This

can improve accuracy, especially in complex

surgeries, and while merging these systems into

workflows remains a challenge, the technology

currently provides surgeons with especially

meticulous aid and advice.

The AI features in the da Vinci surgical system

help improve patient safety, reduce hospital stays, and

improve surgical outcomes. It also reduces workload

for medical staff and optimizes medical resources.

This makes the da Vinci surgical system the pioneer

of AI technology in clinical surgery.

4 AI TECHNOLOGY IN

POSTOPERATIVE CARE AND

MONITORING

After the surgical procedure is completed, doctors

need to track and guide the patient's postoperative

care and monitor, as well as their vital signs status. In

fact, not only patients after surgery, but also patients

with chronic diseases need long-term medication

counseling and body index analysis by doctors. AI

technology can help doctors utilize time and space,

allowing convenient and efficient monitoring and

guiding for a long time.

AI models predict possible complications after

surgery by analyzing vital signs and biomarkers. A

study published on JAMA Network conducted

experiments to predict 6-month mortality among

patients with cancer (Parikh, 2019). The research

team applied the Random Forest Model to achieve an

experimental outcome of an AUC of 0.94 and a PPV

of 51.3% (Parikh, 2019). Furthermore, 58.8% of

patients flagged as high risk were considered

appropriate for a discussion of goals of care and end-

of-life care. This helps doctors to implement early

intervention if necessary and facilitates prognostic

management.

Wearable sensors that are paired with AI can

analyze patients' mobility, patients’ pain level after

discharge, and their postoperative recovery. It can

also detect the physical condition of patients with

chronic diseases and warn of the incidence of chronic

diseases through body biomarker indicators.

AI technology can monitor patients' emergency

status in real time in the future. Once the patient is in

danger, AI devices can take measures to rescue the

patient and quickly call emergency services for the

patient.

After surgery, AI monitors the patient's recovery

status, thereby helping to better recover and possibly

speed up recovery. For training, AI simulators like

LapSim provide a realistic practice environment and

offer trainees with tracking feedback, which can

standardized postoperative care skills and help boost

rehabilitation levels of patients across the institution.

5 ETHICAL AND SAFETY

CONSIDERATIONS

In today's society, although the integration of AI into

medical care brings about many conveniences and

benefits, it also raises issues such as data privacy and

security risks. The system needs to comply with

regulations to protect patient information. The

training data should be accurate and representative.

The training algorithm of AI should be transparent.

Data should conform to standardization and

interoperability (He, 2019). Explicit attention should

be paid to patients’ safety. It is crucial to keep in mind

that AI is not adequate to deal with unique cases, that

is, for specific special patients or cases with body

structures different from ordinary people, AI might

not be able to offer special treatments. There is also a

The Application of AI Technology in the Medical Field, and Its Future Direction

411

risk of bias in AI predictions, which may affect the

completion of the operation. The safety risks of AI

medical technology could also result from the

mistakes of developers and manufacturers during the

design and production process. Moreover, the

technology is not likely to be transparent to the

public, so it cannot be openly supervised by society.

In this case, all supervision responsibilities are fully

borne by the government. However, against the

backdrop of the rapid advancement of AI technology,

the government was not yet clear about its

responsibility guidelines in the early stage. So from

the perspective of ethics and safety, AI-driven

medical technologies should be jointly supervised by

relevant industries and government departments,

highlighting the necessity of transparent and

collaborative human-computer interaction.

The ethical considerations caused by AI medical

technology are also reflected in the initial trial stage

of AI. A large number of volunteers, patients and

deceased people are required to cooperate and support

the experiment and development of AI medical

technology. Whether they have full knowledge of

relevant experiments and fully understand their risks,

and whether these experiments comply with local

laws (considering that new technologies are not

necessarily regulated by sound laws and regulations)

all raise ethical and safety considerations. If an

uncontrollable medical accident occurs, whether the

patient and the surgeon can discover and interrupt

treatment in time and have good remedial measures,

this may not be fully guaranteed during the

experiment.

The application of AI technology may cause an

increase in the unemployment rate of medical staff or

a reduction in the wages of some medical staff,

bringing risks related to ethics and social stability

(Joseph, 2025).

6 CHALLENGES AND FUTURE

DIRECTIONS OF AI

TECHNOLOGY IN THE

MEDICAL INDUSTRY

As mentioned above, medical technology based on

artificial intelligence (AI) has developed swiftly, but

clinical applications have not yet been fully

popularized, and some key practical issues still exist

in the application of AI to existing clinical settings,

including data privacy, algorithm transparency, and so

on. These pose a major challenge for the future of AI

technology in the medical community. When data

sharing and privacy cannot be guaranteed, a large

amount of medical data stored in network systems will

face the risk of being hacked and leaked, while at the

same time, the data may be used for other research or

other commercial purposes, contrary to the patient's

wishes (He, 2019). If the regulatory authorities in

governments around the world fail to provide effective

and safe implementation standards, the future of AI

medical technology is subjected to vulnerabilities and

ethical risks. Ethical issues have always accompanied

AI since its inception. In the field of medical care, the

main concern is accountability, because any decision-

making mistakes could lead to serious consequences.

Since medical professionals do not create or supervise

algorithms, it does not seem fair to hold them

responsible. However, on the other hand, it does not

seem fair to hold AI technology developers

responsible because they are excluded from

contributing in the clinical decision-making process

(Joseph, 2025). Therefore, in response to this

situation, in some countries, AI cannot legally make

any medical decisions without human involvement,

and those who do so will be held accountable. Another

major issue is "whether it violates the patient's right to

know." This happened in 2018 when DeepMind, an

AI-based research lab, was acquired by Google

(Mountain View, California, USA). It was discovered

that the National Health Service (NHS) provided 1.6

million patients' data to DeepMind servers to train its

algorithms without having the patients to know [10,

(Martin, 2017)]. Their application Streams has an

algorithm for managing patients with acute kidney

injury, which was criticized for collecting data without

consent and was therefore considered a data leak. This

is also a challenge for AI technology itself, because no

matter what field AI is applied to, it should be based

on the safety of people, the regulatory system, and the

protection of nature.

Looking into the future, an important application

of AI in the medical field is the integration of AI into

medical imaging and medical surgery. This represents

a major transformation in medicine. Although

traditional surgery is effective, it is inherently limited

by factors such as fatigue of surgeons, current medical

level and surgical ability, and traditional medical

equipment. AI technologies, including machine

learning (ML), deep learning (DL), and medical

imaging, are addressing these limitations through, for

example, fine-tuning detection, surgical precision,

reducing complications, and personalized patient care.

According to a 2024 report by Grand View Research,

the global medical artificial intelligence market is

expected to reach $187.7 billion by 2030 (Grand View

Research, 2024). With the development and

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popularization of AI technology, AI-integrated

surgical robots may possess the potential to replace

doctors to accomplish high-precision, high-

complexity surgeries in the future that human power

cannot do. These robots may be able to serve poor

countries with underdeveloped medical levels and

tackle practical problems related to medical care that

these countries cannot handle. AI-based medical

signal transmission devices can also achieve "In-home

medical care." Patients who need constant monitoring

by doctors for their illness or rehabilitation status can

use AI-based medical transmission devices to detect

and upload their own conditions to doctors to achieve

home medical care. In particular, patients with chronic

diseases, such as hypertension and diabetes, can utilize

these devices to get medical examinations at home and

get immediate personalized feedback from the device.

Then this record, including the patient’s data and AI’s

feedback will be simultaneously uploaded and sent to

the doctor. This can greatly reduce the workload of

doctors, help ease the imbalance of the doctor-patient

ratio in some countries, reduce unnecessary medical

expenses for hospitals and families, and help patients

achieve simple modern medical care.

7 CONCLUSION

In the 21st century, AI technology has made

outstanding contributions in the medical field, like

improving diagnostic accuracy, improving medical

efficiency, improving surgical success rate,

promoting the development of personalized

medicine, helping to optimize medical resources, and

accelerating the progress of medical technology. It

not only marks the sustainable development of AI

technology in the future, but also marks a major

change in medicine. The two influence and

complement each other. However, for new

technologies in the developing stage, there are always

pros and cons. AI-integrated medical technology is

also facing challenges in multiple dimensions, such

as data security, ethics, personal privacy, safety, and

technical security. Whether AI technology can be

further improved in the medical field requires not

only the support from the government and the general

public, but also the confirmation from modern

science and medicine to prove that these technologies

meet various safety and ethical standards. It requires

the progress of human civilization, the progress of

science and technology, and the improvement of laws

and regulations.

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