Telemedicine: What it is, how it works and its relationship to AI

by Luis Daniel Fernádez | Apr 24, 2025 | AI in medicine

In recent decades, the introduction of new technologies in the health sector has enabled the development of new forms of healthcare. It is becoming increasingly common for patients to receive a remote medical care by healthcare professionals, through video consultations, remote monitoring or digital diagnosis. This is what is known as telemedicine.

The main objective of the incorporation of new Information and Communication Technologies (ICT) in medicine was to bringing health services closer to the population who resided in remote regions and who had a reduced health accessibility and resources. Over the years and with the development of new technological innovations, including the emergence of artificial intelligence (AI), ICT became a key tool in the development to improve the quality and efficiency of healthcare.

What is it and how did it come about? In the following article, we explain what telemedicine is, how it works and its relationship with AI, as well as its different advantages and disadvantages.

What is telemedicine?

Telemedicine is the a set of medical practices that use information and communication technologies (ICT) to provide remote health care services.. This includes medical consultations, diagnoses, treatments, clinical follow-up, issuing prescriptions and preventive guidance, without the need for the patient and the professional to be physically present in the same place. For this purpose, telemedicine allows access to health services through electronic devices such as computers, tablets or smartphones, through secure digital platforms.

The World Health Organization (WHO) defines it as: "The provision of health services at a distance through the use of information and communication technologies for diagnosis, treatment, disease prevention and research."

Telemedicine and eHealth (digital health): What are their differences?

At the same time, the eHealth (digital health) conceptThe term "health informatics", which encompasses a broader term and is located between medical informatics, public health and commercial interest. It refers to the use of information and communication technologies (ICT) to improve, support and optimize the delivery of healthcare services and the management of the healthcare system.

Although often used synonymously, eHealth and telemedicine are not the same thing, although they are closely related. While eHealth telemedicine connects physicians and patients at a distance, eHealth encompasses all types of digital tools that improve health.. From an app to a hospital management system, such as the PACS system or the RIS system.

In this context, the telemedicine concept is part of the eHealth ecosystem. and focuses specifically on the provision of medical services, providing remote consultation, diagnosis and treatment.

Origin: Technological stages and evolution

Telemedicine is not a recent concept. Its origin dates back to the 1950s.The first remote communication systems began to be used for medical purposes. One of the first documented cases was in the United States, where a telephone line was used to transmit radiological images between hospitals. The following stages can be distinguished:

Early decades: 50's-70's

In the late 1960s, the NASA played a key role in the development of telemedicine. Faced with the need to be able to monitoring the health of astronauts during space missionsThe development of technologies capable of recording and transmitting biometric data remotely was promoted. These advances were also applied in rural Alaska through the STARPAHC (Space Technology Applied to Rural Papago Advanced Health Care) program, considered one of the first structured telemedicine projects.

Technological evolution: 1980s to 2000

During the 1980s and 1990s, the improvement of computer technology and satellite communications allowed the expansion of telemedicine services, especially in rural and military environments. The use of telemedicine systems for teleconsultation, remote diagnosis and exchange of clinical information between hospitals.

In these years, video calls started to be used for dermatology, psychiatry and radiology consultationsThe use of telemedicine was limited, however, by the high cost of equipment, lack of infrastructure and poor digitization of medical records. However, the use of telemedicine was limited by the high cost of equipment, the lack of infrastructure and the scarce digitalization of medical records.

Digitalization and the rise of the Internet: Years 2000-2010

With the the advent of the Internet, personal computers and smartphones.telemedicine took a qualitative leap forward. Starting in the 2000s, telemedicine began to develop more accessible platforms for online consultations, chronic patient follow-up, diagnostic test referrals and distance medical education. The first electronic medical record systems were also integrated, which facilitated networking among professionals from different health centers.

Telemedicine today

Although telemedicine was already in existence, the COVID-19 pandemic in 2020 marked a definitive turning point in its adoption worldwide. Faced with the need to avoid travel and minimize the risk of contagion, many healthcare systems began to offer video-call consultations, electronic prescriptions, remote monitoring and online psychological care. Today, telemedicine has established itself as a standard medical solution in many countries and has been integrated into public and private healthcare services.

The relationship between telemedicine and artificial intelligence

The development of the artificial intelligence in medicineIn recent years, remote monitoring devices and predictive algorithms are driving the rise of telemedicine. In recent years, the combination of telemedicine and artificial intelligence is transforming the way medical care is delivered to patients. Both technologies, when combined, improve the efficiency, accuracy and accessibility of the different healthcare services. However, although they are two complementary tools, each one has its own operation and its own specific characteristics. medical applications specific.

On the one hand, the telemedicine enables health care professionals to to care for patients remotely. On the other hand, the IA is responsible for analyze large volumes of medical data, detect patterns, automate tasks and suggest diagnoses or treatments.

Therefore, when used together, more agile, intelligent and personalized systems are created. At present, the use of AI software to improve the efficiency and accuracy of medical diagnosis, as well as facilitate patient management and healthcare.

Types of telemedicine: We analyze how it works and what it is used for.

Since the emergence of telemedicine and with the evolution of different technologies, different types of telemedicine have been developed that define the concept as we know it today. Below, we analyze how each of these modalities works and what they are used for:

Teleconsultation

The medical consultation represents the basis of clinical practice in the field of medicine. For this reason, teleconsultation is the most commonly used modality. It is based on the search for local or external medical information or medical advice, using information and communication technologies.

Communication between the patient and the healthcare professional can be done directly or through third parties. Thus, there are two different types of teleconsultation: asynchronous and synchronous.

• Asynchronous teleconsulting

In this type of teleconsultation, known as asynchronous, medical care is provided by means of the The physician will send clinical information and, subsequently, the physician performs the assessment and counseling.

Main advantages

1. The parties involved do not have to be present at the transfer of information.
2. It offers the ability to capture and store still and moving images of the patient, as well as audio and text, providing the healthcare professional with more clinical information.
3. It is an economical and accessible modality, since it supports a large volume of work and analysis of medical tests.

• Synchronous teleconsultation

Synchronous teleconsultation is developed in real timeand, therefore involves the participation of patients and health care professionals in sending information through the use of telecommunication technologies.

In this modality, videoconferencing stands out as the most commonly used technologyIt provides both visual and auditory contact with the patient. This facilitates pattern recognition and greater accuracy in making a medical diagnosis.

Main advantages

1. Fast and effective diagnosis
2. Better understanding between patients and professionals health care
3. Integration of additional techniques that increase reliability
   of clinical information. This is the case of digital auscultation.

Main disadvantages

1. Its execution involves a number of huge costs economically, since it requires a certain telecommunication infrastructure.
2. Requires a increased demands on the time of medical professionals, as they must allocate time for the teleconsultation and, additionally, carry out a pre- and post-evaluation.

Teleeducation

It is defined as the use of information and telecommunication technologies for distance medical education practice. In this field, Internet technologies and videoconferencing are the means most commonly used by health professionals to increase their skills and put their knowledge into practice. Within tele-education, different modalities can be distinguished, depending on the way in which the information is transmitted:

• Tele-education through teleconsulting

A physician who is an expert in a particular specialty provides a diagnosis to the query raised by a non-expert physicianintern or resident.

• Clinical education via the Internet

Allows the access to various databases with medical and clinical articles and books. These include MedLine, Cochrane, the National Library of Medicine in the United States and the National Electronic Health Library in the United Kingdom.

• Academic studies via Internet

Different universities, both public and private, offer courses and tele-educational programs, as well as virtual internships., where participants are evaluated and graded to obtain a set of competencies that will allow them to develop their professional career in the health area.

• Public education through telemedicine

Refers to the medical education and communication offered on different topics related to public health. From diet, exercise and hygiene websites to different diseases, such as cancer and AIDS.

Telemonitoring

Telemonitoring is the use of information and communication technologies to obtain information regarding the patient's condition and status to determine if adjustments or changes to the proposed treatments are necessary.

This type of telemedicine allows professionals to monitor different aspects: physiological variables, test results, as well as images and sounds. It is usually performed at the patient's home or in nursing centers, which reduces costs and resources for the healthcare system.

Telesurgery

Telesurgery is based on the development and execution of surgeries where the surgeon acts by means of remote visualization and manipulation using electronic devices and high technology in telecommunications. The main objective of telesurgery is to offer surgical services to patients who, for reasons of inaccessibility, cannot be attended in person in hospitals and medical clinics. Telesurgery has two different modalities, which are discussed below:

• Tele-surgery through tele-education or telementoring

Telesurgery by means of tele-education or telementoring is an advanced form of remote surgical training and medical care that combines telecommunications technology, real-time surgery and medical teaching techniques.

The telesurgery by means of telementoring is that a skilled surgeon (mentor) provides technical advice, corrections, instructions or live training during a surgical procedure being performed by a less experienced surgeon (trainee)even if they are in different geographical locations. This is achieved by means of videoconferencing systems, augmented reality, laparoscopic cameras or interactive platforms.

For its part, the surgical tele-education goes beyond the operating room and encompasses also theory sessions, case review, virtual classes and guided surgical simulationall from a distance.

• Telepresential surgery

Telepresential surgery is an advanced modality of technology-assisted surgery that enables a surgeon to control surgical instruments remotely using robotic systems connected by high-speed telecommunications networks.

This is a form of telesurgery and allows the professional to act as if he were in the operating room through the use of technology. For example, the use of robotic arms, micro cameras and high resolution optical instruments.

Advantages and disadvantages of telemedicine

Advantages of telemedicine	Disadvantages of telemedicine
Facilitates access to medical care from any location	Does not allow complete physical examinations
Reduces travel and waiting times	Dependence on devices and good internet connection
Saves costs for patients and healthcare facilities	Digital barriers may exist for the elderly or people with few resources
Improved follow-up of chronic patients	Some specialties are not compatible (e.g. surgery, dentistry).
Promotes continuity of care and preventive care	Loss of human and nonverbal contact in the doctor-patient relationship.
Contributes to sustainability by reducing carbon footprint	Medical data security and privacy risks

Main advantages of telemedicine

Telemedicine and technological innovations in the healthcare field have provided a number of benefits, driving the improvement of healthcare and medical diagnosis.

Access to health care from anywhere

Telemedicine makes it easier to people living in rural areas, with limited health care resources or with reduced mobility can receive medical care without the need to travel. In this way, it is characterized by bringing medical care closer to different groups. From the elderly and migrant population to patients with disabilities, which improves health equity.

Time savings and convenience

Avoids trips to health centers or hospitals and eliminates waiting times in consultation rooms. The patient can perform consultations from home, obtaining greater time flexibility and without interrupting his or her daily routine. On the other hand, consultations are shorter and more directThe work of healthcare professionals is optimized.

Cost reduction

It reduces costs for both patients and health centers. On the one hand, patients avoid the expenses associated with transportation, permit applications labor andIn many cases, it is also reduces the cost of consultation. And, for health centers, it represents a significant saving by reducing the need for physical infrastructure, logistical resources and on-site personnel. Thus, it favors the efficiency of the public and private system.

Effective chronic disease monitoring

Allows you to real-time patient monitoring with pathologies such as diabetes or hypertension, avoiding complications and improving adherence to treatment.

Better doctor-patient communication

Promotes a closer and continuous careThe system is ideal for resolving doubts, reviewing tests or following up with the patient without having to visit the health center in person.

Optimization of the healthcare system

Reduces the burden on emergency and primary care by filtering non-urgent consultations and improving medical resource management.

Positive impact on the environment

By reducing the number of trips, we also carbon footprint is reduced associated with medical care.

Boosting digital health and education

Allows to integrate educational content and interactive resources to assist the patient to take care of your health from home. And, at the same time, it makes the medical training for professionals through tele-education.

Greater control and security

Digital platforms protect the patient privacy and generate a electronic medical record with better traceability and follow-up. In this way, patients can consult their records, tests and treatments digitally.

Disadvantages and limitations of telemedicine

Although telemedicine offers numerous benefits, it also presents a number of limitations and challenges:

Direct physical examinations are not possible

The main limitation is that direct physical examinations are not permitted, which can make diagnosis difficult in complex or urgent cases. Some diseases require palpation, auscultation or immediate tests that can only be done through a face-to-face consultation.

Technology dependence and digital divide

For telemedicine to work properly, it is necessary to have a good Internet connection, the use of appropriate medical devices and knowledge of the use of digital applications. Not everyone has access to digital tools or the Internet. This may exclude older people or those with limited technological resources.

Difficulties in the patient-physician relationship

Physical contact and nonverbal communication are key in the clinical relationship. In some cases, telemedicine may lead to a feeling of distance or lack of empathy between health professionals and patientsespecially in consultations with sensitive diagnoses.

Data privacy and security risks

The use of digital platforms entails risks of leakage or misuse of personal and medical information if the following guidelines are not applied. appropriate cybersecurity measures.

Technical limitations and connection failures

Technical problems such as network outages, poor image or sound quality, as well as software malfunctions can interrupt or hinder consultations, affecting the quality of service.

Restrictions on certain medical specialties

Not all areas of medicine are well suited to the virtual environment. For example, surgery, traumatology or dentistry require mandatory physical presence, and telemedicine can only complement some processes, not replace them.

Telemedicine represents the combination of technology and health services. Over the years, its evolution has driven an increasingly complete and efficient healthcare system. In this context, the relationship between telemedicine and artificial intelligence stands out, since it offers a higher quality of health careThe aim is to provide a more accurate medical diagnosis and the development of medical treatments customized to the real needs of patients.

Bibliography

Otero López, M. J. (2012). Telemedicine: A tool also in the rural environment. Primary Care, 44(10), 574-575. https://doi.org/10.1016/j.aprim.2012.03.016

Torres, M. R. R. R., & Collado, M. E. M. (2016). Telemedicine: Current status and perspectives. Clínica Las Condes Medical Journal, 27(5), 571-577. https://doi.org/10.1016/j.rmclc.2016.09.003

Peña González, A., & Córdova Alcaraz, L. (2017). Application of telemedicine in primary health care. Cuban Journal of Information in Health Sciences (ACIMED), 28(2), 135-145. https://www.redalyc.org/pdf/2611/261120984009.pdf

Sánchez-Guzmán, M. A., & González, S. M. (2015). Telemedicine: technological innovation in health. Iberoamerican Journal of Educational Research and Development, 6(12), 1-16. https://www.redalyc.org/pdf/2310/231019866002.pdf

Luis Daniel Fernandez Perez

Director of Diagximag. Distributor of medical imaging equipment and solutions.

Laboratory analysis and hospitals: How to implement AI

by Kiko Ramos | Jan 24, 2025 | AI in medicine

The use of Artificial Intelligence (IA) is transforming medical care in laboratories, clinics and hospitals. Through the use of technology, patient care can be improved, laboratory analysis processes can be optimized and diagnostic imagingas well as offering more efficient hospital management.

Artificial intelligence uses various algorithms that enable highly complex reasoning processes to be carried out, automating many tasks and functions. The use of AI in medicine provides multiple benefits and has a key role to play in the implementation of disease prevention and diagnosis, search for novel treatments and improvements in patient prognosis.

In the following article, we explain the process for implementing Artificial Intelligence solutions in laboratories, clinics and hospitals and the different applications that currently exist.

How to implement AI in laboratory and hospital analysis

Before starting to use artificial intelligence in the clinical setting, it is important to have an well-defined and structured strategy that integrates the technology along with the correct development of the process. These are the main steps to implement AI effectively:

1. Define the main objectives

The first step is to establish the objectives to be achieved with the integration of AI in the healthcare center. Among them, we can highlight:

• Reduction of diagnostic times.
• Customize treatments.
• Optimize resource management.
• Improve patient experience and care.

By setting clear goals, specific solutions can be provided using artificial intelligence, which will enable optimize healthcare management and save time and resources.

2. Analyze weaknesses and needs

Once the main objectives have been set, it is essential to carry out a complete diagnosis of the laboratory, clinic or hospital to analyze its weaknesses. This analysis should include the review of workflows the identification of the most important main problems and the areas that have a greater administrative or technical burden.

On the other hand, it is also important to involve medical, administrative and technical personnel in this process, as their day-to-day experiences provide a more accurate picture of real needs. Through a collaborative approach, AI solutions will be aligned with the specific challenges faced by the organization.

Select the right AI tools and solutions.

Subsequently, you must selecting the artificial intelligence technologies best suited to the hospital area. AI tools are revolutionizing the healthcare sector, especially in hospitals and laboratories, by improving diagnostic accuracy, increasing operational efficiency and delivering better healthcare. In this process, it is important to research the options available in the market and work with specialized healthcare technology providers.

Ensure proper integration into the healthcare ecosystem.

For the successful implementation of AI, it is crucial that new technologies are integrated with the systems that were already in use previously. Some of the tools we can highlight are hospital management software and its linkage with medical equipment, as well as the RIS system and the PACS system.

One of the essential aspects to achieve a correct integration is the interoperability concept. It refers to the importance of systems being compatible and capable of sharing information so that they can work in a coordinated and joint way in the different processes. For this reason, before applying the use of artificial intelligence, it is necessary to check that the systems to be used are compatible with each other.

5. Staff training

Another element to be taken into account is to provide a adequate staff training who will work with these technologies. This includes both medical and administrative staff, as they will be in charge of managing the tools, interpreting the data provided by AI and making the most of them in their day-to-day work.

In addition, it should fostering a culture of trust in technologyHe stressed that AI will not replace professionals, but is a tool that complements and improves their work. With this, it will be possible to ensure a correct transition to the application of new processes and innovations.

6. Ensuring data security and privacy

The management of medical data involves a great responsibility in terms of security and privacy. The implementation of AI must comply with local and international regulations, such as the General Data Protection Regulation (GDPR) in Europe. In this way, it will be possible to ensure that patient information is protected at all times.

The main measures include the correct data encryption, a user authentication and the anonymization of information whenever possible. In addition, it is crucial to conduct regular audits to identify and correct possible vulnerabilities in the systems.

7. Implement in a staggered manner

Introducing AI gradually is a fundamental strategy for minimize disruptions to day-to-day operations and facilitate staff adaptation. You can start with a pilot project in a specific unit, such as radiology, and evaluate its impact before extending the implementation to other areas.

During this phase, it is important to collect feedback from staff and adjust the tools according to their needs and suggestions. Through this step-by-step approach, improvements can be made progressively and achieve a adequate adoption of new artificial intelligence tools.

8. Monitor and measure results

The implementation of AI must be accompanied by an continuous monitoring to ensure that the solutions are meeting the established objectives. This involves define key performance indicators (KPIs)The results of this study have been significant, such as a reduction in diagnostic time, an increase in operational efficiency and an improvement in patient satisfaction. Regularly evaluate these results will identify areas for improvement and adjust strategies as needed, taking full advantage of the benefits of artificial intelligence in healthcare.

9. Promote continuous innovation

The implementation of AI is not an isolated action, but rather a continuous process. Technology is a sector that is constantly evolving. Therefore, it is important to be aware of new tools and methods in the healthcare area in order to be able to implement future improvements. To ensure that a medical institution is committed to innovation and is competitive in its sector, the following can be done to promote various actions. Among them, we can highlight:

• Foster a culture of innovation among the personnel.
• Participate in research programs.
• Collaborate with universities or technology companies.
• Implement new tools and methods. 

Artificial intelligence solutions for laboratory analysis, clinics and hospitals

Source || Freepik

What kind of solutions can be implemented to optimize clinical and hospital management?

Software with artificial intelligence

Through the use of a AI softwareon the same platform, you can storing medical images generated in diagnostic imaging studies, manage patient data in real time, generate automated reports and make comparisons of current studies with previous medical imaging.

AI-assisted diagnostic imaging

The current medical equipment can integrate diagnostic imaging software with AI. These systems employ advanced algorithms to identify anomalies and diseases early, improve diagnostic accuracy and reduce analysis time. They can be used for different types of equipment, ranging from X-raysCT scans, CT scans or TAC, ultrasounds y mammograms to magnetic resonances.

Virtual agents for laboratory analysis and hospital centers

A virtual agent provides automation of different tasksIt can therefore be implemented in the healthcare sector to optimize the management of medical centers, clinics and laboratories. Through an artificial intelligence platform such as Serenity Star AIcan be implemented chatbots and virtual assistants that offer 24-hour patient support, improving customer service. Among its advantages, it stands out for providing instant information on hospital services, resolving patient queries, guiding patients in their search for specialists, and managing appointments and other administrative procedures.

The use of virtual agents also offers other functions very useful in research and hospital management. They allow the analysis of complex medical data with high accuracywhich allows accelerate the performance of medical studies and develop improvements and innovations in areas such as research and laboratory analysis.

Laboratory process automation

There are AI systems that allow automate many functions in laboratory analysis processes. From the performance and analysis of clinical tests to inventory management and the implementation of quality control improvements. Its use helps reduce human error, increase operational efficiency and reduce study processing time.

AI-assisted surgical robots

In the field of surgery, AI and robotic systems are making a difference. The use of AI-assisted surgical robotsas Da Vinci, help to realize more precise and less invasive procedures, decrease surgical risk and reduce recovery times of patients.

Another of the most noteworthy advances in this area is the creation of surgical simulation models to plan, practice and refine procedures before performing them in clinical practice.

Advances in telemedicine: Use of portable and AI-integrated medical equipment

Among the latest innovations, we can highlight the development of portable and AI-integrated medical devices. Its use offers continuous monitoring of patients outside the hospital environment, achieving great advances in telemedicine.

The telemedicine is one of the most outstanding areas of medical innovation, as it allows for assisting people with chronic diseases remotely and reach regions where medical services are not fully available. In this way, regardless of the specialist's location, fast and accurate diagnoses can be made.

Implementing artificial intelligence in laboratories, clinics and hospitals is a process that requires planning, collaboration and a strategic vision. From identifying needs to monitoring results, each step is crucial for ensure that AI is integrated effectively and generates tangible benefits. With proper execution, AI can transform healthcare, improving service quality, optimizing resources and ushering in a new era in healthcare management.

Contact with us to implement AI in the hospital environment.

Kiko Ramos

CEO of 4D Médica. Expert in marketing and distribution of medical equipment.

Artificial intelligence and medical image analysis

by Luis Daniel Fernádez | Dec 5, 2024 | AI in medicine

The progress of new technologies has enabled a great evolution in the field of medicine. Nowadays, artificial intelligence (AI) has become a fundamental tool in different medical specialties, among which the area of image diagnosis. The integration of AI in medical diagnostics offers a multitude of benefits: increased accuracy and quality of diagnostics, early disease detection, task automation, workflow optimization, creation of personalized treatments and preventive measures.

Obtaining a rapid, accurate and effective diagnosis is a key aspect of achieving more efficient healthcare. The use of traditional methods involves the analysis of a large amount of data and the performance of tasks that involve a large amount of data. investment of time and resources. In addition to these aspects, there is also the limitation of the human subjectivityThe use of algorithms for the diagnosis and treatment of the disease, which can lead to errors in clinical practice. In this sense, the use of Artificial Intelligence in medicine has had a remarkable impact on diagnostic imaging. In the following article, we look at how AI that analyzes medical images works and its main applications.

Artificial Intelligence techniques in the analysis of medical images.

Artificial intelligence studies, designs and develops computerized computer systems based on algorithms. that can emulate some of the functions performed by humans, such as thinking and learning to solve problems. An algorithm consists of a set of computer instructions that are designed to perform a specific task. In recent years, a number of tools have emerged, such as the AI-enabled software that use artificial intelligence to automate many tasks and functions in the clinical setting.

What type of technology is used in medical imaging and how does it work? We can differentiate between different techniques:

Machine Learning (ML)

Machine Learning (ML) is a field of artificial intelligence which consists of the use of computer algorithms to analyze and classify data, to learn from it and to make future predictions. The system must comply with a training phase which is referred to as supervised. During this process, medical images are entered with their corresponding, manually implemented labels. As more data is exposed, the algorithm learns to give a specific answer by evaluating different hand-labeled tests.

Most imaging systems make use of this type of artificial intelligence and it is important that, before using it in clinical practice, the system has been tested and validated. One of its main uses is to predict diseases at an early stage. For example, analyzing the probability that a breast lump visible on mammography is a malignant tumor.

Representational Learning or Representation Learning (RL)

Representation Learning (RL) is a sub-type of Machine Learning (ML) that does not require image features to be labeled by hand. Computer algorithm learns on its own the necessary characteristics to classify the data provided. Therefore, human subjectivity is eliminated, i.e., the limitation of analyzing those characteristics that the human being considers relevant. This system is called unsupervised learning and, if sufficient data is provided, the performance that can be obtained is superior to traditional ML.

Deep Learning (DL)

Deep Learning (DL) is an advanced form of Representation Learning (RL). This type of algorithm is in charge of exploring the use of artificial neural networks.based on the structure and function of the human brain. The artificial network of neurons is composed of different layers and connections. Through each layerIn addition, a series of data is propagated that is linked to the performance of a specific task.

In the area of diagnostic imaging, each layer is responsible for analyzing a characteristic of the medical image and assigning a value to it. Subsequently, the final layers of neurons are responsible for collecting all the information and providing a result. This type of technology has great potential and interest in medical image analysis, as it allows multiple uses. From the automatic detection of a lesion in the images and suggest differential diagnoses to structure a report in a preliminary way.

6 applications of AI in medical image analysis

Artificial Intelligence has the ability to process large amounts of data and recognize complex patterns. We can highlight the following applications in the field of diagnostic imaging:

1. Attendance at the radiologist's work

Power of Attorney manage patients' medical records electronically is a very important step forward, since it facilitates the work of the various medical teams involved in the diagnostic imaging process. AI can help to highlight the most relevant data and propose a specific planning of the study to provide information to the different professionals: the clinician, the technician and the radiologist.

2. Optimization of the radiological technique

Using Deep Learning (DL) methods, the algorithms allow for reconstruct images in medical techniques such as magnetic resonance imaging and computed axial tomography, or TAC. With this, the quality of medical images can be increased, making the most of the technical and physical resources available. Another advantage offered by AI is that it makes it possible to establish the ideal amount of radiation for each patient, avoiding the addition of unnecessary radiation.

3. Segmentation and lesion detection

Through the use of AI, the systems can understand the visualized images of an examination and differentiate healthy structures from pathological areas.

4. Classification and diagnosis of pathologies

There are different machine learning algorithms that can identify specific patterns and characteristics in medical imaging for classify them into different disease categories. Currently, algorithms are being developed for the detection of tumors in mammography images and skin cancer in dermoscopy images. In this field, AI can identify cancerous tissues and classify them into specific cancer types, which can lead to faster and more accurate diagnostics.

5. Prediction of treatment response

Artificial Intelligence can also predict patient response to different treatments. Algorithms can access patient data and medical studies with the diagnosis of the patient's disease. With all this information, the patient's response to various treatment options can be predicted. This offers many advantages, as the following can be developed specific treatment plans with a personalized approachadapted to the needs of each patient.

6. Early detection of diseases

Another of the applications of AI in medicine is the early detection of diseases. Through the analysis of large amounts of data, it is possible to detect patterns that may be missed by traditional techniques. For example, one of the uses recently offered by machine learning algorithms is to be able to detect early changes in magnetic resonance images of the brain, which may be indicative of diseases such as Alzheimer's disease.

AI-assisted medical diagnostics is evolving rapidly. Ongoing research is seeking to refine existing AI models with the aim of exploring new applications to provide much more accurate, efficient and faster medical care.

Luis Daniel Fernandez Perez

Director of Diagximag. Distributor of medical imaging equipment and solutions.

AI applications in medicine and their impact on society

by 4D Medical | Nov 12, 2024 | AI in medicine

The use of new technologies and artificial intelligence (AI) has meant a before and after for many sectors. One of them has been medicine, where the latest advances and applications have been influenced by the development of technology. Artificial intelligence is a specialty in the field of computer science that is used to produce programs through a series of algorithms that have the ability to think, learn and make decisions, as humans do.

How does AI work?

AI began to be developed in the 1990s with the aim of creating a computer system that would process data in a similar way to the human brain. One of the branches of artificial intelligence that is most useful in the healthcare sector is the automatic learning. This system has the ability for machines to use the algorithms and learn from the dataThis improves decision making with the processed information.

Through the use of a AI softwareIn addition, a number of functions and tasks can be automated, allowing healthcare professionals to process and analyze medical data more quickly and accurately. This has a significant impact on the different areas of the health care sector and promotes improved healthcare management. Among the main uses offered by AI in the healthcare field, we find that it helps to develop and optimize processes in clinical diagnosis, disease detection and prevention, healthcare, research and the creation or updating of new drugs.

In turn, it has also been a determining factor in the progress of telemedicine and in the development of personalized medical treatments. In the following article, we address the key applications of the AI in medicine and how they are helping to create a more complete, agile and effective healthcare system.

AI applications in medicine

In recent years, AI has been incorporated into medicine to promote higher quality patient care, speed up processes and achieve increased diagnostic accuracy. What are the different areas in which artificial intelligence is currently being used and what improvements have they brought about?

Disease prevention and early diagnosis

AI is a key tool in disease prevention. Through the use of Big Datawhich consists of a combination of digital health data, genomic data and patient behavioral data, can be used as a basis for the development of a new identify risk factors and patterns that lead to the development of certain diseases.

• Spread of diseasesOn the one hand, machine learning algorithms can predict the spread of diseases such as influenza or COVID-19, anticipating epidemic peaks and allowing preventive measures to be taken.
• Detecting signs of chronic diseasesAnother of its applications is that early signs of chronic diseases, such as diabetes or heart disease, can be identified. Chronic diseases are characterized by their slow onset and, in most cases, go unnoticed until they develop into more serious complications. Therefore, the use of AI is very useful for detecting possible signs of disease in medical studies, such as blood tests, ultrasound images or electrocardiograms. In this case, AI algorithms can detect patterns of cardiovascular disease through medical images such as the magnetic resonance imaging or the computed tomography scans.
• Predisposition to genetic diseasesThrough the use of genomic data, artificial intelligence can also analyze predisposition to genetic diseases. AI algorithms are responsible for studying patterns in DNA to identify genetic variants that could indicate a high risk in the development of certain diseases. In oncology, it is used to predict the risk of breast or colon cancer, allowing doctors to design personalized prevention plans.

Clinical diagnosis

In the image processing and interpretation for diagnosisAI offers algorithms that improve the quality and accuracy of clinical diagnostics. They allow to recognize complex patterns in image data automatically, to eliminate noise to increase their quality and to establish three-dimensional models from images of specific patients. In this field, we can highlight the research by IBM researchers on a new study on the use of a new AI model can predict the development of malignant breast cancer.

With rates comparable to those obtained by human radiologists, this algorithm can learn and make decisions about cancer development from imaging data and patient history. Specifically, it was able to predict the 87% of the analyzed cases and was also able to interpret the 77% of noncancerous cases. Therefore, this model could be a fundamental tool to help radiologists confirm or dismiss positive cases of breast cancer.

Personalized medical treatments

Another use of AI in medicine is to find personalized medical treatments for each patient. Based on a set of factors, such as medical history, lifestyle and genetics, the AI algorithms can analyze a large volume of genomic and biomarker data to identify patterns and risk factors.

This can be used to develop a specific medical treatment for the patient's needsThe use of AI in oncology, for example, helps to identify the best treatment for each type of cancer, taking into account the specific genetics of the tumor. For example, in oncology, AI helps to identify the best treatment for each type of cancer, considering the specific genetics of the tumor.

Health care

Patient care is one of the areas where AI can provide great support to both medical professionals and patients. In this case, the AI-based virtual assistants are an ideal solution for automating functions and tasks. These include the appointment management, the realization of basic health consultations, the symptom assessment and the administration of medications.

Promoting telemedicine

These systems have also enabled the evolution of telemedicine. In this sense, professionals can monitoring patients suffering from chronic diseases remotely and receive alerts of possible anomalies that may arise in their health condition. This offers wide-ranging benefits in reaching a larger number of patients, especially those who live in regions that do not have all the health services in their localities and must travel to receive medical care.

Resource management in medical centers and hospitals

Another area where AI can be implemented is in the management of material and human resources in clinics, hospitals and health centers. Examining large amounts of data from historical records can be essential for to foresee the resources required in a given situationThe company's management and optimization of the available resources can be very helpful for the management and optimization of the available resources. This can be of great help to avoid overcrowding of medical centers at times of high demand and be able to manage the inventory of medical supplies and the availability of beds and medications.

Drug research and development

Artificial intelligence has been fundamental in the development of medical research, both in the development of new drugs as in the optimization of clinical trials. The integration of artificial intelligence into drug design involves a multidisciplinary approach combining both chemistry and biology concepts as well as computer science. to accelerate the discovery of new treatments and medical solutions.

For this purpose, AI models created with machine learning and deep learning algorithms are used to analyze large amounts of data on chemical and biological compounds and the interaction between them.

Robotic surgery

Robotic surgery systems such as the Da Vinci use AI to perform complex surgical procedures with greater control and precision. These robots are controlled by the surgeons to make small incisions, which helps to reduce the margin of error, perform minimally invasive surgeries and improve patient recovery times..

Another key area in which artificial intelligence can be applied is in the creation of customized surgical plans. In this case, the following are used data from previous surgeries to optimize techniques and to predict possible complications. that may arise during operations.

Training

AI has a key role to play in the training of health professionals. It provides multiple tools that help medical specialists to acquire and perfect their skills in different areas, increasing their knowledge in a more efficient and personalized way.

On the one hand, the medical simulations through AI allow students to be able to implementing complex procedures and reducing the risk of errors. At the same time, the following stand out learning platforms that use AI to adjust educational content based on the level of knowledge of the learnerThe aim is to achieve greater efficiency in the learning process.

In summary, AI has a wealth of applications in medicine and there are new improvements and innovations every day that help to further advance the healthcare sector.

Kiko Ramos

CEO of 4D Médica. Expert in marketing and distribution of medical equipment.

Diagnostic Imaging in Veterinary Medicine: Advances and Applications

by Kiko Ramos | Oct 10, 2024 | AI in medicine

The veterinary radiology has undergone significant progress in the last decade, radically transforming the way animal diseases are diagnosed and treated. With the introduction of advanced technologies and high-definition equipment, veterinarians are now able to obtain clear and detailed images that are crucial for a accurate diagnosis. This not only improves the quality of treatment, but also minimizes stress and discomfort for the animals during diagnostic procedures.

One of the most noteworthy innovations is the digitization of X-ray equipmentwhich allows instant viewing of images and facilitates faster and more accurate analysis. This technology not only speeds up the diagnostic process, but also reduces radiation exposure for both animals and operators, ensuring a safer and more efficient environment in veterinary clinics.

Main advances in veterinary diagnostic imaging

What advances have arisen in diagnostic imaging in the veterinary sector? We can highlight the following:

Use of CT and MRI in small animals

The use of CT in small animals has become an invaluable tool in veterinary radiology. This technique provides three-dimensional images of the inside of the animal's bodywhich is essential for accurate diagnosis of complex conditions. CT in small animals is especially useful for examining fine details of bones, internal organs and other structures that are difficult to visualize by conventional X-rays.

On the other hand, the veterinary magnetic resonance imaging offers significant advantages over other forms of diagnostic imagingespecially with regard to the soft tissue visualization. This method is extremely sensitive for detecting diseases of the brain, spinal cord and other internal organs in small animals. MRI does not use ionizing radiation, which makes it safe for repeated use for detailed follow-up and longitudinal diagnostics.

Advances in veterinary ultrasonography

Ultrasonography has revolutionized veterinary radiology applications, offering a new way of non-invasive diagnostic tool that is capable of to produce real-time images of internal organs. This technology is particularly useful for examining the abdomen and heart, as well as for reproductive management in a wide variety of animal species. With advances in transducers and improvements in image quality, ultrasonography allows veterinarians to perform precise diagnoses and procedures such as guided biopsies with high accuracy.

Innovations in veterinary cardiac ultrasound

Cardiac ultrasound has become an indispensable tool in veterinary medicine for the diagnosis and treatment of evaluation of the heart of the animals. This advanced technique enables veterinarians to observe the heart function in real time, offering a crucial window into the early detection of heart disease. Improvements in ultrasound machines and the development of more sensitive probes have significantly increased the accuracy of diagnoses, allowing earlier interventions and increasing the success rates of treating cardiac conditions in animals.

All these innovations, new trends and applications allow further progress in the veterinary sector to improve the diagnostics and treatments needed for all types of animals.

Kiko Ramos

CEO of 4D Médica. Expert in marketing and distribution of medical equipment.

The Future of Medical Equipment: Trends and Developments

by Kiko Ramos | Oct 10, 2024 | AI in medicine

The medical technology is advancing at an unprecedented pace, driven by ongoing innovation in areas such as robotics, telemetry and digital diagnostic systems.. These advances are designed not only to improve the quality of medical care, but also to make it more accessible. 

Advances in medical technology

Innovations in X-ray equipment, for example, now make it possible to perform procedures faster and with less radiation exposure, benefiting both patients and healthcare professionals. What kind of advances are there in medical technology? The following is an overview of the latest news and developments in the healthcare sector:

New developments in X-ray equipment

The integration of artificial intelligence is enabling X-ray systems to provide preliminary diagnostic recommendations, which can accelerate the medical evaluation process and improve the accuracy of the early detection of diseases. 

Innovations in ultrasound and endoscopy equipment

In the field of ultrasonography and endoscopy, improvements in technology have led to the creation of more compact and portable devicesThe new system allows diagnostics to be performed in less conventional settings such as rural clinics or remote offices. The ultrasound systems are capable of providing high-resolution images that are essential for the diagnosis of musculoskeletal and cardiac conditions. At the same time, innovations in endoscopy are improving the ability of visualize and treat gastrointestinal conditions with less discomfort for the patient. 

Telemedicine

Telemedicine is another area that has shown great growth. It allows physicians to have remote consultations with patients and can monitor their health. Through the use of portable devicesspecialists will be able to perform diagnostics patients without having to be in person at a medical center. This is a major breakthrough, since facilitates access to medicine to more people, especially those living in less populated and remote regions.

Advances in high-field magnetic resonance imaging in veterinary medicine

High-field magnetic resonance imaging is revolutionizing veterinary diagnostics, particularly in small and exotic animals. This technology makes it possible to obtain detailed soft tissue imagingThis is crucial for neurological and soft-tissue diagnostics. With stronger magnetic fields, veterinarians are able to detect anomalies more accurately and quicklyThis will facilitate more accurate diagnoses and more effective treatments.   

Use of fluoroscopy in interventional veterinary procedures

Fluoroscopy is an imaging technique that makes it possible to view internal structures in motion in real time, being especially useful in veterinary interventional procedures. This technology is used to guide procedures such as stent placement, biopsies and minimally invasive surgeries. By providing continuous visualization, fluoroscopy helps to increase the precision of procedures and reduces the risks associated with surgical procedures. 

As we can see, there are more and more advances in the field of medical technology that help to make diagnoses and interventions faster, more effective and less risky for patients.

Kiko Ramos

CEO of 4D Médica. Expert in marketing and distribution of medical equipment.