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Artificial Intelligence and Medicine: Advances and Challenges

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18 September 2023

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20 September 2023

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Abstract
Artificial Intelligence (AI) has emerged as a disruptive technology in various areas, and medicine is no exception. In the last decade, there has been rapid progress in the application of AI in healthcare, from diagnosing and prognosticating diseases to improving operational efficiency in hospitals. This literature review explores the advances of artificial intelligence in the field of medicine, discusses its benefits, and discusses the ethical and regulatory challenges that need to be addressed to maximize its potential.
Keywords: 
Subject: Social Sciences  -   Education

1. Introduction

Since time immemorial, the search for solutions to diseases has been a fundamental pillar of humanity, medicine has advanced exponentially, and in recent years, AI has burst into this field as a knowledge accelerator and an ally for Health professionals. Currently, the panorama is fascinating, since AI has demonstrated the ability to process vast volumes of medical information and learn complex patterns that could have gone unnoticed by the human eye (Avila, Mayer & Quesada, 2020).
Since the advent of AI, medicine has undergone a significant evolution; machine learning algorithms backed by the power of data processing in the cloud have opened the door to more accurate and earlier diagnoses; Medical images such as X-rays, CT scans and MRIs can be analyzed with astonishing precision and speed, making it possible to detect pathologies even in their most incipient stages. Early detection not only improves the effectiveness of treatments, but can also save lives and reduce costs associated with late and complex treatments (Ghorishi et al., 2023; Pantelidis et al., 2023; Sanabria et al., 2023).
In addition, AI has enabled the development of precision medicine, an approach that takes into account the unique characteristics of each patient and their genetic predispositions, analyzing an individual’s DNA and, based on this information, recommending personalized treatments that are more effective and with fewer side effects. This has revolutionized the treatment of chronic diseases and cancers, bringing hope to patients who previously faced grim prognoses (Goldmann et al., 2023; Moreno & Vida, 2022).
AI has also shown a significant impact in predictive medicine, as by analyzing large data sets, such as electronic medical records and data collected from wearable devices, the development of diseases and health risks can be anticipated over time. weather. This early prediction capacity not only allows for more proactive medical care, but also allows health authorities to design preventive policies and wellness programs more effectively (Loaiza, 2021).
However, this fascinating relationship between AI and medicine is not without its challenges, the security and privacy of patient data being among the top ethical concerns as AI takes over healthcare, ensuring that patient data is protected becomes essential; health professionals and technology experts must work together to establish strong cybersecurity measures and ensure that the confidentiality of data is kept intact (Bermúdez & Jiménez, 2022; Waisbert et al., 2023).
Artificial intelligence and medicine are converging to form a fascinating and promising future. AI has proven to be a powerful tool to improve the diagnosis, treatment and prediction of diseases. However, as we move towards this new healthcare paradigm, we must address ethical and regulatory challenges with responsibility and commitment (Waldo & Germán, 2020). As this is achieved, the potential of AI to deliver more accurate, accessible and patient-centered healthcare can be fully harnessed, radically transforming the field of medicine and improving the quality of life for humanity (Teigens & Mikelsten, 2020; Yoo, 2023).

2. Methodology

In this research, the method used has focused on a documentary analysis (Arias, 2012). This author comments that this process is based on the search, compilation, analysis, criticism and interpretation of secondary information, obtained and compiled by other researchers in different documentary sources. The most important thing in this study is to relate the data that exists for a certain construct, generating a panoramic vision that allows adequate solutions to the problem posed (Parsons et al., 2023; Rivera et al., 2018).
In accordance with the above, the article addresses concepts related to artificial intelligence related to medicine, explores the advances of AI in the medical field, analyzes its benefits and discusses the ethical and regulatory challenges that must be addressed to maximize its effectiveness. potential. In this review article, it can be highlighted that a rigorous selection of works available in the Google Scholar, Redalyc, Dialnet, Web of Science and Scopus databases, published during the periods 2019-2023, was carried out. As a starting point and following the logical search order, the terms "artificial intelligence" and "medicine" were used, filtering them with commas and asterisks, obtained in Google Scholar 16200 search results, in Redalyc 258972, Dialnet 263, Web of Science 100 and Scopus 1830.

3. Results

Derived from the bibliographic review, it was possible to determine that Artificial Intelligence (AI) has emerged as a fundamental pillar in the technological revolution of the 21st century, with impressive applications in various areas of society, medicine being one of its most promising fields and transformers, where the synergy between artificial intelligence and medical practice has opened up a horizon of unprecedented possibilities (Vidal & Vidal, 2022). In addition to the above, this review allows us to explore the world of AI applied to medicine, in different aspects such as most outstanding achievements, benefits and challenges, as well as reflect on the potential that these models have to improve medical care and transform lives. of millions of people around the world.

4. Accurate Diagnosis and Prognosis in Medicine: The Impact of Artificial Intelligence

AI has not only excelled in diagnosis, but has also enabled a more accurate and personalized prognosis for patients by analyzing large data sets, including medical history, pre-existing conditions, and genetic risk factors. Can predict the prognosis of an individual patient, making this information invaluable to physicians, as it allows them to make more informed decisions about treatment and design care plans tailored to the unique needs of each individual (Hogg et al., 2023; Lüthy, 2022).
For example, early diagnosis is a crucial factor for the effective treatment of diseases, and in this regard, AI has proven to be an invaluable tool since it can analyze large amounts of patient data, from electronic medical records to medical images, to identify patterns subtle that could go unnoticed by health professionals, greatly supporting the identification of diseases such as cancer, cardiovascular diseases and neurological disorders (Tucci, Saary & Doyle, 2022; Vega, Mora & Badilla, 2020).
Even diseases can present symptoms and complex clinical characteristics, which makes their accurate diagnosis difficult, however, AI has the ability to analyze multiple variables and factors that can influence the appearance of a disease and detect complex patterns in the data; This holistic analysis capacity allows a more accurate identification of diseases and, in some cases, even the detection of unknown or rare diseases (Gamero & Chamorro, 2021).
Personalized prognosis has also been especially valuable in the care of patients with chronic diseases, where AI analyzes long-term follow-up data and predicts how the disease may progress over time, enabling proactive and effective disease management. to improve the quality of life of patients (Morandin, Romero & Villanueva, 2023).
The diagnosis and prognosis in medicine has been radically transformed by AI, improving the precision, speed and personalization of medical care; its ability to identify complex patterns, predict outcomes, and reduce medical errors has proven to be an invaluable asset to healthcare professionals and, ultimately, to patients. However, the path towards a broader adoption of AI in medicine implies addressing ethical and technical challenges, which will ensure that its use is safe, reliable and beneficial to society as a whole (Santeliz, 2023).
Treatment Personalization: the potential of artificial intelligence in medicine.
Precision medicine has become a reality thanks to AI, since by analyzing a patient’s DNA relevant genetic markers can be identified, and thereby determine which treatments will be most effective for a particular individual, improving the results obtained. and reducing side effects and costs associated with ineffective treatments (Galdames, 2023).
Medicine has evolved from a generalized approach to a more detailed and personalized understanding of health and disease; The increasing availability of medical data and advances in Artificial Intelligence have allowed the integration of individualized information to develop personalized treatments, through an approach that considers the genetic, environmental and lifestyle characteristics of a patient to design specific treatments and effective (Herrán, 2022).
As in the case of oncology, which is one of the areas of medicine that has been most transformed by AI, since data from cancer patients are analyzed and different treatments are obtained by identifying biomarkers and patterns, which indicate which therapies are more appropriate for specific patients through more effective, less invasive treatments and with fewer side effects, which significantly improves the quality of life of patients in their fight against cancer (Alcañiz et al., 2020).
The future of AI treatment personalization is exciting; As technology continues to advance, we are likely to see more integration of data from multiple sources, such as the addition of data from wearables and other real-time monitoring devices. This will allow for more continuous and proactive medical care, which will improve treatment outcomes and reduce unnecessary hospitalizations (Medinaceli & Silva, 2021).
Artificial Intelligence has transformed medicine in many aspects, and treatment personalization is one of the fields that has experienced the most significant changes, from precision medicine to personalized drug therapies, AI has opened a new era in medical care, bringing hope to patients with rare and chronic diseases, however, it is essential to address ethical and technical challenges to ensure that this revolutionary technology is used responsibly and for the benefit of humanity (Joison et al., 2021).

5. Predictive Medicine: The Future Powered by Artificial Intelligence

In relation to preventive medicine, AI has the potential to revolutionize this concept, since data from multiple sources, such as electronic medical records and wearable devices, can be analyzed in order to anticipate the development of diseases before symptoms appear; this will allow doctors to take preventive measures and provide more proactive care to patients (Ruiz & Velásquez, 2023).
Predictive medicine is a field in constant evolution that uses artificial intelligence (AI) as a fundamental tool to anticipate and foresee the development of diseases and health risks in individuals and populations, one of the greatest achievements in this field being the detection of diseases in their early stages, generating a more timely intervention by doctors, which allows them to provide preventive treatments that significantly improve the prognosis and quality of life of the patient (Ávila, Mayer & Quesada, 2021).
Predictive medicine with AI not only focuses on the early detection of diseases, but also on the identification of personalized risk factors for health through data analysis, allowing the prediction of risk of developing certain diseases or health complications in people, giving guidelines for health professionals to implement specific preventive interventions that provide more proactive and personalized medical care. During the COVID-19 pandemic, AI was instrumental in modeling and predicting the spread of the virus, allowing for a faster and more effective response by health systems and governments (Márquez, 2020).
Predictive medicine and precision medicine are closely linked, as both rely on personalized data analysis to improve health outcomes; AI has enabled the integration of these two approaches, leading to a more holistic and personalized approach to healthcare (Mazarico, 2022). AI-powered predictive medicine is transforming healthcare, providing proactive and preventative insight into the health of individuals and populations, ranging from early detection of disease to identification of personalized risk factors, demonstrated by this its ability to improve health outcomes and reduce the burden of disease (Nunes, Guimarães & Dadalto, 2022).

6. Improving Operational Efficiency: The Potential of Artificial Intelligence

The usefulness of AI in the optimization of hospital processes and resource management has been of great help to increase efficiency and reduce costs, since appointments can be scheduled, medication inventory managed, as well as predict the demand for services. physicians thereby improving operational efficiency in the healthcare industry. AI helps identify areas for improvement in hospital management, such as reducing waiting times, optimizing supply logistics and improving administrative processes, allowing hospitals to provide faster and more effective care to patients. patients and increase user satisfaction (Reyes, 2023).
AI has simplified and streamlined medical appointment scheduling, improving the patient experience and reducing the administrative burden for medical staff, as well as sending automatic reminders to patients, thereby reducing no-show rates and improving efficiency in appointment scheduling. Regarding the medication administration process, the AI has improved it, avoiding medication errors and specifying their dosage, using the analysis of the medical history and the patient’s conditions, to recommend the medication and the appropriate doses according to the treatment. established by health professionals (Pimienta & Mosquera, 2021).
Artificial Intelligence has proven to be a powerful tool for improving operational efficiency in the medical field, from hospital management to scheduling medical appointments and medication administration, AI has transformed the way healthcare is delivered. and has optimized the use of resources and costs, positioning itself as a fundamental tool for the future of operational efficiency in medicine, offering unprecedented potential to improve medical care and improve the health and well-being of society as a whole (Del Bull & Alfonso, 2023).

7. Privacy and Data Security in Artificial Intelligence and Medicine

In this vein, health organizations must ensure that strong measures are implemented to protect confidential data and prevent unauthorized access, this being one of the main challenges in the implementation of AI in medicine, since it is often required to analyze large data. amounts of personal and medical data including sensitive information such as medical histories, diagnoses, treatments and medical results (Galdames, 2023).
One viable option is to use the technique of data anonymization to protect privacy, since removing personally identifiable information from the data used prevents third parties from identifying individual patients; however, anonymization is not always enough, since advanced re-identification techniques can reveal the identity of patients, making it necessary to implement additional measures, such as data encryption and restricted access, to strengthen privacy; this includes the use of firewall systems, user authentication, constant surveillance, and training for staff on the importance of data security (Medinaceli & Silva, 2021).
It is essential that medical AI development teams strive to ensure that the data used is representative of various populations and demographics, and it is important to perform regular testing and validation to detect and correct potential biases; The implementation of AI in medicine also poses challenges in terms of accountability and transparency, since as it becomes more complex and sophisticated, the results and decisions can be difficult to interpret and justify, which can lead to mistrust among medical professionals. health and patients, since they do not understand how certain conclusions are reached (Mina, 2020).
The implementation of AI in medicine must comply with the specific rules and regulations of each country and region, privacy and data protection laws, such as the General Data Protection Regulation (GDPR) in the European Union, establish strict guidelines on the handling of personal and medical data, being the responsibility of health institutions and AI developers to ensure that their systems comply with applicable rules and regulations, this implies ensuring that informed consent is obtained from patients to use their data, as well as implement adequate security and privacy measures to protect confidential information (Juca, 2023).
Collaboration between AI developers, healthcare professionals, healthcare institutions and regulators is essential to establish strong ethical and legal frameworks that protect the privacy of patients and ensure the mutual benefit of AI and medicine, through an approach responsible and conscious so that it continues to be a valuable tool to improve medical care, as well as the health and well-being of humanity (Basáez & Mora, 2022).

8. Responsibility and Transparency in the Application of Artificial Intelligence in Medicine

AI in medicine can provide recommendations and make clinical decisions based on the analysis of large data sets; however, it is critical to understand that the ultimate responsibility rests with healthcare professionals as decision-making responsibility also involves making sure that AI algorithms are developed ethically and consider patients’ values and preferences, so it can be used as a support tool for doctors, instead of replacing their clinical judgment and experience (Galdames, 2023).
Transparency is crucial to build trust and allow healthcare professionals and patients to understand the reasoning behind AI recommendations, so medical AI researchers and developers should strive to improve the explainability of models. and provide tools to visualize and understand how certain decisions are reached, allowing health professionals to carry out an adequate review and make informed decisions based on the results obtained (Paz, 2023).
It is essential that the collection and use of data is carried out in an ethical manner and respectful of the privacy of patients, it must be based on a rigorous and continuous evaluation of results and effectiveness, so AI models must be tested and validated in different clinical settings to ensure they are accurate and reliable, with communication and education being key components in fostering these points. AI education should also focus on how healthcare professionals can work collaboratively with technology to deliver more effective and personalized healthcare, as well as provide information to patients about how their data is used and what their rights are. in relation to privacy and information security (Paladino, 2023).
The ultimate responsibility for decision-making remains with physicians, and AI should be seen as a support tool in clinical practice, rather than a substitute, with responsible and conscious implementation, with the tendency to significantly improve clinical practice. quality and efficiency of medical care, providing tangible benefits for both health professionals and patients. Continued collaboration and a focus on ethics and transparency will ensure that AI in medicine is used responsibly and ethically, providing lasting benefits for the health and well-being of humanity (Ramón, 2021).

9. Cognitive Biases and Equity in Artificial Intelligence and Medicine

The bias in the training data is one of the main challenges in the application of AI in medicine, since these models are based on large data sets to learn patterns and make decisions. For example, if the data used to train a diagnostic model is comprised primarily of patients of a specific ethnicity or gender, the model may not be accurate in diagnosing diseases in other populations, which can lead to inequities in diagnosis and treatment, negatively affecting certain groups of patients (Oldema & Ibánez, 2022).
Equity in the accessibility and use of AI in medicine is another major challenge, as although AI has the potential to improve healthcare and reduce health inequalities, its uneven implementation can create new gaps in access to it. attention. In some cases, well-resourced healthcare institutions may have more access to advanced AI technologies, resulting in better care for their patients, while resource-constrained communities may be left behind and face barriers in accessing information. technology, which perpetuates existing inequalities in medical care (Herrán, 2022).
Equity is also a critical aspect in medical AI research and development; the lack of diversity in research and development teams can lead to implicit biases in the conception and design of models, so it is essential to encourage diversity in research teams, including medical experts, data scientists and representatives of various populations, to ensure that these models are fair and accurate for all patients (Basáez & Mora, 2022).
Bias and fairness are fundamental challenges in the application of Artificial Intelligence in medicine. The bias in the training data can lead to biased and inequitable results, which negatively affects certain groups of patients, in terms of equity in the accessibility and use of AI in medicine is also critical to avoid the creation of new inequalities in medical care (Macchiavelli, 2021).
It is essential that AI developers, healthcare professionals, regulators and society as a whole work together to address these challenges and ensure fair and ethical implementation, with a conscious focus on responsibility and fairness so that AI becomes a powerful tool to improve the quality and accessibility of medical care, offering significant benefits for the health and well-being of humanity (Rojas, 2022).

10. Clinical Validation in Artificial Intelligence and Medicine

Clinical validation is a critical step in the development of AI applications in medicine, it allows evaluating whether AI models can generalize their results to new data and clinical situations; It is essential that the models are validated in representative populations and in different clinical settings to ensure their accuracy and reliability, also helping to identify possible biases and limitations that may negatively affect certain groups of patients (Gruson, 2021).
Medicine is a complex and multifaceted discipline, which can lead to different clinical outcomes even for patients with similar conditions, so clinical validation must consider this variability and provide a robust and realistic assessment of the models’ accuracy. To address the challenges in the clinical validation of AI in medicine, it is important to follow best practices and rigorous approaches, some of these practices include (Rico, 2021):
  • Selection of Representative Data Sets: Data sets used for clinical validation should be representative of the target population and reflect a variety of clinical settings. The diversity in the data will allow for a more accurate and generalizable assessment of AI models.
  • Splitting Data for Training and Validation: It is important to split the data into separate sets for training and validation. This allows you to evaluate how the model behaves on data that you haven’t seen during training, which helps measure its generalizability.
  • Cross Validation and Bootstrap: Cross validation and the Bootstrap method are techniques that allow multiple validation iterations to be performed to obtain more accurate estimates of model performance. These approaches help mitigate the impact of variability in the validation data.
  • Assessment of Bias and Equity: Clinical validation should include a detailed assessment of potential bias in the results of the AI model. It is essential to ensure that the algorithms are fair and accurate for all populations, avoiding biases that may negatively affect certain groups of patients.
  • Testing in Real World Settings: AI models must be tested in real world clinical settings to ensure their applicability and performance in real healthcare situations. Pilot testing and implementation in clinical settings are crucial to validate the usefulness and effectiveness of AI in practice.
Combining medical knowledge with expertise in data science and machine learning is essential to ensure rigorous validation and successful implementation of AI in clinical practice. In addition, collaboration between health institutions and regulatory entities is also important to establish standards and guidelines for the validation and responsible use of AI in medicine. Clinical validation is a critical step in the development and application of Artificial Intelligence in medicine, however, AI can become a powerful tool to improve the quality and efficiency of medical care, providing significant benefits for the health and well-being of society as a whole through proper clinical validation and an ethical and responsible approach (Biggs et al., 2022; Cascella et al., 2023).

11. Discussion

The convergence between artificial intelligence (AI) and medicine has been the subject of an exciting debate in the academic and medical community, with a number of authors providing their perspectives on the advances and challenges associated with this intersection. A prominent author in this discussion is Topol (2019), who in his work "Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again" explores how AI can improve healthcare and return the human approach to the center of medicine, highlighting how AI can free clinicians from routine and repetitive tasks, allowing them to focus on patient-physician interaction and making more informed clinical decisions.
In an article published in the journal Clinical Research, Li et al. (2019) discuss the role of AI in the interpretation of medical images, noting that AI has shown comparable or even superior performance to humans in detecting certain pathologies in images, which could speed up diagnoses and improve efficiency in medical imaging. healthcare, however, also caution about the need for rigorous validation and joint interpretation of results by clinicians and algorithms.
According to Davenport and Kalakota (2019), AI has demonstrated its potential in improving accurate diagnoses through the analysis of large sets of medical data; this is in line with the vision of Pastén et al. (2019), who explore how AI algorithms can match and even surpass physicians’ skills in interpreting medical images, such as X-rays and mammograms. Additionally, Hughes et al. (2020) discuss how interpretation of AI algorithm results can be complex and clinician confidence in algorithmic recommendations can vary.
The debate on ethics and privacy in the implementation of AI in medicine is addressed by Murphy et al (2021) in their article "Artificial intelligence for good health: a scoping review of the ethics literature"; the authors explore how AI can deliver benefits for medical decision-making, but also raise questions about informed consent, data ownership, and liability for algorithmic errors.
Regulatory challenges and the need for a strong regulatory framework are addressed by Obermeyer and Emanuel (2016) in their paper "Predicting the Future — Big Data, Machine Learning, and Clinical Medicine", they argue that while AI has the potential to To revolutionize medicine, it is essential to establish regulations that guarantee the safety and effectiveness of these technologies, avoiding unnecessary risks and ensuring that patients benefit adequately.
Regarding equity in access to AI-powered healthcare, Chen et al. (2023) in their article "Human-centered design to address biases in artificial intelligence" discuss how AI algorithms can perpetuate biases and inequalities if not carefully designed and trained. The authors stress the importance of identifying and addressing data biases to ensure that the implementation of AI in medicine is equitable and beneficial to all patient groups, while in terms of regulatory challenges Levenstein et al. (2020) warn about the need to establish clear policies and regulations for the use of AI in medicine.
The existing literature reflects a consensus around the transformative potential of artificial intelligence in the field of medicine, while highlighting the ethical, regulatory and equity challenges that must be addressed proactively, so the discussion around the AI and medicine is a constant reminder of the need to find a balance between technological innovation and the preservation of fundamental human values in healthcare.

12. Conclusions

Artificial intelligence offers great potential to transform medicine and improve people’s quality of life; Advances in diagnostics, personalized treatment, and predictive medicine are just the beginning of what this technology can achieve. However, it is essential to address the ethical and regulatory challenges associated with the implementation of AI in medicine, in order to fully reap the benefits of AI in the field of health, providing more precise, efficient and patient-focused medical care. the patient.
AI has demonstrated its ability to analyze large volumes of medical data and extract complex patterns and relationships that humans may miss, thereby generating more accurate and faster diagnoses, leading to earlier and more efficient medical care. effective. In addition, AI has allowed the identification of personalized treatments adapted to the needs of each patient, thus improving the effectiveness of therapeutic plans.
However, the path towards the full and successful implementation of AI in medicine is not without challenges, such as data privacy and security, as well as responsibility and transparency in its use, being critical aspects that must be addressed. approached cautiously and ethically; finding that clinical validation is also positioned as a fundamental pillar to guarantee that these models are accurate, reliable and equitable in medical care. It is essential that technological advances in AI go hand in hand with ethics and responsibility, ensuring that patients and healthcare professionals trust the technology and feel secure in its implementation; In addition, multidisciplinary collaboration between clinicians, data scientists, engineers, and regulators is essential to drive responsible and effective implementation.
Artificial intelligence will not replace healthcare professionals, but will become a powerful ally in improving healthcare and addressing healthcare challenges more effectively with the right approach, to help clinicians make more informed decisions. and accurate, as well as to optimize the use of resources in hospitals, and to provide a more patient-centred approach.
In conclusion, AI and medicine are destined to form a fruitful and revolutionary partnership, as this synergy continues to be advanced, it is imperative to maintain a balance between the potential of AI to improve healthcare and ethical and humane values. that guide medical practice, for if challenges are properly addressed and responsible and ethical collaboration fostered, AI in medicine has the power to transform the health and well-being of humanity in unprecedented ways. Derived from the above, projects related to the application of AI techniques in medical diagnosis, disease prediction and personalization of treatments can be addressed, as well as an analysis of how neural networks and deep learning can improve the early precision of pathologies such as cancer and heart disease thereby helping to maximize the benefit potential of modern medical care.

References

  1. Alcañiz, M., Chicchi Giglioli, I. A., Sirera, M., Minissi, E., & Abad, L. (2020). Biomarkers of autism spectrum disorder based on biosignals, virtual reality and artificial intelligence. Medicine (Buenos Aires), 80, 31-36.
  2. Arias, F.G. (2012). The research project. Introduction to scientific methodology. 6th. Phidias G. Arias Odón.
  3. Artavia-Díaz, K. Y., & Alejandra, C. G. (2021). ARTIFICIAL INTELLIGENCE: DIGITAL TRANSFORMATION AND INNOVATION IN DISTANCE EDUCATION. ANALYSIS OF THE UNED, COSTA RICA. REFCalE: Electronic Magazine Training and Educational Quality. ISSN 1390-9010, 9(3), 1-15.
  4. Avila-Tomás, J.F., Mayer-Pujadas, M.A., & Quesada-Varela, V.J. (2020). Artificial intelligence and its applications in medicine I: introduction, background to AI and robotics. Primary Care, 52(10), 778-784. [CrossRef]
  5. Basáez, E., & Mora, J. (2022). Health and artificial intelligence: how have we evolved? Las Condes Clinic Medical Journal, 33(6), 556-561.
  6. Bermúdez-Tamayo, C., & Jiménez-Pernet, J. (2022). Artificial intelligence for the advancement of health systems. Possible contributions and challenges. Social Security Law Review, Laborum, 401-414.
  7. Biggs, D., Vargas, M., Larraín, T., Alvear, A., & Pedemonte, J.C. (2022). Artificial intelligence in medicine: Selection of methods, applications and considerations (Part II). Rev. Chil. Anest, 51(5), 535-542.
  8. Cascella, M., Montomoli, J., Bellini, V., Ottaiano, A., Santorsola, M., Perri, F., ... & Bignami, E. G. (2023). Writing the paper “Unveiling artificial intelligence: an insight into ethics and applications in anesthesia” implementing the large language model ChatGPT: a qualitative study. Journal of Medical Artificial Intelligence, 6. [CrossRef]
  9. Cevallos-Culqui, A., Pons, C., & Rodriguez, G. (2023). Semi-supervised learning models for document classification: A systematic review and meta-analysis. Inteligencia Artificial, 26(72), 81-111. [CrossRef]
  10. Chen, Y., Clayton, E.W., Novak, L.L., Anders, S., & Malin, B. (2023). Human-centered design to address biases in artificial intelligence. Journal of Medical Internet Research, 25, e43251. [CrossRef]
  11. Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94. [CrossRef]
  12. Del Toro Reyes, L., & Alfonso, J.E.L. (2023). Artificial intelligence and human resource management. GADE: Scientific Magazine, 3(4), 289-298.
  13. Esteban, P. G., & del Puerto, D. A. (2022). Artificial Intelligence as an educational resource during initial teacher training. IRIED. Ibero-American Journal of Distance Education, 25(2), 347-358.
  14. Flores, A., Tito-Chura, H., & Zea-Rospigliosi, L. (2023). Prediction of Research Project Execution using Data Augmentation and Deep Learning. Inteligencia Artificial, 26(71), 46-58. [CrossRef]
  15. Galdames, I.S. (2023). Artificial intelligence in Human Medicine. International Journal of Medical and Surgical Sciences, 10(1), 1-4.
  16. Gamero, A.M., & Chamorro, M.R. (2021). Artificial intelligence in the control of COVID-19. Primary Care, 53(10).
  17. Ghorishi, A. R., Ogunfuwa, F. O., Ghaddar, T. M., Kandah, M. N., Smith, B. W., Ta, Q., ... & Amundson, P. K. (2023). Narrative review of open source, proprietary, and experimental artificial intelligence algorithms in radiology. Journal of Medical Artificial Intelligence, 6. [CrossRef]
  18. Goldmann, N., Skalicky, S. E., Weinreb, R. N., Guedes, R. A. P., Baudouin, C., Zhang, X., ... & Goldberg, I. (2023). Defining functional requirements for a patient-centric computerized glaucoma treatment and care ecosystem. Journal of Medical Artificial Intelligence, 6. [CrossRef]
  19. Gómez, W. O. A. (2023). Artificial Intelligence and its Impact on Education: Transforming Learning for the 21st Century. International Journal of Pedagogy and Educational Innovation, 3(2), 217-229.
  20. González, L. A. O., Baren, C. Y. O., & Zapata, E. J. P. (2023). The impact of artificial intelligence in the educational field. FIPCAEC Scientific Magazine (Promotion of multidisciplinary scientific-technical research and publication). ISSN: 2588-090X. Pole of Training, Research and Publication (POCAIP), 8(3), 342-354.
  21. Grusson, D. (2021). Big Data, artificial intelligence and laboratory medicine: the hour of integration. Advances in Laboratory Medicine/Avances en Medicina de Laboratorio, 2(1), 5-7.
  22. Herrán Ortiz, A. I. (2022). Artificial intelligence, health and human rights: towards a government of algorithms?. Artificial intelligence, health and human rights: towards a government of algorithms?, 297-335.
  23. Hogg, H. D. J., Sendak, M. P., Denniston, A. K., Keane, P. A., & Maniatopoulos, G. (2023). Unlocking the potential of qualitative research for the implementation of artificial intelligence-enabled healthcare. Journal of Medical Artificial Intelligence, 6. [CrossRef]
  24. Joison, A. N., Barcudi, R. J., Majul, E. A., Ruffino, S. A., De Mateo Rey, J. J., Joison, A. M., & Baiardi, G. (2021). Artificial intelligence in medical education and health prediction. Research Method Applied to the Biological Sciences, 6(1).
  25. Juca-Maldonado, F. (2023). The impact of artificial intelligence on academic and research work. Metropolitan Journal of Applied Sciences, 6(S1), 289-296.
  26. Levenstein, D., Alvarez, V. A., Amarasingham, A., Azab, H., Gerkin, R. C., Hasenstaub, A., & Iyer, R. (2020). On the Role of Theory and Modeling in Neuroscience. arXiv. arXiv preprint arXiv:2003.13825. [CrossRef]
  27. Li, Y., Ma, W., & Zhao, Y. (2019). Application of Digital Image Processing Technology Based on Artificial Intelligence in the Analysis of Medical Images. Clinical Investigation, 60(6), 1548-1561.
  28. Loaiza-Bonilla, A. (2021). Artificial intelligence in oncology: current context and a vision for the next decade. Medicine, 43(4), 527-534.
  29. Luthy, I.A. (2022). Artificial intelligence and machine learning in cancer diagnosis and treatment. Medicine (Buenos Aires), 82(5), 798-800.
  30. Macchiavelli, N. (2021). Gender perspective in new technologies. The problem of biases. Law and Technology Supplement Journal, (84).
  31. Marquez Diaz, J. (2020). Artificial intelligence and Big Data as solutions against COVID-19. Bioethics and Law Magazine, (50), 315-331.
  32. Mazarico, L.C. (2022). Artificial intelligence as a transversal science: the role of the Artificial Intelligence Research Institute. Multidisciplinary meetings, 24(72), 5.
  33. Medinaceli Díaz, K.I., & Silva Choque, M.M. (2021). Impact and regulation of Artificial Intelligence in the health field. IUS Magazine, 15(48), 77-113.
  34. Mejías, M., Coronado, Y. C. G., & Peralta, A. L. J. (2022). Artificial intelligence in the field of nursing. Implications in assistance, administration and education. Health, Science and Technology, 2, 88-88.
  35. Mina, A. (2020). Big data and artificial intelligence in the future management of patients. Where to start? At what point are we? Quo tendimus? Advances in Laboratory Medicine/Avances en Medicina de Laboratorio, 1(3), 20200052.
  36. Morandin-Ahuerma, F., Romero-Fernández, A., & Villanueva-Méndez, L. (2023). Artificial intelligence applied to health: guarded prognosis.
  37. Moreno, I.M., & Vida, M.N.M. (2022). The e-health. Towards 5P medicine: personalized, precise, preventive, predictive and participatory medicine. Social Security Law Review, Laborum, 415-443.
  38. Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D.J., Malhotra, N., Cai, J.C., ... & Gibson, J. (2021). Artificial intelligence for good health: a scoping review of the ethics literature. BMC medical ethics, 22(1), 1-17. [CrossRef]
  39. Nunes, H. D. C., Guimarães, R. M. C., & Dadalto, L. (2022). Bioethical challenges of the use of artificial intelligence in hospitals. Bioethics Magazine, 30, 82-93. [CrossRef]
  40. 40. Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine (2016). N Eng J Med; 375(13):1216-9. doi: 10.1056/NEJMp1606181. PMID: 27682033; PMCID: PMC5070532. [CrossRef]
  41. Olmeda, M.V., & Ibánez, J.C. (2022). Manual of applied ethics in Artificial Intelligence. Anaya Multimedia.
  42. Paladino, M.S. (2023). Artificial Intelligence in Medicine. Ethical reflections from the thought of Edmund Pellegrino. Square bioeth, 25-35. [CrossRef]
  43. Pantelidis, P., Bampa, M., Oikonomou, E., & Papapetrou, P. (2023). Machine learning models for automated interpretation of 12-lead electrocardiographic signals: a narrative review of techniques, challenges, achievements and clinical relevance. Journal of Medical Artificial Intelligence, 6. [CrossRef]
  44. Parsons, O., Barlow, N. E., Baxter, J., Paraschin, K., Derix, A., Hein, P., & Dürichen, R. (2023). Enabling scalable clinical interpretation of machine learning (ML)-based phenotypes using real world data. Journal of Medical Artificial Intelligence, 6. [CrossRef]
  45. Pastén-Zapata, A. E., González-Habib, R., Hernández-Salazar, J. A., & Gómez-Torres, P. C. (2019). Expression of immunohistochemical markers in surgical pathology of breast cancer in northern Mexico. Gynecology and Obstetrics of Mexico, 87(11), 734-739.
  46. Peace, C. (2023). Artificial intelligence in general medicine and genomics. Metro Science, 30(2), 81-86.
  47. Pimienta, S. X., & Mosquera-Martínez, M. L. (2021). Curricular, technological and pedagogical considerations for the transition to the new educational model in the field of health supported by artificial intelligence (AI). Medicine, 43(4), 540-554.
  48. Ramón Fernández, F. (2021). Artificial intelligence in the doctor-patient relationship: Some issues and proposals for improvement. Chilean Law and Technology Magazine, 10(1), 329-351.
  49. Reyes, N.S. (2023). Use of artificial intelligence in personalizing the user experience on digital platforms. Pole of Knowledge, 8(6), 1190-1206.
  50. Rico-Carrillo, A.E. (2021). Support tools for clinical reasoning in internal medicine based on artificial intelligence. Medicine, 43(4), 555-569.
  51. Rivera Porras, D. A., Carrillo Sierra, S. M., Forgiony Santos, J. O., Nuván Hurtado, I. L., & Rozo Sánchez, A. C. (2018). Organizational culture, challenges and challenges for healthy organizations.
  52. Rojas-Gualdron, D.F. (2022). Should the evaluation of health technologies based on artificial intelligence be different? CES Public Health and Epidemiology Magazine, 1(1), 53-58.
  53. Ruiz, R. B., & Velásquez, J. D. (2023). Artificial intelligence at the service of the health of the future. Las Condes Clinic Medical Journal, 34(1), 84-91.
  54. Sajiv, G., & Ramkumar, G. (2022, July). Multiple Class Breast Cancer Detection Method Based on Deep Learning and MIRRCNN Model. In 2022 International Conference on Inventive Computation Technologies (ICICT) (pp. 981-987). IEEE. [CrossRef]
  55. Sanabria-Navarro, J. R., Silveira-Pérez, Y., Pérez-Bravo, D. D., & de-Jesús-Cortina-Núñez, M. (2023). Incidences of artificial intelligence in contemporary education. Communicate: Scientific Journal of Communication and Education, 31(77). [CrossRef]
  56. Sánchez, J. L. G., Garcia, F. R. V., Parra, A. E. M., Calva, S. W. G., & Arévalo, B. M. B. (2023). Application of Artificial Intelligence in Higher Education. Domino of Sciences, 9(3), 1097-1108.
  57. Santeliz, J. (2023). Is artificial intelligence the way to change the future of medicine? Postgraduate Medical Bulletin, 39(2), 6-7.
  58. Teigens, V., Skalfist, P., & Mikelsten, D. (2020). Artificial intelligence: the fourth industrial revolution. Cambridge Stanford Books.
  59. Topol, E. (2019). Deep medicine: how artificial intelligence can make healthcare human again. Hachette UK.
  60. Tucci, V., Saary, J., & Doyle, T. E. (2022). Factors influencing trust in medical artificial intelligence for healthcare professionals: A narrative review. Journal of Medical Artificial Intelligence, 5. [CrossRef]
  61. Vega, M. Á., Mora, L. M. Q., & Badilla, M. V. C. (2020). Artificial intelligence and machine learning in medicine. Synergy Medical Journal, 5(8), e557-e557.
  62. Vicente-Yagüe-Jara, M. I., López-Martínez, O., Navarro-Navarro, V., & Cuéllar-Santiago, F. (2023). Writing, creativity and artificial intelligence. ChatGPT in the university context. Communicate: Scientific Journal of Communication and Education, 31(77), 47-57.
  63. Vidal, J.R., & Vidal, O.R. (2022). Applications of artificial intelligence in medicine. Peruvian Journal of Health Research, 6(3), 131-133.
  64. Vidal Ledo, M. J., Madruga González, A., & Valdés Santiago, D. (2019). Artificial intelligence in medical teaching. Higher Medical Education, 33(3).
  65. Vinodhini, M., Rajkumar, S., Reddy, M. V. K., & Janesh, V. (2023). Detection of Post COVID-Pneumonia Using Histogram Equalization, CLAHE Deep Learning Techniques: Deep Learning. Inteligencia Artificial, 26(72), 137-145. [CrossRef]
  66. Waldow, V. R., & Gérman-Bés, C. (2020). Advanced Technologies and Artificial Intelligence: reflection on development, trends and implications for Nursing. Nursing Index, 29(3), 142-146.
  67. Waisberg, E., Ong, J., Kamran, S. A., Masalkhi, M., Zaman, N., Sarker, P., ... & Tavakkoli, A. (2023). Bridging artificial intelligence in medicine with generative pre-trained transformer (GPT) technology. Journal of Medical Artificial Intelligence, 6. [CrossRef]
  68. Yoo, T. K. (2023). Actions are needed to develop artificial intelligence for glaucoma diagnosis and treatment. Journal of Medical Artificial Intelligence, 6. [CrossRef]
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