The efficacy and use of Artificial Intelligence in medical data management and decision-making to future horizons

doi:10.15406/ijrrt.2025.12.00418

International Journal of

eISSN: 2574-8084

Radiology & Radiation Therapy

Mini Review Volume 12 Issue 2

The efficacy and use of Artificial Intelligence in medical data management and decision-making to future horizons

Nina TUNÇEL,^1,2

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Erdi BİLGİÇ³

¹Department of Physics, Faculty of Science, Akdeniz University, Turkey
²Department of Radiation Oncology, Faculty of Medicine, Akdeniz University, Turkey
³Department of Medical Services and Techniques, Vocational School of Health Sciences, Antalya Bilim University, Turkey

Correspondence: Nina TUNÇEL, Department of Radiation Oncology, Faculty of Medicine and Department of Physics, Faculty of Science, Akdeniz University, Antalya Turkey, Pınarbaşı, Akdeniz Unv. Hst., 07070 Konyaalti Antalya, Turkey

Received: May 01, 2025 | Published: May 16, 2025

Citation: TUNÇEL N, BİLGİÇ E. The efficacy and use of Artificial Intelligence in medical data management and decision-making to future horizons. Int J Radiol Radiat Ther. 2025;12(2):41-43. DOI: 10.15406/ijrrt.2025.12.00418

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Abstract

The wide range of applications of Artificial Intelligence (AI) in all scientific and technological activities, by its inclusion in healthcare systems, is mentioned. The integration of AI within healthcare systems is prompting an extensive transformation in the domains of medical data management and clinical decision-making. This study aims to ponder the efficacy of AI in processing massive and sophisticated medical datasets. By highlighting its influence on enhancing diagnostic accuracy, optimizing treatment planning, and advancing personalized medicine. Furthermore, this provides a comprehensive analysis of the methods throughout generative and productive AI, re-exploring the ever-evolving field of healthcare by evaluating the potential benefits, challenges, ethical considerations, and possible developments. It could be concluded that the reproductive possibilities of AI will redesign the modern healthcare landscape in the future horizons.

Keywords: artificial intelligence, diagnostic accuracy treatment planning, good machine learning practice

Abbrevation

AI, artificial intelligence; ML, Machine Learning; NLP, Natural language processing; HIPAA, health insurance portability and accountability act; GDPR, general data protection regulation; UK, United Kingdom; CNN, convolutional neural network; MR-Linac, magnetic resonance-linear accelerator; CDSS, clinical decision support systems; GMLP, good machine learning practice; EU, European Union

Introduction

Advances in AI have led to significant breakthroughs in the healthcare industry. AI technologies such as machine learning (ML), natural language processing, and image processing are now being applied to manage medical data and assist in clinical decisions and manage treatment processes. As electronically stored health records, imaging data, and genomic information accumulate in the virtual environment, traditional data management techniques become inadequate. Moreover, AI offers promising solutions by improving accuracy and predictive capabilities with the development of the ability to process this data.

Medical data management using artificial intelligence

Data structuring and cleaning

AI algorithms are adept at cleaning and organizing unstructured or semi-structured data, such as doctor notes, lab reports, and medical imaging. Natural language processing can extract meaningful information from free-text data and improve the quality and usability of medical records.

Example: IBM Watson Health uses NLP to process clinical notes and extract important patient data, helping doctors understand patient history without manually reviewing voluminous records. This has been particularly useful in oncology, where oncologists rely on NLP to quickly summarize and access complex treatment histories.¹

Multimodal data integration

AI facilitates the integration of various data types, including clinical notes, imaging data, and genome sequences. By creating a consistent dataset, AI supports a more comprehensive understanding of patient health, which is important for accurate diagnosis and personalized treatment.

Example: Tempus, a health technology company, uses AI to integrate and analyze genomic and clinical data to help oncologists determine the best treatment for cancer patients. The system correlates genetic mutations with therapy responses, enabling precision medicine applications in real-time oncology care.²

Data security and privacy

AI technologies, including blockchain and anomaly detection algorithms, increase data security by identifying unauthorized access and potential breaches. Additionally, AI can support compliance with regulatory frameworks such as HIPAA in GDPR.

Example: AI platform DeepMind Health (by Google) has developed an auditing tool that monitors access to patient data, ensuring transparency and privacy compliance in UK hospitals. This system creates detailed logs and uses machine learning to detect suspicious access patterns, alerting administrators in real time.³

Artificial intelligence in clinical decision making

Diagnostic support

AI-driven diagnostic tools, such as convolutional neural networks (CNNs), have demonstrated performance equal to or superior to human experts in fields such as radiology and pathology. These tools analyze medical images to detect conditions such as tumors, fractures, and neurological disorders.⁴

Example: Google’s DeepMind has developed an AI system that detects more than 50 eye diseases with the same accuracy as eye specialists using retinal scans. It automatically triages urgent cases, helping to triage patients for faster treatment and reduce delays in care.⁵

Treatment planning and optimization

Machine learning models can evaluate patient data to recommend treatment options, predict outcomes, and adjust treatment approaches in real time.⁶ AI is particularly useful in oncology, where it can optimize radiotherapy plans and chemotherapy regimens.⁷

Example: Varian Medical Systems integrates AI into its Ethos therapy platform to personalize radiotherapy plans in real time based on daily patient imaging. This enables adaptive radiation therapy, where the dose is modified based on changes in tumor size or organ movement.⁸

Additional Example: The Elekta system incorporates AI into the Unity MR-Linac platform by combining real-time MRI imaging with adaptive radiotherapy planning. This allows visualization of the tumor and surrounding organs during each treatment session and real-time adjustments. For example, if a tumor has shifted slightly or a nearby organ is in a sensitive position, the AI can suggest changing the radiation beam, resulting in greater treatment precision and fewer side effects.⁹

Predictive analytics

AI enables predictive modeling for disease progression and risk assessment. For example, AI-powered early warning systems identify patients at risk of sepsis or cardiac events, enabling timely interventions.¹⁰

Example: Zaprt Johns Hopkins. It does this by continuously analyzing hundreds of clinical variables, such as heart rate, lab values, and medication records, and sending real-time alerts to staff.¹¹

Clinical decision support systems (CDSS)

AI enhances CDSS by providing evidence-based recommendations tailored to individual patients. These systems assist in diagnosis, drug selection, and monitoring treatment effectiveness.¹²

Example: Mayo Clinic helps. Their platform suggests possible diagnoses, confidence scores, and links to relevant guidelines and treatment protocols.¹³

Benefits of Artificial Intelligence in healthcare

Improved diagnostic accuracy and consistency
Improved patient outcomes with personalized treatment
Reducing the administrative burden on healthcare providers
Real-time insights and faster decision making

It reaffirmed its commitment to advance regulatory pathways by promoting the development of standards, guidelines, best practices and tools throughout the medical product lifecycle, based on the principles of Good Machine Learning Practice (GMLP).¹⁴

The ethical use of AI in healthcare requires respect for patient autonomy; the WHO emphasizes the importance of AI systems being transparent and understandable, which is vital to ensuring accountability and responsibility for AI-enabled medical decisions.¹⁵

Gómez-González and co-authors conducted a comprehensive analysis of current and emerging applications of AI in medicine, health, and well-being, focusing on their impact on physical and mental health, as well as societal and environmental well-being, including the well-being of future generations.¹⁶

The European Union (EU) has published guidelines for implementing Regulation (EU) 2024/1689, which establishes a scientific panel of independent experts in artificial intelligence to advise on ethical, technical, and regulatory matters, ensuring responsible AI development and use across member states.¹⁷

Challenges in limitations

Data Bias and Fairness: AI models trained on unrepresentative datasets may produce biased results.

Interpretability: Many AI models function as “black boxes,” making their decisions difficult to interpret.

Ethical and legal issues: There are concerns about informed consent, data ownership, and liability in case of error.

Integration with existing systems: Legacy systems may not be compatible with advanced AI solutions.

Conclusion

Artificial intelligence has a revolutionary effect on many steps such as medical data management, clinical decision-making and treatment planning processes. Despite its multifaceted benefits, current limitations and ethical concerns of artificial intelligence in the field of health should be taken into account and progress should be made in this direction. Future research should focus on creating regulatory frameworks that provide data diversity and support safe and effective artificial intelligence integration, while focusing on various artificial intelligence models. From this perspective, there are promising areas of study such as artificial intelligence-supported drug discovery, artificial intelligence-supported personalized medicine, autonomous clinical systems and neuro-symbolic artificial intelligence in health services. As long as each of these studies develops within the framework of ethical standards, they can offer promising opportunities in terms of technology as well as improving the possibilities in the field of health.