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Revolutionizing healthcare: the role of artificial intelligence in clinical practice. ​

Literature Information ​

DOI10.1186/s12909-023-04698-z
PMID37740191
JournalBMC medical education
Impact Factor3.2
JCR QuartileQ1
Publication Year2023
Times Cited600
KeywordsAI, Clinicians, Decision-making, Healthcare, Patient care
Literature TypeJournal Article, Review
ISSN1472-6920
Pages689
Issue23(1)
AuthorsShuroug A Alowais, Sahar S Alghamdi, Nada Alsuhebany, Tariq Alqahtani, Abdulrahman I Alshaya, Sumaya N Almohareb, Atheer Aldairem, Mohammed Alrashed, Khalid Bin Saleh, Hisham A Badreldin, Majed S Al Yami, Shmeylan Al Harbi, Abdulkareem M Albekairy

TL;DR ​

This review highlights the transformative potential of artificial intelligence (AI) in healthcare, emphasizing its applications in disease diagnosis, treatment recommendations, and patient engagement while addressing ethical and legal challenges. By providing a comprehensive analysis of AI's capabilities and implications, the study aids healthcare organizations in effectively adopting AI technologies to enhance patient care and optimize clinical practices.

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AI · Clinicians · Decision-making · Healthcare · Patient care

Abstract ​

INTRODUCTION Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools.

RESEARCH SIGNIFICANCE This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies.

MATERIALS AND METHODS The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application.

RESULTS Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust.

CONCLUSION AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

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Primary Questions Addressed ​

  1. What specific AI technologies are currently being utilized in clinical practice, and how do they differ in their applications?
  2. How can healthcare organizations ensure ethical implementation of AI technologies while maintaining patient privacy and trust?
  3. What are the potential barriers to the widespread adoption of AI in healthcare, and how can these challenges be overcome?
  4. In what ways can AI enhance patient engagement and education, and what impact does this have on treatment outcomes?
  5. How does the integration of AI in healthcare influence the role of healthcare professionals, particularly in decision-making processes?

Key Findings ​

Research Background and Objectives ​

The integration of artificial intelligence (AI) into healthcare has the potential to revolutionize clinical practice, improving patient care and outcomes. This review aims to provide a comprehensive overview of the current state of AI in clinical practice, highlighting its applications, benefits, limitations, and future directions. The study underscores the necessity of equipping healthcare providers with the knowledge and tools to implement AI effectively.

Main Methods/Materials/Experimental Design ​

The authors conducted a systematic review of indexed literature from databases such as PubMed/Medline, Scopus, and EMBASE, focusing on AI's impact in healthcare settings. The review included articles published in English without time constraints. Key search terms included various AI techniques (machine learning, deep learning, natural language processing) and their applications in personalized medicine, diagnostics, and patient management.

Mermaid diagram

Key Results and Findings ​

  1. Diagnostic Improvements: AI tools demonstrated superior accuracy in disease diagnosis compared to traditional methods, with significant reductions in false positives and negatives. For instance, AI showed higher sensitivity in breast cancer detection than radiologists.

  2. Treatment Optimization: AI can assist in developing personalized treatment plans, optimizing medication dosages, and predicting patient responses to therapies, thus enhancing the efficacy of clinical decisions.

  3. Cost and Time Efficiency: The integration of AI into healthcare has the potential to reduce operational costs and save time while minimizing human errors in clinical settings.

  4. Patient Engagement: AI technologies can improve patient education and engagement through virtual health assistants and chatbots, enhancing the overall patient experience.

Main Conclusions/Significance/Innovativeness ​

The review concludes that AI is not merely a tool for automating tasks but a transformative technology that can significantly enhance patient care across various healthcare settings. The responsible implementation of AI in clinical practice requires addressing ethical considerations, data privacy issues, and the need for human expertise in decision-making.

Research Limitations and Future Directions ​

  • Limitations: The review is limited to English-language publications and may not encompass all relevant studies. Additionally, the challenges related to data quality, privacy, and algorithmic bias remain significant barriers to AI integration in healthcare.

  • Future Directions: Future research should focus on improving data quality, developing robust AI models, and ensuring ethical guidelines are in place. Training healthcare professionals on AI technologies is essential for effective integration into clinical practice.

Summary Table ​

AspectFindings
AI ApplicationsDiagnostics, treatment planning, patient engagement, and population health management
Key BenefitsImproved accuracy, reduced costs, enhanced efficiency, and better patient outcomes
ChallengesData privacy, algorithmic bias, need for human oversight, and ethical considerations
Future Research DirectionsFocus on data quality, ethical guidelines, and training healthcare providers in AI technologies

References ​

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Literatures Citing This Work ​

  1. Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine. - Francisco Guillen-Grima;Sara Guillen-Aguinaga;Laura Guillen-Aguinaga;Rosa Alas-Brun;Luc Onambele;Wilfrido Ortega;Rocio Montejo;Enrique Aguinaga-Ontoso;Paul Barach;Ines Aguinaga-Ontoso - Clinics and practice (2023)
  2. Pilot Testing of a Tool to Standardize the Assessment of the Quality of Health Information Generated by Artificial Intelligence-Based Models. - Malik Sallam;Muna Barakat;Mohammed Sallam - Cureus (2023)
  3. The leading global health challenges in the artificial intelligence era. - Amal Mousa Zaidan - Frontiers in public health (2023)
  4. How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. - Luís Pinto-Coelho - Bioengineering (Basel, Switzerland) (2023)
  5. Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis. - Aabid Mustafa Koul;Faisel Ahmad;Abida Bhat;Qurat-Ul Aein;Ajaz Ahmad;Aijaz Ahmad Reshi;Rauf-Ur-Rashid Kaul - Biomedicines (2023)
  6. Synergistic strategies: Optimizing outcomes through a multidisciplinary approach to clinical rounds. - Varsha Srinivas;Udit Choubey;Jatin Motwani;Fnu Anamika;Chaitanya Chennupati;Nikita Garg;Vasu Gupta;Rohit Jain - Proceedings (Baylor University. Medical Center) (2024)
  7. Radiological Insights into Sacroiliitis: A Narrative Review. - Asma'a Al-Mnayyis;Shrouq Obeidat;Ammar Badr;Basil Jouryyeh;Saif Azzam;Hayat Al Bibi;Yara Al-Gwairy;Sarah Al Sharie;Giustino Varrassi - Clinics and practice (2024)
  8. Gross failure rates and failure modes for a commercial AI-based auto-segmentation algorithm in head and neck cancer patients. - Simon W P Temple;Carl G Rowbottom - Journal of applied clinical medical physics (2024)
  9. Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review. - Isabella Veneziani;Angela Marra;Caterina Formica;Alessandro Grimaldi;Silvia Marino;Angelo Quartarone;Giuseppa Maresca - Journal of personalized medicine (2024)
  10. A Preliminary Checklist (METRICS) to Standardize the Design and Reporting of Studies on Generative Artificial Intelligence-Based Models in Health Care Education and Practice: Development Study Involving a Literature Review. - Malik Sallam;Muna Barakat;Mohammed Sallam - Interactive journal of medical research (2024)

... (590 more literatures)

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