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Curated Papers > Highly Cited Authoritative Literature on "AI Drug Discovery"

The potential for artificial intelligence in healthcare. ​

Literature Information ​

DOI10.7861/futurehosp.6-2-94
PMID31363513
JournalFuture healthcare journal
Publication Year2019
Times Cited842
KeywordsArtificial intelligence, clinical decision support, electronic health record systems
Literature TypeCase Reports, Journal Article
ISSN2514-6645
Pages94-98
Issue6(2)
AuthorsThomas Davenport, Ravi Kalakota

TL;DR ​

The increasing complexity and volume of healthcare data are driving the adoption of artificial intelligence (AI) across various sectors, including diagnostics, patient engagement, and administrative functions. While AI shows potential to outperform humans in certain tasks, significant implementation challenges and ethical considerations will hinder widespread automation in healthcare professions for the foreseeable future.

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Artificial intelligence · clinical decision support · electronic health record systems

Abstract ​

The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.

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Primary Questions Addressed ​

  1. How can AI enhance the accuracy of diagnosis and treatment recommendations in healthcare?
  2. What specific ethical issues arise from the implementation of AI technologies in patient care?
  3. In what ways can AI improve patient engagement and adherence to treatment plans?
  4. What are the barriers to large-scale automation of healthcare jobs despite the capabilities of AI?
  5. How do different stakeholders in healthcare, such as payers and providers, perceive the integration of AI technologies?

Key Findings ​

Research Background and Objectives ​

The paper discusses the increasing role of artificial intelligence (AI) in healthcare, driven by the growing complexity and volume of data. The authors aim to explore various AI applications in healthcare, their potential benefits, and the challenges to their widespread implementation.

Main Methods/Materials/Experimental Design ​

The authors provide a comprehensive overview of several AI technologies relevant to healthcare, categorized into key applications:

  1. Machine Learning (ML): Focuses on predictive modeling, particularly in precision medicine.
  2. Natural Language Processing (NLP): Involves understanding and generating human language, applicable in clinical documentation.
  3. Robotic Process Automation (RPA): Automates repetitive administrative tasks.
  4. Physical Robots: Used in surgical procedures and hospital supply delivery.

Technical Route Overview ​

Mermaid diagram

Key Results and Findings ​

  • AI technologies, particularly machine learning and deep learning, have shown potential in outperforming human capabilities in specific tasks, such as disease diagnosis and patient risk assessment.
  • Applications in precision medicine are evolving, with AI systems capable of analyzing vast datasets to predict treatment outcomes.
  • NLP is enhancing the management of clinical documentation, improving workflow efficiency.
  • Despite the promise of AI, many healthcare organizations face barriers to implementation, including integration challenges with existing systems and the need for regulatory approval.

Main Conclusions/Significance/Innovation ​

The paper concludes that while AI holds transformative potential for healthcare delivery, its widespread adoption will be gradual. Key challenges include:

  • Integration with electronic health records (EHR) and clinical workflows.
  • Addressing ethical concerns related to transparency, accountability, and potential biases in AI algorithms.
  • The role of AI will likely be to augment, rather than replace, human clinicians, enabling them to focus on tasks requiring empathy and complex decision-making.

Research Limitations and Future Directions ​

  • Limitations: The paper primarily discusses theoretical applications and does not provide empirical data on AI effectiveness in clinical settings.
  • Future Directions: Future research should focus on:
    • Developing standardized AI systems that can be easily integrated into clinical workflows.
    • Addressing ethical and regulatory challenges to facilitate AI adoption.
    • Exploring the potential for AI to create new job roles in healthcare.

In summary, while AI technologies are poised to enhance healthcare delivery significantly, the transition will require careful navigation of integration, ethical, and practical challenges.

References ​

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Literatures Citing This Work ​

  1. Staring into the crystal ball. - Tom Downes - Future healthcare journal (2019)
  2. Factors Influencing Motivation and Engagement in Mobile Health Among Patients With Sickle Cell Disease in Low-Prevalence, High-Income Countries: Qualitative Exploration of Patient Requirements. - David-Zacharie Issom;André Henriksen;Ashenafi Zebene Woldaregay;Jessica Rochat;Christian Lovis;Gunnar Hartvigsen - JMIR human factors (2020)
  3. Novel Coronavirus Infection (COVID-19) in Humans: A Scoping Review and Meta-Analysis. - Israel Júnior Borges do Nascimento;Nensi Cacic;Hebatullah Mohamed Abdulazeem;Thilo Caspar von Groote;Umesh Jayarajah;Ishanka Weerasekara;Meisam Abdar Esfahani;Vinicius Tassoni Civile;Ana Marusic;Ana Jeroncic;Nelson Carvas Junior;Tina Poklepovic Pericic;Irena Zakarija-Grkovic;Silvana Mangeon Meirelles Guimarães;Nicola Luigi Bragazzi;Maria Bjorklund;Ahmad Sofi-Mahmudi;Mohammad Altujjar;Maoyi Tian;Diana Maria Cespedes Arcani;Dónal P O'Mathúna;Milena Soriano Marcolino - Journal of clinical medicine (2020)
  4. A "Third Wheel" Effect in Health Decision Making Involving Artificial Entities: A Psychological Perspective. - Stefano Triberti;Ilaria Durosini;Gabriella Pravettoni - Frontiers in public health (2020)
  5. Emergence of New Disease: How Can Artificial Intelligence Help? - Yurim Park;Daniel Casey;Indra Joshi;Jiming Zhu;Feng Cheng - Trends in molecular medicine (2020)
  6. Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing. - Debbie Rankin;Michaela Black;Raymond Bond;Jonathan Wallace;Maurice Mulvenna;Gorka Epelde - JMIR medical informatics (2020)
  7. Artificial Intelligence in Modern Medicine - The Evolving Necessity of the Present and Role in Transforming the Future of Medical Care. - Pradnya Brijmohan Bhattad;Vinay Jain - Cureus (2020)
  8. Factors Associated with 5-Year Costs of Care among a Cohort of Alcohol Use Disorder Patients: A Bayesian Network Model. - Elina Rautiainen;Olli-Pekka Ryynänen;Tiina Laatikainen;Pekka Kekolahti - Healthcare informatics research (2020)
  9. Shooting from the hip into our own foot? A perspective on how artificial intelligence may disrupt medical training. - Anmol Arora - Future healthcare journal (2020)
  10. Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians. - Onur Asan;Alparslan Emrah Bayrak;Avishek Choudhury - Journal of medical Internet research (2020)

... (832 more literatures)

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