1. Home
  2. HPMIJ
  3. Artificial intelligence for advancing nursery service
Submit manuscript...
eISSN: 2576-4497

Hospice & Palliative Medicine International Journal

Editorial Volume 8 Issue 4

Artificial intelligence for advancing nursery service

Da Yong Lu,1 Yu Zheng Chen,2 Da Feng Lu2

1School of Life Sciences, Shanghai University, China
2The Second Hospital of Neijiang District, China

Correspondence: Da Yong Lu, School of Life Sciences, Shanghai University, Shanghai200444, PRC, China

Received: November 21, 2025 | Published: November 25, 2025

Citation: Lu DY, Chen YZ, Lu DF. Artificial intelligence for advancing nursery service. Hos Pal Med Int Jnl. 2025;8(4):87-88. DOI: 10.15406/hpmij.2025.08.00279

Download PDF

Abstract

Nursery services face growing demand and workforce shortage. Technical or medical knowledge updating provide new possibility of these updating and improve service satisfactory. Among these hot areas, some supportive techniques associated with artificial intelligence is a great modern trend. This Editorial addresses possible pathway and solution in nursery science progresses.

Keywords: healthcare, nursing, medical discipline, modern technology, artificial intelligence

Introduction

Background

Nursery services face growing demand and workforce shortage. Technical or medical knowledge updating provide new possibility of these updating and improve service satisfactory. Among these hot areas, some supportive techniques associated with artificial intelligence is a great modern trend. This Editorial addresses possible pathway and solution in nursery science progresses.

Current scenes

Nursery service is high physical-burden (physical demanding and less payment).1–4 Physical or spiritual burden can be alleviated by technical advancements.5–9 New nursery initiatives can improve quality of nurses and patient’s satisfactory.10–17

Problem orientation

A large technical issues should be optimized-costs, feasability and enduration optimization in general practices. Robots, software, and algorithms should be aimed.1

Methods

Different areas and disciplines

Different platforms and areas are abstracted (Table 1).

Expanding to more diseases

Expanding AI nursery to more areas and disease treatment is currently underway. It is not narrowed only in nursery. Scientific and technical improvements are also useful. In the future, AI popularity will quickly help nursery service to thrive and indispensable.

Areas

Major applications

Automation

Feasability and replacement of current convention

Scientific knowledge

Notification of new clinical evidence

Reorganization at any times

Changeable therapeutic option adjustment and personalization

24 hour service

Enduring physical or spiritual demanding properly

Cost adjustment

Save human service and resources

Drug treatment

Optimization in doses and times

Working areas

Suitability for more disease treatments

Service progress

Perfection nursery and treatment by machine/deep learning

Technical progress

Improving in diagnosis and treatments in quick & detail ways

Table 1 AI strategies in nursery applications

Future possibility

The high quality of nursery service can be gradually built via different AI approaches. It includes as

  1. System updating and integration
  2. Interaction with medicine19–22
  3. AI in multidisciplines of medicine
  4. Help to all humans (patients, doctors and nurses)
  5. Great future (magic progress) are expected from all angles of medical or pharmaceutical sciences

Conclusion

Some supportive techniques can be integrated. To sustain these medical and technical work, AI techniques may be flourished. Let’s participate it

Acknowledgments

None.

Conflicts of interest

The author declares that there are no conflicts of interest.

References

  1. Lu DY, Chen YZ, Lu DF, et al. Patient’s care and nursery in different diseases. Hosp Palliat Med Int J. 2019;3(1):28–30.
  2. Lu DY, Chen YZ, Lu DF, et al. Patient’s care and nursery in modern medicine. Nursery Pract Health Care. 2019;1(1):101.
  3. Lu DY, Chen YZ, Lu DF. Nursery service, quality promotion. Hosp Palliat Med Int J. 2019;3(3):97–98.
  4. Lu DY, Chen YZ, Lu DF, et al. Nursery service in modern day. Adv Biomed Eng Biotechnol. 2019;1(3):1–2.
  5. Lu DY, Chen YZ, Lu DF. Nursery education, capability and service promotion. Open Access J Nursery. 2019;2(3):1–4.
  6. Lu DY, Chen YZ, Lu DF. Nursery education in schools, significance for career. Biomed Res Rev. 2019;2(2):113.
  7. Iqbal U, Humayyn A, Li YC. Healthcare quality improvement and measurement strategies and its challenges ahead. Int J Qual Health Care. 2019;31(1):1.
  8. Iqbal U, Rabrenovic M, Li YC. Healthcare quality challenges in low- and middle-income countries. Int J Qual Health Care. 2019;31(3):165.
  9. Leebov W, Scott G. Service quality improvement. The customer satisfaction strategy for healthcare. J Healthc Qual. 1996;18(4):35.
  10. Ghaffari M. Building a community of learners: Lessons learned. Nursery Pract Health Care. 2019;1(1):104.
  11. Lu DY, Chen YZ, Lu DF. Nursery education for diabetes. Nurs Care Open Access J. 2020;7(2):35–37.
  12. Calik T, Yalmaz V, Unalp A. Nursing approaches in pediatric epilepsy and ketogenic diet treatment. EC Paediatr. 2020;7(8):110–115.
  13. Lu DY, Chen YZ, Shen Y, et al. Medical treatment for chronic or aggressive diseases, palliative therapy and nursery. Novel Res Sci. 2020;3(2):556.
  14. Lu DY, Chen YZ, Lu DF. Nursery education, narrow-range or wide-range. Nurs Care Open Access J. 2020;7(4):87–89.
  15. Lu DY, Chen YZ, Lu DF. Nursery promotion, education and system updating. Int J Multidiscip Res Updates. 2022;3(1):1–6.
  16. Lu DY, Chen YZ, Che JY, et al. Nursery services in future. J Med Clin Nurs. 2025;6(1):1–3.
  17. Lu DY, Chen YZ, Wu HY, et al. Nursery services advances, global campaign. Nurs Care Open Access J. 2025;11(1):1–3.
  18. Lu DY, Chen YZ, Lu DF. Artificial intelligence in nursery service. Nurs Care Open Access J. 2025;11(3):103–104.
  19. Lu DY, Chen XL, Ding J. Individualized cancer chemotherapy integrating drug sensitivity tests, pathological profile analysis and computational coordination-an effective strategy to improve clinical treatment. Med Hypotheses. 2006;66(1):45–51.
  20. Lu DY. Personalized cancer chemotherapy, an effective way for enhancing outcomes in clinics. Woodhead Publishing; 2014.
  21. Lu DY, Lu TR, Xu B, Che JY, Shen Y, Yarla NS. Individualized cancer therapy, future approaches. Curr Pharmacogenomics Pers Med. 2018;16(2):156–163.
  22. Lu DY, Lu TR, Che JY, Yarla NS. Individualized cancer therapy, what is the next generation? EC Cancer. 2018;2(6):286–297.
Creative Commons Attribution License

©2025 Lu, et al. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.

Citations

Media Partner & Supporters

Journal Menu

Useful Links