Evidence map›Paper›PMID 37799217›Full record

ReviewCureus2023

Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine.

Javed Iqbal, Diana Carolina Cortés Jaimes, Pallavi Makineni, Sachin Subramani, Sarah Hemaida, Thanmai Reddy Thugu, Amna Naveed Butt, Jarin Tasnim Sikto, Pareena Kaur, Muhammad Ali Lak and 3 more

Abstract readReview
In one paragraph

Review in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Reimagining pharmacology education.Pharmacological reviews · 2026
    Review
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Review
  10. Review
  11. Article
  12. Review
  13. Review
  14. Article
  15. Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications.Medical science monitor : international medical journal of experimental and clinical research · 2025
    Review
  16. Genomic medicine and personalized treatment: a narrative review.Annals of medicine and surgery (2012) · 2025
    Review
  17. Article
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Javed IqbalNeurosurgery, Mayo Hospital, Lahore, PAK.
Diana Carolina Cortés JaimesEpidemiology, Universidad Autónoma de Bucaramanga, Bucaramanga, COL.
Pallavi MakineniMedicine, All India Institute of Medical Sciences, Bhubaneswar, Bhubaneswar, IND.
Sachin SubramaniMedicine and Surgery, Employees' State Insurance Corporation (ESIC) Medical College, Gulbarga, IND.
Sarah HemaidaInternal Medicine, Istanbul Okan University, Istanbul, TUR.
Thanmai Reddy ThuguInternal Medicine, Sri Padmavathi Medical College for Women, Sri Venkateswara Institute of Medical Sciences (SVIMS), Tirupati, IND.
Amna Naveed ButtMedicine/Internal Medicine, Allama Iqbal Medical College, Lahore, PAK.
Jarin Tasnim SiktoMedicine, Jahurul Islam Medical College, Kishoreganj, BGD.
Pareena KaurMedicine, Punjab Institute of Medical Sciences, Jalandhar, IND.
Muhammad Ali LakInternal Medicine, Combined Military Hospital, Lahore, PAK.
Monisha AugustineMedicine, Kasturba Medical College, Manipal, Manipal, IND.
Roheen ShahzadMedicine, Combined Military Hospital (CMH) Lahore Medical College and Institute of Dentistry, Lahore, PAK.
Mustafa ArainInternal Medicine, Civil Hospital Karachi, Karachi, PAK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.

Indexed as

ai & robotics in healthcareartificial intelligence in healthcaremodern medicinenewer technology in healthcarenew insights

Identifiers

PMID37799217
PMCPMC10549955

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.