Evidence map›Paper›PMID 38756258›Full record

ReviewCureus2024

Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature.

Mohit Lakkimsetti, Swati G Devella, Keval B Patel, Sarvani Dhandibhotla, Jasleen Kaur, Midhun Mathew, Janvi Kataria, Manisha Nallani, Umm E Farwa, Tirath Patel and 5 more

Abstract readReview
In one paragraph

Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

15 authors.

Mohit LakkimsettiInternal Medicine, Mamata Medical College, Khammam, IND.
Swati G DevellaMedicine, Kempegowda Institute of Medical Sciences, Bangalore, IND.
Keval B PatelSurgery, Narendra Modi Medical College, Ahmedabad, IND.
Sarvani DhandibhotlaGastroenterology, Massachusetts General Hospital, Boston, USA.
Jasleen KaurEmergency Medicine, Max Healthcare, Delhi, IND.
Midhun MathewInternal Medicine, Trinitas Regional Medical Center, Elizabeth, USA.
Janvi KatariaMedicine, DY Patil University, Mumbai, IND.
Manisha NallaniMedicine, Kamineni Academy of Medical Sciences and Research Center, Hyderabad, IND.
Umm E FarwaEmergency Medicine, Jinnah Sindh Medical University, Karachi, PAK.
Tirath PatelMedicine, American University of Antigua, Saint John's, ATG.
Uzoamaka C EgbujoPediatrics, Lagos University Teaching Hospital, Lagos, NGA.
Dakshin Meenashi SundaramInternal Medicine, Employees' State Insurance Corporation (ESIC) Medical College & Post Graduate Institute of Medical Science and Research (PGIMSR), Chennai, IND.
Samar KenawyGeneral Practice, Ministry of Health, Malé, MDV.
Mehak RoyInternal Medicine, School of Medicine Science and Research, Delhi, IND.
Saniyal Farheen KhanMedicine, Governement General Hospital, Nizamabad, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has the ability to completely transform the healthcare industry by enhancing diagnosis, treatment, and resource allocation. To ensure patient safety and equitable access to healthcare, it also presents ethical and practical issues that need to be carefully addressed. Its integration into healthcare is a crucial topic. To realize its full potential, however, the ethical issues around data privacy, prejudice, and transparency, as well as the practical difficulties posed by workforce adaptability and statutory frameworks, must be addressed. While there is growing knowledge about the advantages of AI in healthcare, there is a significant lack of knowledge about the moral and practical issues that come with its application, particularly in the setting of emergency and critical care. The majority of current research tends to concentrate on the benefits of AI, but thorough studies that investigate the potential disadvantages and ethical issues are scarce. The purpose of our article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers. In order to responsibly and successfully integrate AI in these important healthcare domains, policymakers and healthcare professionals must collaborate to create strong regulatory frameworks, safeguard data privacy, remove prejudice, and give healthcare workers the necessary training.

Indexed as

ai and machine learningbig datacomputer-aided detectioncomputer-aided diagnosiscomputer visionconvoluted neural networkdeep learning artificial intelligencedeep neural networkemergency medicine - emergency critical care - disaster medicine

Identifiers

PMID38756258
PMCPMC11098056

What Socratic 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 Socratic graph.