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Authors are the souls of any journal, and deserve much respect. To publish a journal manuscripts are needed from authors. Authors have a great responsibility for producing facts of their work in terms of number and results truthfully and an individual honesty is expected from authors in this regards. Both ways its true "No authors-No manuscripts-No journals" and "No journals–No manuscripts–No authors". Reviewing a manuscript is also a very responsible and important task of any peer-reviewed journal and to be taken seriously. It needs knowledge on the subject, sincerity, honesty and determination. Although the process of reviewing a manuscript is a time consuming task butit is expected to give one's best remarks within the time frame of the journal.
Salient features of the JCDR: It is a biomedical, multidisciplinary (including all medical and dental specialities), e-journal, with wide scope and extensive author support. At the same time, a free text of manuscript is available in HTML and PDF format. There is fast growing authorship and readership with JCDR as this can be judged by the number of articles published in it i e; in Feb 2007 of its first issue, it contained 5 articles only, and now in its recent volume published in April 2011, it contained 67 manuscripts. This e-journal is fulfilling the commitments and objectives sincerely, (as stated by Editor-in-chief in his preface to first edition) i e; to encourage physicians through the internet, especially from the developing countries who witness a spectrum of disease and acquire a wealth of knowledge to publish their experiences to benefit the medical community in patients care. I also feel that many of us have work of substance, newer ideas, adequate clinical materials but poor in medical writing and hesitation to submit the work and need help. JCDR provides authors help in this regards.
Timely publication of journal: Publication of manuscripts and bringing out the issue in time is one of the positive aspects of JCDR and is possible with strong support team in terms of peer reviewers, proof reading, language check, computer operators, etc. This is one of the great reasons for authors to submit their work with JCDR. Another best part of JCDR is "Online first Publications" facilities available for the authors. This facility not only provides the prompt publications of the manuscripts but at the same time also early availability of the manuscripts for the readers.
Indexation and online availability: Indexation transforms the journal in some sense from its local ownership to the worldwide professional community and to the public.JCDR is indexed with Embase & EMbiology, Google Scholar, Index Copernicus, Chemical Abstracts Service, Journal seek Database, Indian Science Abstracts, to name few of them. Manuscriptspublished in JCDR are available on major search engines ie; google, yahoo, msn.
In the era of fast growing newer technologies, and in computer and internet friendly environment the manuscripts preparation, submission, review, revision, etc and all can be done and checked with a click from all corer of the world, at any time. Of course there is always a scope for improvement in every field and none is perfect. To progress, one needs to identify the areas of one's weakness and to strengthen them.
It is well said that "happy beginning is half done" and it fits perfectly with JCDR. It has grown considerably and I feel it has already grown up from its infancy to adolescence, achieving the status of standard online e-journal form Indian continent since its inception in Feb 2007. This had been made possible due to the efforts and the hard work put in it. The way the JCDR is improving with every new volume, with good quality original manuscripts, makes it a quality journal for readers. I must thank and congratulate Dr Hemant Jain, Editor-in-Chief JCDR and his team for their sincere efforts, dedication, and determination for making JCDR a fast growing journal.
Every one of us: authors, reviewers, editors, and publisher are responsible for enhancing the stature of the journal. I wish for a great success for JCDR."
Thanking you
With sincere regards
Dr. Rajendra Kumar Ghritlaharey, M.S., M. Ch., FAIS
Associate Professor,
Department of Paediatric Surgery, Gandhi Medical College & Associated
Kamla Nehru & Hamidia Hospitals Bhopal, Madhya Pradesh 462 001 (India)
E-mail: drrajendrak1@rediffmail.com
On May 11,2011
Dr. Shankar P.R.
"On looking back through my Gmail archives after being requested by the journal to write a short editorial about my experiences of publishing with the Journal of Clinical and Diagnostic Research (JCDR), I came across an e-mail from Dr. Hemant Jain, Editor, in March 2007, which introduced the new electronic journal. The main features of the journal which were outlined in the e-mail were extensive author support, cash rewards, the peer review process, and other salient features of the journal.
Over a span of over four years, we (I and my colleagues) have published around 25 articles in the journal. In this editorial, I plan to briefly discuss my experiences of publishing with JCDR and the strengths of the journal and to finally address the areas for improvement.
My experiences of publishing with JCDR: Overall, my experiences of publishing withJCDR have been positive. The best point about the journal is that it responds to queries from the author. This may seem to be simple and not too much to ask for, but unfortunately, many journals in the subcontinent and from many developing countries do not respond or they respond with a long delay to the queries from the authors 1. The reasons could be many, including lack of optimal secretarial and other support. Another problem with many journals is the slowness of the review process. Editorial processing and peer review can take anywhere between a year to two years with some journals. Also, some journals do not keep the contributors informed about the progress of the review process. Due to the long review process, the articles can lose their relevance and topicality. A major benefit with JCDR is the timeliness and promptness of its response. In Dr Jain's e-mail which was sent to me in 2007, before the introduction of the Pre-publishing system, he had stated that he had received my submission and that he would get back to me within seven days and he did!
Most of the manuscripts are published within 3 to 4 months of their submission if they are found to be suitable after the review process. JCDR is published bimonthly and the accepted articles were usually published in the next issue. Recently, due to the increased volume of the submissions, the review process has become slower and it ?? Section can take from 4 to 6 months for the articles to be reviewed. The journal has an extensive author support system and it has recently introduced a paid expedited review process. The journal also mentions the average time for processing the manuscript under different submission systems - regular submission and expedited review.
Strengths of the journal: The journal has an online first facility in which the accepted manuscripts may be published on the website before being included in a regular issue of the journal. This cuts down the time between their acceptance and the publication. The journal is indexed in many databases, though not in PubMed. The editorial board should now take steps to index the journal in PubMed. The journal has a system of notifying readers through e-mail when a new issue is released. Also, the articles are available in both the HTML and the PDF formats. I especially like the new and colorful page format of the journal. Also, the access statistics of the articles are available. The prepublication and the manuscript tracking system are also helpful for the authors.
Areas for improvement: In certain cases, I felt that the peer review process of the manuscripts was not up to international standards and that it should be strengthened. Also, the number of manuscripts in an issue is high and it may be difficult for readers to go through all of them. The journal can consider tightening of the peer review process and increasing the quality standards for the acceptance of the manuscripts. I faced occasional problems with the online manuscript submission (Pre-publishing) system, which have to be addressed.
Overall, the publishing process with JCDR has been smooth, quick and relatively hassle free and I can recommend other authors to consider the journal as an outlet for their work."
Dr. P. Ravi Shankar
KIST Medical College, P.O. Box 14142, Kathmandu, Nepal.
E-mail: ravi.dr.shankar@gmail.com
On April 2011 Anuradha
Dear team JCDR, I would like to thank you for the very professional and polite service provided by everyone at JCDR. While i have been in the field of writing and editing for sometime, this has been my first attempt in publishing a scientific paper.Thank you for hand-holding me through the process.
Dr. Anuradha
E-mail: anuradha2nittur@gmail.com
On Jan 2020
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Full Version
Artificial Intelligence and Healthcare
Correspondence Address :
Dr. Harish Rangareddy,
Associate Professor, Department of Biochemistry, Sri Devaraj Urs Medical College, Kolar, Karnataka, India.
E-mail: harishreddy1349@gmail.com
Computer reasoning, Knowledge acquisition, Knowledge representation
Indian economy is rapidly growing with the world’s second largest population where the young people are eager to adopt Artificial Intelligence (AI) to resolve complex problems. “AI is a branch of computer science capable of analysing complex medical data”. The ability of AI to establish significant relationships within a data set can be employed in various clinical situations for diagnosis, therapy and prognosis (1). The AI has brought a paradigm in the advent of technology in the areas of education, healthcare, financial services and others. AI has been applied to increase productivity and simplify the processes of various areas. The spectrum of usage spans from the smartphones to the diagnosis of diseases (2). With the increasing societal burden of both communicable and non communicable diseases in the healthcare sector AI is set to change the outlook of healthcare delivery. AI is utilised in multifarious settings of hospitals, diagnostic laboratories and research facilities. The technology is used to understand and construct data from various medical data sets and analyse like humans. This created an opportunity which is still not completely explored to enhance patient care by engaging patients and improving patient compliance (3). However, a lacuna of knowledge exists about the concepts of AI and its potential in healthcare delivery system among healthcare professionals. This article attempts to introduce the reader to the myriad of uses of AI in the medical realm.
What is AI?: A computer simulating the characteristics of human intellectual processes including capacity and potential to rationalise, apprehend, simplify, or analyse from prior experience to obtain outcomes without being specifically programmed for distinctive action represents AI (4). The general consensus is any machine which reasons, develops strategy, solves problems, and makes “judgements under uncertainty representing knowledge, including common sense knowledge, plans, learns, communicates in natural language and integrates all these skills towards common goals” conforms intelligence (5).
Machine Learning and Natural Language Processing (NLP) are subfields of AI and it is worth noting nuances between the two. In machine learning, the device analyses structured data such as raw data of cardiac electrophysiology, diagnostic imaging and genetics. For healthcare applications, the machine learning allows phenotyping of individuals’ health facilitating targeted diagnostic or therapeutic approaches to diseases. AI with natural language processing can be applied for clinical data to extract information from free text such as clinical observations or health journals and converting into standard clinical terminologies. This can be used to fill specified templates enhancing structured health check data, a process called as “Structured Data Capture (SDC)”. NLP technique helps in data improvement of data generated from clinical work and clinical notes in human language and once processed the data is investigated by machine learning leading to clinical decision-making (6),(7). The process how the NLP facilitates to arrive at a diagnosis is shown in (Table/Fig 1).
Why AI is needed?: Clinical case management involves decision-making in its integrated phases of diagnosis, therapy and prognosis. Clinical decision-making is a crucial event in the medical field which is multifarious and many a times compounded by co-morbidities of the patient. This can lead to a delay in diagnosis or there may be a remote chance of improper medication. This occurs due to the confounding variables which ought to be taken into consideration as a potential source of difference of opinions amongst practitioners. The need for an effective tool that can contemplate all of the variables and suggest the outcomes in different situations in a patient led to the application of AI in the medical field (8).
What are the applications of AI in medicine?: The evolution of AI in biomedicine has been remarkable. The AI systems already in use for patient care are IBM Watson and Babylon’s AI Chatbot. IBM Watson has transformed the field of Oncology care as an advisory device using NLP to abridge patients’ electronic medical records and search databases. This can be made use to advise the best feasible treatment options in cancer chemotherapy. It has facilitated customised drug selection for treatment of cancer patients and revealed to be equal or better in efficiency than human experts. Apart from this, IBM Watson AI has been implemented as genomic analysis tool for cancer, training tool for medical students, diagnostic tool and clinical decision support tool (9).
In Oregon, USA at the Oregon Health and Science University (OHSU) Knight Cancer Institute, the AI based Project Hanover of Microsoft has predicted the most effective chemotherapeutic drugs customised for individual patients with efficiency similar to that of human subject experts leading to precision cancer therapy. United Kingdom’s Royal Free London NHS Foundation Trust, used an AI-based phone application called Streams working on Google’s DeepMind platform for detecting health risks by analysing medical data collected from NHS patients (10).
An algorithm developed by Stanford’s researchers was able to diagnose pneumonia better than human radiologists and in ophthalmology, the computer was as efficient as an expert Ophthalmologist in making a referral decision for diabetic retinopathy (11). AI-assisted screening and diagnosis of diseases based on images is presently evolving; especially in the field of Ophthalmology with a focus on diseases of high incidence like diabetic retinopathy, cataract, glaucoma and retinal vein occlusion. There is software available that integrate AI and machine learning for processing the fundal images and assessment (12).
At Alexandru Ioan Cuza University in Lasi, Romania the Computer Science faculty has collaborated with the University of Medicine, Lasi on the REVERT (taRgeted thErapy for adVanced colorectal canceR paTients) project with the main goal of improving personalised approach to drugs using AI which identifies the best possible therapeutic intervention for patients with metastatic colorectal cancer (13).
Thurzo A et al., (2021) explored the application of 3 Dimensional (3D) Convolutional Neural Networks (3D CNN) in forensic research from five perspectives for application: (i) age estimation (ii) gender determination (iii) facial growth vectors prediction (iv) 3D cephalometric landmark detection for craniofacial morphometry (v) facial probabilistic estimation from the skull and vice versa (14).
During the pandemic, AI involving both machine learning based diagnostic applications and deep learning based diagnostic applications have been deployed to aid in the diagnosis of Coronavirus Disease-2019 (COVID-19). AI facilitated the rapid review of electronic medical records and medical images (Chest Computed Tomography, X-ray) and differentiating COVID-19 from other pneumonia. Another feature of AI, the percentage involvement of lobes of the lung can be quantified and the changes in the follow-up scans evaluated the infection progression under various treatment modalities of COVID-19 (15).
With the advent of healthcare delivery systems the result is a change in demographics with increased life expectancy. The elderly individuals require living assistance characteristic to the spatial, temporal and activity variance. AI based physical assistance systems process information acquired from the physical environment using cyber resources and deliver living assistance as well as interfaces to further medical services.
Critical analysis of AI revolution leading to high value care: For AI to be utilised successfully in healthcare delivery systems, sustained access to appropriate data is quintessential. The complex data sets may be analysed rapidly and with greater precision by AI to identify patterns. The scientific literature can be searched for pertinent studies and systematic reviews for information retrieval and combining the diverse data using AI, for facilitating in drug discovery. AI based mobile applications related to the medical field have the ability to allow people to assess their own symptoms and seek appropriate medical attention aiding in secondary prevention.
The potential applications of AI are limited by the discrepancies in the accessibility and quality of data as AI depends on digital data. Other problems of implementing AI in healthcare delivery system are regulatory compliance requirements, adoption of the AI by patient and healthcare provider, and also dearth of data exchange.
Current Perspectives
In a survey conducted by Ranjana V et al., (2021) to evaluate the awareness among dental students regarding the utility of AI medicine it was observed that 56% were aware that the analysis of the huge data generated from patient information in the medical field can be carried out without direct human input using AI and 57% felt that AI can revolutionise the clinical decision-making (16).
From an epistemological perspective, Arnold MH (2021) has proposed that clinicians should neither accept AI without critically analysing nor irrationally resist the developments in medical field utilising AI (17).
In his commentary on algorithms, machines and medicine Coiera E (2019) stated that the current medical practitioners should know “the difference between what a machine says and what we must do” (18).
What are the AI adoption challenges in India?: In India there are no clear laws that regulate the data transfer. Healthcare providers should be cautious when sharing patient data with third party who is not obliged with maintaining the confidentiality of the patient. The patients must be informed when such data sharing is done and consent must be obtained. As an effort to minimise the data exchange the data which is collected without explicit consent of patients may be identified and published in public domain by a statutory body. The key challenge with AI adoption in India will be data privacy, misuse and accountability.
Infrastructural and financial feasibility of adoption are also relevant factors in India. AI systems can be expensive to train, test and deploy. India is long overdue for AI in healthcare delivery but from an economic perspective the expansion costs of healthcare delivery acts as a deterrent.
The leveraging of AI by healthcare professionals to improve patient care may be deterred by low familiarity and whether the medical professional are ready for accepting AI within established healthcare practices is a factor which cannot be ignored. The patient doctor relation and related entities are likely to be transformed with the use of AI.
In India AI applications which are being developed and made use of, include algorithms that analyse radiology images like chest X rays and other images, read electrocardiograms and spot abnormal patterns, evaluate fundus photographs for signs of retinopathy and scan pathology slides. There are resource gaps in India and these can be bridged with the advent of technology by exploring the prospects of AI to bring forth advances in healthcare delivery system.
DOI: 10.7860/JCDR/2022/56148.17020
Date of Submission: Mar 06, 2022
Date of Peer Review: Jun 18, 2022
Date of Acceptance: Aug 02, 2022
Date of Publishing: Nov 01, 2022
AUTHOR DECLARATION:
• Financial or Other Competing Interests: None
• Was informed consent obtained from the subjects involved in the study? No
• For any images presented appropriate consent has been obtained from the subjects. NA
PLAGIARISM CHECKING METHODS:
• Plagiarism X-checker: Mar 15, 2022
• Manual Googling: Jul 28, 2022
• iThenticate Software: Aug 01, 2022 (12%)
ETYMOLOGY: Author Origin
- Emerging Sources Citation Index (Web of Science, thomsonreuters)
- Index Copernicus ICV 2017: 134.54
- Academic Search Complete Database
- Directory of Open Access Journals (DOAJ)
- Embase
- EBSCOhost
- Google Scholar
- HINARI Access to Research in Health Programme
- Indian Science Abstracts (ISA)
- Journal seek Database
- Popline (reproductive health literature)
- www.omnimedicalsearch.com