Artificial intelligence in healthcare in developing nations: The beginning of a transformative journey

Mahajan, Abhishek; Vaidya, Tanvi; Gupta, Anurag; Rane, Swapnil; Gupta, Sudeep

doi:10.4103/crst.crst_50_19

Cited by 69 publications

(21 citation statements)

References 32 publications

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“…[40][41][42][43] and have highlighted the existing gaps in implementation and acceptance of AI in health care relevant to various cultural and economic backgrounds. 44 Users in Mexico reported that clinics who lack expertise in a particular subspecialty would benefit, as would medical students and residents. 19 In China, one study demonstrated that an AI-based tool promoted the standardization and personalization of treatment, 29 and another suggested that a majority of Chinese users approved the quality (86.3%) and comprehensibility (88.2%) of treatment options and rationale offered by AIbased tools.…”

Section: Discussionmentioning

confidence: 99%

Physicians' Perceptions and Expectations of an Artificial Intelligence-Based Clinical Decision Support System in Cancer Care in an Underserved Setting

et al. 2022

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Objectives Artificial intelligence (AI) tools are being increasingly incorporated into health care. However, few studies have evaluated users' expectations of such tools, prior to implementation, specifically in an underserved setting. Methods We conducted a qualitative research study employing semistructured interviews of physicians at The Instituto do Câncer do Ceará, Fortaleza, Brazil. The interview guide focused on anticipated, perceived benefits and challenges of using an AI-based clinical decision support system tool, Watson for Oncology. We recruited physician oncologists, working full or part-time, without prior experience with any AI-based tool. The interviews were taped and transcribed in Portuguese and then translated into English. Thematic analysis using the constant comparative approach was performed. Results Eleven oncologists participated in the study. The following overarching themes and subthemes emerged from the analysis of interview transcripts: theme-1, “general context” including (1) current setting, workload, and patient population and (2) existing challenges in cancer treatment, and theme-2, “perceptions around the potential use of an AI-based tool,” including (1) perceived benefits and (2) perceived challenges. Physicians expected that the implementation of an AI-based tool would result in easy access to the latest clinical recommendations, facilitate standardized cancer care, and allow it to be delivered with greater confidence and efficiency. Participants had several concerns such as availability of innovative treatments in resource-poor settings, treatment acceptance, trust, physician autonomy, and workflow disruptions. Conclusion This study provides physicians' anticipated perspectives, both benefits and challenges, about the use of an AI-based tool in cancer treatment in a resource-limited setting.

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Section: Discussionmentioning

confidence: 99%

Physicians' Perceptions and Expectations of an Artificial Intelligence-Based Clinical Decision Support System in Cancer Care in an Underserved Setting

et al. 2022

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“…There may be sharp differences in how different stakeholders perceive the changed distribution of benefits and other consequences when established patterns are disrupted [ 4 ]. Moreover, in lower-income settings marked by power inequalities, negative effects may be pronounced [ 50 ]. Recognizing these concerns, alongside the benefits, is needed to optimally achieve the potential of AI solutions.…”

Section: Discussionmentioning

confidence: 99%

Using Artificial Intelligence for High-Volume Identification of Silicosis and Tuberculosis: A Bio-Ethics Approach

Spiegel

Ehrlich

Yassi

et al. 2021

Annals of Global Health

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Although Artificial Intelligence (AI) is being increasingly applied, considerable distrust about introducing “disruptive” technologies persists. Intrinsic and contextual factors influencing where and how such innovations are introduced therefore require careful scrutiny to ensure that health equity is promoted. To illustrate one such critical approach, we describe and appraise an AI application – the development of computer assisted diagnosis (CAD) to support more efficient adjudication of compensation claims from former gold miners with occupational lung disease in Southern Africa. In doing so, we apply a bio-ethical lens that considers the principles of beneficence, non-maleficence, autonomy and justice and add explicability as a core principle. We draw on the AI literature, our research on CAD validation and process efficiency, as well as apprehensions of users and stakeholders. Issues of concern included AI accuracy, biased training of AI systems, data privacy, impact on human skill development, transparency and accountability in AI use, as well as intellectual property ownership. We discuss ways in which each of these potential obstacles to successful use of CAD could be mitigated. We conclude that efforts to overcoming technical challenges in applying AI must be accompanied from the onset by attention to ensuring its ethical use.

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“…g. Infrastructure: Use of DL requires a robust infrastructure which may not be available everywhere. h. Lack of trained personnel and expertise and lack of awareness about collaboration for implementation of AI projects (25).…”

Section: Challenges and Limitations Of DLmentioning

confidence: 99%

Editorial: The use of deep learning in mapping and diagnosis of cancers

Mahajan

Chakrabarty

2022

Front. Oncol.

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Editorial on the Research TopicThe use of deep learning in mapping and diagnosis of cancers Deep Learning (DL) is a subset and an augmented version of Machine Learning (ML), which in turn is a subgroup of Artificial Intelligence (AI), that uses layers of neural networks, similar to human brain, for performing complex tasks quickly and accurately. AI can recognize patterns in a large volume of data and extract characteristics imperceptible to the human eye (1). Convolutional Neural Network (CNN) is the most commonly used network of DL, which contains multiple layers, with weighted connections between neurons that are trained iteratively to improve performance. DL can be supervised or unsupervised, but most of the practical uses of DL in cancer has been with supervised learning where labelled images are used for data training (2). Despite the growing number of uses of DL in cancer mapping and diagnosis, there are uncharted territories in DL which remain to be explored to utilize it to its full capacity. Also, in spite of the revolution in cancer research that DL has ushered in, there are a lot of challenges to overcome, before DL can be widely used and accepted in every corner of the world. Role of DL in oncologyThere has been an unprecedented surge in DL based research in oncology due to the availability of big data, powerful hardware and robust algorithms. Screening and diagnosis of cancer, prediction of treatment response, and survival outcome and recurrence prediction, are the various roles of ML and DL in cancer management. AI algorithms integrated with clinical decision support (CDS) tools can automatically mine electronic health record (EHR) and identify cohort that would benefit maximum from Frontiers in Oncology frontiersin.org 01

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Artificial intelligence in healthcare in developing nations: The beginning of a transformative journey

Cited by 69 publications

References 32 publications

Physicians' Perceptions and Expectations of an Artificial Intelligence-Based Clinical Decision Support System in Cancer Care in an Underserved Setting

Physicians' Perceptions and Expectations of an Artificial Intelligence-Based Clinical Decision Support System in Cancer Care in an Underserved Setting

Using Artificial Intelligence for High-Volume Identification of Silicosis and Tuberculosis: A Bio-Ethics Approach

Editorial: The use of deep learning in mapping and diagnosis of cancers

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