Artificial intelligence in healthcare: opportunities and risk for future

Sunarti, Sri; Rahman, Ferry Fadzlul; Naufal, Muhammad Farhan; Risky, Muhammad; Febriyanto, Kresna; Hassan, Rusni

doi:10.1016/j.gaceta.2020.12.019

Cited by 135 publications

(65 citation statements)

References 13 publications

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“…Since AI has shown itself to be a "risky" technology across myriad domains, such as self-driving cars [3], healthcare [74], and replicating biases in data [58], we begin our discussion by looking at how participants' attitudes toward risk factored into their ratings of different interfaces for various AI products and found three categories of insights:…”

Section: Results: Cognitive Style Impacted Guideline Responsesmentioning

confidence: 99%

Human-AI Interaction for Diverse Humans: What Cognitive Style Disaggregation Reveals

Anderson¹,

Li²,

Vorvoreanu³

et al. 2021

Preprint

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AI) is becoming more pervasive through all levels of society, trying to help us be more productive. Research like Amershi et al.'s 18 guidelines for human-AI interaction aim to provide high-level design advice, yet little remains known about how people react to Applications or Violations of the guidelines. This leaves a gap for designers of human-AI systems applying such guidelines, where AI-powered systems might be working better for certain sets of users than for others, inadvertently introducing inclusiveness issues. To address this, we performed a secondary analysis of 1,016 participants across 16 experiments, disaggregating their data by their 5 cognitive problem-solving styles from the Gender-inclusiveness Magnifier (GenderMag) method and illustrate different situations that participants found themselves in. We found that across all 5 cogniive style spectra, although there were instances where applying the guidelines closed inclusiveness issues, there were also stubborn inclusiveness issues and inadvertent introductions of inclusiveness issues. Lastly, we found that participants' cognitive styles not only clustered by their gender, but they also clustered across different age groups.CCS Concepts: • Human-centered computing → User studies; • Computing methodologies → Intelligent agents.

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Section: Results: Cognitive Style Impacted Guideline Responsesmentioning

confidence: 99%

Human-AI Interaction for Diverse Humans: What Cognitive Style Disaggregation Reveals

Anderson¹,

Li²,

Vorvoreanu³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In general, predictive analytics makes use of supervised ML algorithms. These strategies either categorize data into discrete categories (classification methods) or predict value (regression methodology) [50] [51]. The DL approach progressively extracts higher-level information from the input image using several processing layers of linear and nonlinear transformations.…”

Section: Methodsmentioning

confidence: 99%

Diagnosis of Gastric Cancer Using Machine Learning Techniques in Healthcare Sector: A Survey

Jamil

2022

IJCAI

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Many researchers are trying hard to minimize the incidence of cancers, mainly GC. For GC, the five-year survival rate is generally 5-25%, but for EGC, it is almost up to 90%. Among the cancers, GC is very deadly. It is difficult for doctors to assess its threat to patients as it requires years of medical practice and rigorous testing. The healthcare sector has benefitted from AI for the early diagnosis or classification of GC. However, the current AI-based techniques need further improvement in clinical testing. Heterogeneous GC characterization requires more optimized methods for early detection of GC because of its type and severity. Hence, it is essential to investigate this area further and develop more optimized approaches for early diagnosis. Early detection will increase the chances of successful treatments. In this study, we have conducted a literature survey detailing the role of AI in the healthcare sector for GC diagnosis. We discuss basic principles, advantages and disadvantages, training and testing of data, and integration of applications like DSS, CDSS, KDD, ML, DM, BD, and DL, and their relevance to the healthcare industry. The study focuses on the application of ML techniques used in the diagnosis of GC. This review paper also introduces DM techniques, their application in the healthcare industry, limitations, roles, and operational challenges. These assist pathologists in helping minimize their workload while increasing diagnostic accuracy. These techniques will further assist medical practitioners with their decision-making process. Povzetek: Raziskava o uporabi tehnik strojnega učenja pri diagnostiki raka želodca v zdravstvenem sektorju.

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“…Using top-level technologies to build strong and reliable healthcare solutions has been one of the major achievements. AI is being widely used in healthcare to prevent, diagnose, and treat diseases and act as a helping hand to medical professionals [ 4 ]. For example, computer vision is widely used in medical imaging.…”

Section: Recent Ai Contributions In Healthcarementioning

confidence: 99%

“…Consolidation or merging of healthcare entities is, unfortunately, a trend that does not seem to be stopping soon and will lead to an increase in the healthcare prices and dominance of a few large and prominent players in the healthcare markets in the coming years. This consolidation is believed to be a direct result of the impact of the Affordable Care Act [ 4 ]. Thirdly, with the increasing advent of big data and other technologies in healthcare, such as AI/ML, there is a higher focus on ensuring patient data confidentiality.…”

Section: Introductionmentioning

confidence: 99%

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

Pandya

Thakur

Saxena

et al. 2021

Sensors

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The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

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Artificial intelligence in healthcare: opportunities and risk for future

Cited by 135 publications

References 13 publications

Human-AI Interaction for Diverse Humans: What Cognitive Style Disaggregation Reveals

Human-AI Interaction for Diverse Humans: What Cognitive Style Disaggregation Reveals

Diagnosis of Gastric Cancer Using Machine Learning Techniques in Healthcare Sector: A Survey

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

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