Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore

Mapping the use of artificial intelligence in medical education: a scoping review

INTRODUCTION

The integration of artificial intelligence (AI) in healthcare has transformed clinical practices and medical education, with technologies like diagnostic algorithms and clinical decision support increasingly incorporated into curricula. However, there is still a gap in preparing future physicians to use these technologies effectively and ethically.

OBJECTIVE

This scoping review maps the integration of artificial intelligence (AI) in undergraduate medical education (UME), focusing on curriculum development, student competency enhancement, and institutional barriers to AI adoption.

MATERIALS AND METHODS

A comprehensive search in PubMed, Scopus, and BIREME included articles from 2019 onwards, limited to English and Spanish publications on AI in UME. Exclusions applied to studies focused on postgraduate education or non-medical fields. Data were analyzed using thematic analysis to identify patterns in AI curriculum development and implementation.

RESULTS

A total of 34 studies were reviewed, representing diverse regions and methodologies, including cross-sectional studies, narrative reviews, and intervention studies. Findings revealed a lack of standardized AI curriculum frameworks and notable global discrepancies. Key elements such as ethical training, collaborative learning, and digital competence were identified as essential, with an emphasis on transversal skills that support AI as a tool rather than a standalone subject.

CONCLUSIONS

This review underscores the need for a standardized, adaptable AI curriculum in UME that prioritizes transversal skills, including digital competence and ethical awareness, to support AI's gradual integration. Embedding AI as a practical tool within interdisciplinary, patient-centered frameworks fosters a balanced approach to technology in healthcare. Further regional research is recommended to develop frameworks that align with cultural and educational needs, ensuring AI integration in UME promotes both technical and ethical competencies.

Studying the Potential Effects of Artificial Intelligence on Physician Autonomy: Scoping Review

BACKGROUND

Physician autonomy has been found to play a role in physician acceptance and adoption of artificial intelligence (AI) in medicine. However, there is still no consensus in the literature on how to define and assess physician autonomy. Furthermore, there is a lack of research focusing specifically on the potential effects of AI on physician autonomy.

OBJECTIVE

This scoping review addresses the following research questions: (1) How do qualitative studies conceptualize and assess physician autonomy? (2) Which aspects of physician autonomy are addressed by these studies? (3) What are the potential benefits and harms of AI for physician autonomy identified by these studies?

METHODS

We performed a scoping review of qualitative studies on AI and physician autonomy published before November 6, 2023, by searching MEDLINE and Web of Science. To answer research question 1, we determined whether the included studies explicitly include physician autonomy as a research focus and whether their interview, survey, and focus group questions explicitly name or implicitly include aspects of physician autonomy. To answer research question 2, we extracted the qualitative results of the studies, categorizing them into the 7 components of physician autonomy introduced by Schulz and Harrison. We then inductively formed subcomponents based on the results of the included studies in each component. To answer research question 3, we summarized the potentially harmful and beneficial effects of AI on physician autonomy in each of the inductively formed subcomponents.

RESULTS

The search yielded 369 studies after duplicates were removed. Of these, 27 studies remained after titles and abstracts were screened. After full texts were screened, we included a total of 7 qualitative studies. Most studies did not explicitly name physician autonomy as a research focus or explicitly address physician autonomy in their interview, survey, and focus group questions. No studies addressed a complete set of components of physician autonomy; while 3 components were addressed by all included studies, 2 components were addressed by none. We identified a total of 11 subcomponents for the 5 components of physician autonomy that were addressed by at least 1 study. For most of these subcomponents, studies reported both potential harms and potential benefits of AI for physician autonomy.

CONCLUSIONS

Little research to date has explicitly addressed the potential effects of AI on physician autonomy and existing results on these potential effects are mixed. Further qualitative and quantitative research is needed that focuses explicitly on physician autonomy and addresses all relevant components of physician autonomy.

Health workers' adoption of digital health technology in low- and middle-income countries: a systematic review and meta-analysis

Objective

To conduct a systematic review and meta-analysis of the facilitators of and barriers to the acceptance and use of digital health technology by health workers in low- and middle-income countries.

Methods

We searched several databases for relevant articles published until 25 April 2024. We extracted data on four unified theories of acceptance and use of technology factors (performance expectancy, effort expectancy, social influence and facilitating conditions) and six additional factors (attitude, habit, incentive, risk, trust and self-efficacy); how these affected the outcomes of behavioural intention and actual use; and the strength of association if reported. We conducted a meta-analysis of the quantitative studies.

Findings

We reviewed 36 publications, 20 of which were included in our meta-analysis. We observed that performance expectancy was the most frequently reported facilitator (in 21 studies; 58.3%) and that lack of facilitating conditions was the most cited barrier (10; 27.8%). From our meta-analysis, trust (r = 0.53; 95% confidence interval, CI: 0.18 to 0.76) and facilitating conditions (r = 0.42; 95% CI: 0.27 to 0.55) were the leading facilitators of behavioural intention and actual use, respectively. We identified concerns with performance expectancy (r = -0.14, 95% CI: -0.29 to 0.01) as the primary barrier to both outcomes.

Conclusion

Our approach of clustering the facilitators of and barriers to the acceptance and use of digital health technology from the perspective of health workers highlighted the importance of creating an enabling ecosystem. Supportive infrastructure, tailored training programmes and incentive policies should be incorporated in the implementation of digital health programmes in low- and middle-income countries.

The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiological data for predicting AMR outbreaks. Although AI/ML are relatively new fields, numerous case studies offer substantial evidence of their successful application in predicting AMR outbreaks with greater accuracy. These models can provide insights into the discovery of novel antimicrobials, the repurposing of existing drugs, and combination therapy through the analysis of their molecular structures. In addition, AI-based clinical decision support systems in real-time guide healthcare professionals to improve prescribing of antibiotics. The review also outlines how can AI improve AMR surveillance, analyze resistance trends, and enable early outbreak identification. Challenges, such as ethical considerations, data privacy, and model biases exist, however, the continuous development of novel methodologies enables AI/ML to play a significant role in combating AMR.

Artificial intelligence, medications, pharmacogenomics, and ethics

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing various scientific and clinical disciplines including pharmacogenomics (PGx) by enabling the analysis of complex datasets and the development of predictive models. The integration of AI and ML with PGx has the potential to provide more precise, data-driven insights into new drug targets, drug efficacy, drug selection, and risk of adverse events. While significant effort to develop and validate these tools remain, ongoing advancements in AI technologies, coupled with improvements in data quality and depth is anticipated to drive the transition of these tools into clinical practice and delivery of individualized treatments and improved patient outcomes. The successful development and integration of AI-assisted PGx tools will require careful consideration of ethical, legal, and social issues (ELSI) in research and clinical practice. This paper explores the intersection of PGx with AI, highlighting current research and potential clinical applications, and ELSI including privacy, oversight, patient and provider knowledge and acceptance, and the impact on patient-provider relationship and new roles.

Doctors' perception on the ethical use of AI-enabled clinical decision support systems for antibiotic prescribing recommendations in Singapore

Objectives

The increased utilization of Artificial intelligence (AI) in healthcare changes practice and introduces ethical implications for AI adoption in medicine. We assess medical doctors' ethical stance in situations that arise in adopting an AI-enabled Clinical Decision Support System (AI-CDSS) for antibiotic prescribing decision support in a healthcare institution in Singapore.

Methods

We conducted in-depth interviews with 30 doctors of varying medical specialties and designations between October 2022 and January 2023. Our interview guide was anchored on the four pillars of medical ethics. We used clinical vignettes with the following hypothetical scenarios: (1) Using an antibiotic AI-enabled CDSS's recommendations for a tourist, (2) Uncertainty about the AI-CDSS's recommendation of a narrow-spectrum antibiotic vs. concerns about antimicrobial resistance, (3) Patient refusing the "best treatment" recommended by the AI-CDSS, (4) Data breach.

Results

More than half of the participants only realized that the AI-enabled CDSS could have misrepresented non-local populations after being probed to think about the AI-CDSS's data source. Regarding prescribing a broad- or narrow-spectrum antibiotic, most participants preferred to exercise their clinical judgment over the AI-enabled CDSS's recommendations in their patients' best interest. Two-thirds of participants prioritized beneficence over patient autonomy by convincing patients who refused the best practice treatment to accept it. Many were unaware of the implications of data breaches.

Conclusion

The current position on the legal liability concerning the use of AI-enabled CDSS is unclear in relation to doctors, hospitals and CDSS providers. Having a comprehensive ethical legal and regulatory framework, perceived organizational support, and adequate knowledge of AI and ethics are essential for successfully implementing AI in healthcare.