Challenges and Ethical Considerations to Successfully Implement
                    Artificial Intelligence in Clinical Medicine and Neuroscience: a Narrative
                    Review

Objective Obtain clinicians’ perspectives on early warning scores (EWS) use within context of clinical cases. Material and Methods We developed cases mimicking sepsis situations. De-identified data, synthesized physician notes, and EWS representing deterioration risk were displayed in a simulated EHR for analysis. Twelve clinicians participated in semi-structured interviews to ascertain perspectives across four domains: (1) Familiarity with and understanding of artificial intelligence (AI), prediction models and risk scores; (2) Clinical reasoning processes; (3) Impression and response to EWS; and (4) Interface design. Transcripts were coded and analyzed using content and thematic analysis. Results Analysis revealed clinicians have experience but limited AI and prediction/risk modeling understanding. Case assessments were primarily based on clinical data. EWS went unmentioned during initial case analysis; although when prompted to comment on it, they discussed it in subsequent cases. Clinicians were unsure how to interpret or apply the EWS, and desired evidence on its derivation and validation. Design recommendations centered around EWS display in multi-patient lists for triage, and EWS trends within the patient record. Themes included a “Trust but Verify” approach to AI and early warning information, dichotomy that EWS is helpful for triage yet has disproportional signal-to-high noise ratio, and action driven by clinical judgment, not the EWS. Conclusions Clinicians were unsure of how to apply EWS, acted on clinical data, desired score composition and validation information, and felt EWS was most useful when embedded in multi-patient views. Systems providing interactive visualization may facilitate EWS transparency and increase confidence in AI-generated information.

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AI-Driven Decision-Making Applications in Pharmaceutical Sciences

Yingngam,

Navabhatra,

Sillapapibool

2024

Advances in Media, Entertainment, and the Arts

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This chapter explores AI's influence on pharmaceutical sciences, highlighting its enhancement of traditional design methodologies. It explores AI's transformational role in key sectors, including drug discovery, virtual screening, and drug formulation development. AI's ability to efficiently identify potential drug candidates from large chemical libraries and its use of optimization algorithms in the selection of suitable excipients and dosage forms are discussed. The chapter also emphasizes AI's significance in improving pharmaceutical manufacturing processes through parameter refinement, quality outcome prediction, and real-time anomaly detection. The integration of traditional design methods with AI ensures robust, reliable, AI-driven processes that are compliant with regulations. In conclusion, the chapter highlights the potential of AI in pharmaceutical sciences and the importance of its integration with traditional design methods. This approach empowers scientists to innovate, speed up drug development, and improve patient outcomes.

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Challenges and Ethical Considerations to Successfully Implement Artificial Intelligence in Clinical Medicine and Neuroscience: a Narrative Review

Cited by 3 publications

References 55 publications

Convergence of Diverse Expertise: A Multidisciplinary Training on the Ethics of Artificial Intelligence in Healthcare Technology and Research

Convergence of Diverse Expertise: A Multidisciplinary Training on the Ethics of Artificial Intelligence in Healthcare Technology and Research

Clinician perspectives on how situational context and augmented intelligence design features impact perceived usefulness of sepsis prediction scores embedded within a simulated electronic health record

AI-Driven Decision-Making Applications in Pharmaceutical Sciences

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