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Design+ AI’s role in medical history taking

enhancing various medical processes, from patient record- medical history-taking by addressing training-inference
keeping to real-time monitoring and decision-making. gaps and improving question relevance during patient
For instance, AI-supported systems such as anesthesia interactions. In addition, virtual standardized patients
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information management systems facilitate automated utilizing AI for natural language understanding and rule-
record-keeping, predictive analytics, and patient based dialog management have shown promise in medical
monitoring, which have proven to be vital in improving education, enabling students to practice history-taking
4
anesthesia management and patient safety. Furthermore, with high conversational fidelity. 13
AI applications in anesthesiology have expanded to include Furthermore, the feasibility and acceptability of
event prediction, ultrasound guidance, pain management, conversational AI systems for medical interviewing have
and operating room logistics, underscoring its multifaceted been positively received by patients, suggesting that these
role in modern. 5 tools can aid clinicians in understanding patient health

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AI’s integration into surgical practices has also been and identifying risk factors. However, comprehensive
transformative, particularly in pre-operative planning, knowledge-grounded dialogue systems for medical
intraoperative guidance, and the use of surgical robots. conversations are still in the early stages, and there is a need
AI technologies have been instrumental in improving the for more robust systems capable of accurately extracting
accuracy and efficiency of surgical procedures through and utilizing heterogeneous medical information. 15
enhanced imaging, navigation, and real-time decision The research analyzed reflects medical history only to
support systems. In addition, AI-driven intraoperative a limited extent. The main focus of the researched use of
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decision support systems aim to augment the information AI is on technical analysis, e.g., image interpretation or
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available to surgeons, accelerate pathology analysis, and support in surgical matters.
recommend surgical steps, thus potentially improving
patient outcomes. 7 Based on our literature, we identified a significant
research gap in the widespread acceptance and practical
Beyond anesthesiology and surgery, AI has found implementation of AI in medical anamnesis. Studies show
applications in various medical disciplines, including that while there is optimism about AI’s role in medicine,
dentistry and brain care. In dentistry, AI has enhanced there are concerns about its reliability and integration
diagnostic accuracy and treatment planning through into clinical practice. Physicians and medical students
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8
advanced data analysis and visualization techniques. In generally have positive attitudes toward AI but lack
brain care, AI has been utilized for diagnosis, surgical practical experience and knowledge, highlighting the need
planning, and post-operative assessment, showcasing for comprehensive AI education in medical curricula.
its ability to handle complex medical data and provide Moreover, there is a noticeable reluctance among health-
meaningful insights for clinical decision-making. These care providers to trust AI systems over human judgment,
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advancements highlight AI’s potential to revolutionize underscoring the need for further research into improving
medical practices across various specialties. AI’s reliability and addressing ethical and regulatory
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Despite the promising applications, several challenges concerns. Future research should focus on bridging these
hinder the widespread adoption of AI in medical gaps to enhance AI’s integration into clinical workflows,
anamnesis. Data quality and quantity, technical limitations, ensuring both clinicians and patients benefit from AI
and ethical and legal concerns are significant obstacles advancements in medical anamnesis.
that need to be addressed to ensure the reliable and However, to realize the benefits of using AI in the
secure implementation of AI systems. Overcoming these clinical environment, it is also necessary to consider the
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barriers requires the development of robust guidelines acceptance of the technology by the users. Can existing
for ethical AI use, improvement in AI system reliability, technology acceptance models be used to successfully
and certification of health data precision and security. As evaluate AI applications in medical history, i.e., in the close
AI continues to evolve, future research should focus on doctor–patient relationship? (Research question 1, RQ1).
expanding its applications, enhancing data security, and It is also important to examine the deeper attitudes of
integrating AI into medical education and training to fully users. What concerns do physicians have about using AI to
realize its potential in medical anamnesis. 11
support part of the medical history taking? (RQ2). We can
In the realm of AI-driven medical history-taking also look at what benefits users expect from AI in medical
and patient dialogue, significant advancements have history taking (RQ3). The combined answer to RQ2 and
been made, yet there remain gaps that warrant further RQ3 leads to the expected intention to use and therefore
exploration. AI systems such as dialogue-contextualized to RQ4. The study design and the research questions are
re-ranking models have been developed to enhance summarized in Figure 1.

Volume 2 Issue 1 (2025) 2 doi: 10.36922/dp.7675