In emergency medicine, tough clinical questions arise — and answers are needed fast. A junior doctor might urgently ask in the resuscitation room: “What is the target blood pressure and preferred agent for acute aortic dissection?”

Artificial intelligence (AI) is now transforming how medical professionals and students answer clinical questions and conduct literature searches. By drawing from vast medical databases, AI delivers reliable, evidence-based answers in seconds, making it an invaluable tool for physicians.

This article explores how AI enhances clinical question answering and literature searches, outlines its benefits and risks, introduces user-friendly tools, and emphasizes its role as a decision-support system.

AI in Clinical Question Answering

Clinical questions, such as those about drug dosages, diagnostic challenges, or treatment plans, are commonly encountered in healthcare. Before AI became widely used, tools like UpToDate were vital for medical doctors, providing a trusted reference for clinical queries.

A study indicated that hospitals using UpToDate may experience shorter patient stays, lower mortality rates, and improved quality of care compared to those without it, underscoring the importance of quick access to reliable information.1 AI advances this by using natural language processing (NLP) and machine learning to deliver rapid, evidence-based answers from clinical guidelines and peer-reviewed journals. 

Key benefits include: 

• Speed: 

  • Delivers answers in seconds which is crucial in time-sensitive settings like emergency departments. 

• Accessibility: 

  • Available 24/7 and reduce the need to consult senior colleagues for advice

• Comprehensive search:

  • Scans multiple sources, ensuring a broader evidence base than single-database tools.

• Summarization: 

  • Generate concise article summaries and saves time. 

Despite there are numerous benefits, multiple risks exist:

• Misinterpretation of vague question

  • AI may misinterpret vague questions and unclear queries leading to incorrect answers. 

• Biased or poor quality sources

  • Tools trained on biased data or low quality datasets may produce skewed recommendations, particularly for underrepresented groups.

• Fabricated information

  • Generative AI can produce fake references and knowledge that users may not be able to differentiate. Answers without considering the broader context could lead to oversights. 

• Deskilling of clinical reasoning 

  • Overreliance on AI may weaken clinicians' reasoning skills and critical thinking, which are essential for medical training. 

Comparisons of different AI tools

Several AI tools are tailored for clinical questions and literature searches, ideal for healthcare workers beyond general ones like ChatGPT or Grok:

1. UpToDate with AI enhancement2

   • With AI-improved search and recommendation capabilities, it excels in query interpretation but it requires a subscription.

2. Consensus3

   • It searches and analyses peer reviewed research papers and lists the references for the answers. It also categorises the type of reference such as meta-analysis, whether it is from a rigorous journal or highly cited. However, it is subscription-based and the free trial is restricted. 

3. Scispace copilot4

   • Tailored for researchers, clinicians and biomedical professionals. It is great for handling large volumes of literature but may lack advanced clinical decision support. 

4. Elicit5

   • It shows the process of gathering and screening sources with details included. Each paper was also reviewed for key aspects that mattered most to the research questions. However, the speed is slower compared to other tools and it is limited to publications in Semantic Scholar and PubMed.

5. OpenEvidence6

   • It was launched from the Mayo Clinic Platform Accelerate program to incorporate innovative technologies into healthcare. The company claims it is the first AI ever to achieve a perfect score of 100% on the United States Medical Licensing Examination (USMLE). The interface is easy to use and is currently available for free to verified medical professionals.

Best practices for doctors

Despite it is convenient to use, there are some tips to use them effectively:

1. Ask clear questions and make specific queries

   • Well-structured prompts and questions yield better results and it is recommended to avoid vague phrasing. 

   • Bad prompt (vague):

     1. “Tell me about aortic dissection.” (Too broad — may return background info, pathophysiology, or surgical details, but not what’s immediately useful in the ED.)

   • Good prompt (specific):

     1. “What is the recommended target systolic blood pressure and preferred IV antihypertensive agent for acute aortic dissection in the emergency department, according to current guidelines?” (Clear, structured, and time-critical — AI is more likely to return concise, guideline-based management advice relevant to the ED setting.)

2. Verify information and use combination of tools 

   • Generative AI can sometimes produce inaccurate or fabricated references, which may be difficult to spot without careful review

   • Users are responsible to cross-check AI outputs with primary sources and different tools

   • Many AI tools now provide citations with their responses, allowing users to cross-check sources and verify the accuracy of the information.

3. Protect data and privacy

   • Queries should not include patient personal identifiers and it is necessary to comply with privacy regulations.

Conclusion

AI is definitely a game-changer for answering clinical questions and conducting literature searches. It empowers doctors to tackle complex medical queries and research efficiently. However, these tools should be used as decision-support, but not replace human judgement with final clinical decisions made by medical professionals to ensure patient safety and optimal care. 

In our future articles, we will share the latest development of chatbot in Hospital Authority and other tips of using AI in research. Stay tuned!