Introduction to AI in clinical practice
Artificial intelligence is rapidly moving from a theoretical concept to a practical tool integrated into every step of the clinical care pathway. From management algorithms and test selection to diagnostic reasoning, AI is helping clinicians manage information overload and enhance the quality of their decision-making (Frontiers).
This article provides a guide for UK clinicians and students on how to leverage these powerful new capabilities—including automated documentation, investigation prioritization, and differential diagnosis generation. Crucially, it clarifies the scope and purpose of different AI tools, emphasizing that features like the iatroX Brainstorm are intended for structured, educational exploration rather than direct, live patient management.
AI for clinical management
One of the most immediate impacts of AI is its ability to streamline clinical workflows and reduce administrative burden.
Automating routine tasks
Tools that automate documentation are at the forefront of this shift. Ambient scribes such as Tortus, Tandem’s Accurx Scribe, and Heidi Health can listen to patient consultations and auto-generate clinical notes, freeing clinicians from manual note-taking to focus on the patient (BioMed Central). Beyond scribing, AI-driven alerts for changes in vital signs and risk stratification algorithms are helping to reduce the cognitive load on busy ward staff (PMC).
Reducing diagnostic errors
AI can also play a crucial role in improving diagnostic accuracy. Advanced systems can aggregate a patient's entire history—including lab results, imaging, and past consultations—into a concise summary, ensuring no critical piece of information is missed (PMC). To be truly effective, this decision support must be integrated directly within Electronic Health Records (EHRs), delivering evidence in-context rather than as disparate results that add to a clinician's workload (JAMA Network).
AI for investigation prioritization
AI is proving highly effective at optimising the use of diagnostic resources, ensuring the right tests are done for the right patients at the right time.
Test selection algorithms
Using Bayesian logic and machine learning models, new algorithms can analyse a patient's presenting symptoms and history to calculate pre-test probabilities for various conditions. This helps prioritise the most appropriate imaging and lab tests, improving cost-effectiveness and reducing the number of unnecessary investigations (The Guardian).
Resource allocation
At a population level, AI can identify high-risk patients for early intervention. A prime example is the use of cancer-detection AI in UK GP practices, which has been shown to boost diagnosis rates by as much as 8% by flagging patients who would benefit most from urgent investigation (The Guardian).
AI for differential diagnosis
One of the most exciting—and high-stakes—applications of AI is in generating differential diagnoses (DDx).
Generating ranked lists
Large Language Models (LLMs) like ChatGPT have demonstrated an ability to propose DDx lists that are comparable to a non-expert physician's preliminary assessment (medinform.jmir.org). However, studies show that dedicated, medically-trained systems like Google's AMIE still show superior top-1 accuracy over generalist models like GPT-4 in head-to-head evaluations, while bespoke "AI orchestrators" have achieved up to 85% diagnostic accuracy at a 20% lower cost compared to doctors in some settings (Nature, TIME).
Structured thinking with Brainstorm
The iatroX Brainstorm feature is designed not as a diagnostic tool, but as an educational one to support structured thinking. It provides a scaffolded approach to hypothesis generation and concept mapping, making it an ideal tool for trainees, students, and multidisciplinary teams preparing for case reviews or journal clubs. By using the iatroX Brainstorm feature, users can explore potential diagnoses in a safe, educational environment, ensuring that any AI-generated suggestions are always cross-checked with primary literature and senior clinical judgment.
Rapid Q&A for reference & learning
For more targeted queries, instant Q&A tools provide rapid access to evidence. The Ask iatroX feature is designed to retrieve evidence-linked answers to specific clinical questions in under 30 seconds. This capability is intended to support "point-of-study" learning rather than live patient decisions. A powerful way to use the tool is to bookmark common queries (e.g., “sepsis bundle criteria”) and use Ask iatroX during quieter moments on-call or after a clinic to consolidate learning from cases seen during the day.
Limitations & considerations
While the potential of AI is immense, clinician oversight remains essential to mitigate risks.
- Accuracy & omissions: Meta-analyses report general AI diagnostic accuracy at around 52%; expert human oversight is therefore mandatory to catch missing information or misordered priorities (Nature).
- Bias & equity: AI models may under-represent minority populations in their training data. Clinicians must always validate AI outputs against guidelines and their own clinical judgment to ensure equitable care (Frontiers).
- Privacy & compliance: Before using any AI tool, ensure it complies with GDPR, MHRA regulations, and local Trust policies for data protection and patient safety (arXiv).
Future outlook & integration with CDSS
The next evolution will involve deeper integration between AI systems. Standards like the FHIR-CDS API will allow AI-generated observations (e.g., from a scribe) to be funnelled directly into comprehensive CDSS workflows, like iatroX’s core platform, for real-time analysis. We expect a shift toward "hybrid intelligence" models, where systems guide clinicians through branching workflows but require human confirmation at each key decision point.
Conclusion & action steps
AI tools offer a powerful new layer of support for clinical management, investigation, and diagnostic reasoning. To leverage them safely and effectively:
- Pilot & evaluate: Implement educational tools like iatroX Brainstorm and Ask iatroX in simulation environments and teaching sessions. Track user feedback and accuracy before considering any wider clinical rollout.
- Establish a governance framework: Create multidisciplinary oversight committees to review AI suggestions, maintain audit logs, and ensure tools are updated in line with new evidence.
- Promote continuous learning: Integrate AI-guided case discussions into regular teaching rounds to foster proficiency and, most importantly, critical appraisal skills among all staff.
By following this structured approach, clinicians can harness the power of AI to enhance their structured thinking and decision-making, while always maintaining the primacy of their own expert judgment.