Bayesian networks: Making the most of a history

History taking is said to be the “most powerful and sensitive and most versatile instrument available to the physician”,1 but what use is a good tool if it is not known how to use it? Whilst studies show that history- taking alone can provide sufficient information for diagnosis in 75% of patients, they also show that medical students are far from achieving this figure.2 In our experience, as final year medical students at Imperial College London, we frequently clerk patients alone without supervision during our clinical rotations. As a result we find it difficult to measure how successful we have been in generating a differential diagnosis and often miss the opportunity for reflective learning. What use is a good tool if it is not known how to use it? To make impactful change we should anticipate social shifts. Given the growing importance and potential of artificial intelligence (AI) in medicine, we feel there is a need to incorporate AI in the education of the future generation of doctors.3 As software develops and availability increases, medical students are likely to be the future adopters of AI systems, and thus their incorporation into curricula is pivotal to building trust in AI in the medical field. There is a need to incorporate AI in the education of the future generation of doctors. Fortunately, we are at a time when companies are creating AI engines that rely on the statistical principle of Bayesian inference to aid history taking and diagnosis.4 Bayesian engines draw information from large databases containing retrospective data on the epidemiology, risk factors and clinical features of a set of conditions. Using a patient’s data they calculate conditional probabilities to generate differentials. Knowing these values, we can enter them into the formula (Figure 1) to obtain the likelihood of a pneumonia diagnosis. We can see from this that the probability of a 65 year old male presenting with dyspnoea having pneumonia is 35% making it a likely diagnosis. Whilst this is a fairly simple calculation using only one input, in practice multiple inputs would be used with a more complex form of the equation to generate a more relevant probability. Moreover, it would be applied to a range of diseases to calculate the probabilities of a set of differentials. A free-to-access engine known as BayesianMed (bayesianmed.org) depicts how Bayesian engines can be used by students to assess their history taking and differential diagnosis formation. The software not only analyses the inputs to produce a differential diagnosis but also explains its rationale using probabilities and prompts further questions alongside the elicited history to narrow down the list.7 This can serve to improve medical students’ clerking skills and accuracy of differentials along with their confidence and ability in using AI. As humans we have cognitive biases, AI and Bayesian networks help us to operate more systematically and objectively thus reducing the effects of these biases.8 This has the potential to reduce errors in medical decision making, ensure that diagnoses are not missed in clinical practice and ultimately lead to better patient outcomes. We propose that medical students are encouraged to use a Bayesian engine after clerking patients during their clinical rotations, especially if there is no doctor available for feedback. A student can input their findings into Bayesian software on a tablet or smartphone, which informs them of the differential diagnosis and likelihood of each, breaking down the reasoning. Furthermore, the student should take note of the further differentiating questions, and endeavour to incorporate them into future practice. The implementation of this change could also be eased into practice through it being a directly observed procedural skill under the supervision of a trained educator. To ensure safety and success, however, necessary steps and precautions must be taken. Some medical schools have begun to integrate elective modules on programming and machine learning into their curricula.3 We support this change and propose students are initially taught how Bayesian models work during their transition from pre-clinical to clinical medicine, to begin with as an elective module or an online seminar series for students who are curious. This initial educational step is vital to overcome the potential mistrust in AI and in allaying the fears of the patient and the public. Furthermore, it allows students to appreciate the possible weaknesses in statistical modelling such as overfitting and not becoming over-reliant, potentially ruling out rarer diagnoses or atypical presentations. We stress the importance of aspects of history taking such as communication skills and empathy. These still require the presence of a clinician to observe, assess and provide constructive feedback. The authors report no conflicts of interest in this communication.