Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors’ Reasoning with Deep Reinforcement Learning
Abstract
To reduce medical doctor’s workload and democratize access to medical care,the automation of the evidence acquisition and diagnosis process has attractedincreasing attention recently.
However, most works proposed in the machinelearning literature focus solely on improving the prediction accuracy of the patient’s pathology.
We argue that this objective is insufficient to ensure doctors’acceptability of the system.
For doctors to trust the system recommendations, theyneed to understand how the gathered evidences led to the predicted diseases.
Inparticular, interactions between the system and a patient should emulate doctors’reasoning.
To do so, we propose to model the evidence acquisition and automaticdiagnosis tasks in a deep reinforcement learning framework by considering threeessential aspects of doctors’ reasoning, namely using differential diagnosis withthe exploration-confirmation approach while prioritizing severe pathologies .
Wepropose metrics for evaluating interaction quality concerning these three aspects.
We show that our approach performs better than existing models while maintainingcompetitive prediction accuracy.
Introduction
In recent years, the digital healthcare industry has been growing rapidly, benefiting from the advancements in machine learning (Esteva et al., 2019; Xiao et al., 2018).
In particular, telemedicine,i.e., healthcare services provided via digital means, has received much attention (Kichloo et al.,2020).
Aiming to reduce human doctors’ workload and thereby broaden access to care by automatingparts of patients’ interaction, telemedicine applications typically involve two significant components,among others: evidence acquisition and automatic diagnosis .
In a typical interaction, a patient firstpresents his/her chief complaint 1 to the telemedicine system, then the system asks further questionsto gather more evidences, and finally, it makes a prediction regarding the underlying disease based onall collected evidences.
Importantly, a human doctor would typically review the entire interaction,including the evidence acquisition process as well as the prediction made, before eventually makingthe final diagnosis and providing next instructions to the patient (e.g., additional exams, prescriptions).
There are many existing works in the machine learning literature that aim to improve the automated evidence acquisition and disease diagnosis steps, using Reinforcement Learning (RL) (Kao et al., 2018;
Wei et al., 2018; Yuan and Yu, 2021), Bayesian networks (Guan and Baral, 2021; Liu et al., 2022), orsupervised approaches (Luo et al., 2020; Chen et al., 2021).
Regardless of the approach, its successis commonly measured by the accuracy of both the collected evidences and the predicted pathology.
However, a crucial part of patients’ interaction is largely overlooked in prior works, namely usingthe collected evidences and predicted diseases to support human doctors’ final decisions.
A patient’sinteraction does not end with models’ predictions, and therefore it is important to build models thatdoctors can trust ; otherwise, they would need to interact with patients from scratch, defeating thepurpose of such models.
For doctors to trust its output, the model, in addition to making accuratepredictions, needs to behave in a way that mimic human doctors’ reasoning so that it is understandableby doctors (Druce et al., 2021).
The importance of building doctor-understandable machine learningsystems for the purpose of translation into medical applications is echoed in prior works in othermedical areas (Clough et al., 2019).
In particular, when interacting with a patient, senior doctorstend to proceeds under an organized and logical diagnostic framework (Mansoor, 2018) to build adifferential diagnosis , that is a set of plausible underlying pathologies, with a particular attention puton collecting evidences for ruling out severe pathologies (Ramanayake and Basnayake, 2018).
In this work, we make a case for explicitly modelling senior doctors’ reasoning in designing evidenceacquisition and automated diagnosis models using RL.
More specifically, we focus on (1) usingdifferential diagnosis , rather than the ground truth pathology, as models’ training targets, (2)modulating the evidence acquisition process to resemble senior doctors’ exploration-confirmationapproach, and (3) prioritizing severe pathologies .
In the remaining sections, we explain the concepts of differential diagnosis, exploration-confirmation,and severe pathologies in the context of medical history taking (§2); we review existing works andtheir shortcomings (§3); we introduce some notations (§4) and describe our method that improvesupon prior works especially in mimicking doctors’ reasoning (§5); we provide empirical resultsdemonstrating the benefits of the proposed approach (§6 and 7); finally, we discuss the limitations(§8) before concluding this work (§9).
Our main contributions are: (1) we reformulate the task ofevidence acquisition and automated diagnosis by introducing doctors’ trust as a desideratum, andargue for explicitly designing models towards this goal; (2) we propose a RL agent, CASANDE, thatpromotes the desired behavior in addition to accurate predictions, by means of predicting differentialsand reward shaping; (3) we empirically show that existing strong models on this task, even withmodifications, are not sufficient for the proposed goal of mimicking human doctors’ reasoning, andthat CASANDE improves over existing models while being competitive on conventional metrics.
Medical history taking
Medical history is a broad concept encompassing various kinds of information relevant to a patient’sconcern 2 .
It includes, for example, past illnesses, current symptoms, demographics, etc.
In takingmedical history, a doctor would organize the questions under an exploration-confirmation framework.
At any point during the interaction, the doctor would have in their mind a differential diagnosis of diseases worth considering, which might be modified after further questions.
In forming the differentialdiagnosis, the doctor would treat severe pathologies differently by prioritizing questions that couldhelp ruling them out even if they are less likely.
We provide more details about these concepts below.
Differential diagnosis Prior work on automatic diagnosis primarily focused on predicting theground truth pathology, i.e., the disease causing the patient’s symptoms(Chen et al., 2021; Zhao et al.
,2021; Yuan and Yu, 2021; Guan and Baral, 2021; Wei et al., 2018; Xu et al., 2019; Kao et al., 2018).
However, medical doctors consider a set of plausible diseases, known as the differential diagnosisor simply differential , which is refined throughout the interaction with the patient(Henderson et al.
,2012; Guyatt et al., 2002; Rhoads et al., 2017).
Richens et al. (2020) considers the differential in theirapproach; however it is unclear whether all evidences are provided to the model at the same time orif the model is part of the acquisition process.
Our approach considers the differential diagnosis asan essential part of the model.
Considering differentials instead of a single pathology has the addedbenefit of accounting for uncertainty and errors inherent in diagnosis, especially when decisions aremade solely based on interactions with patients and without medical exams or tests.
For this reason,being able to predict the differentials rather than the ground truth pathology is an important part ofgaining doctors’ trust in the model.
Exploration-confirmation
In addition to considering differentials, inquiring about evidences ina manner similar to the way a doctor would engage with patients is another important factor in gaining doctors’ trust in automated systems, and this is largely overlooked in prior works as well.
Doctors generally proceed according to a diagnostic framework that provides an organized andlogical approach for building a differential.
More specifically, the interactions conducted by a seniordoctor generally consist of two distinct phases: exploration followed by confirmation .
Duringexploration, the doctor mainly asks questions that keep multiple pathologies under evaluation.
Inthat phase, pathologies can be removed from or added to the differential depending on the patient’sanswers.
During the confirmation phase, the doctor inquires about evidences that strengthen theactual differential (
s)he is considering (Mansoor, 2018; Richardson and Wilson, 2015; Rhoads et al.,2017).
This two-phase quality of doctors’ interactions with patients is a separate dimension of theacquisition process that differs from the objective of predicting differentials, and therefore it should beconsidered and measured separately from the common paradigm of optimizing prediction accuracy.
Severe pathologies Among all possible diseases, severe pathologies receive more attention thanothers.
Generally, a doctor does not want to miss out on them and, therefore, they would explicitlygather evidences to rule them out from the differential as soon as they become plausible, even if theyare less unlikely than other diseases (Rhoads et al., 2017).
Emulating this behavior within automatedsystems would contribute to increasing doctors’ trust in these systems.
Related work
There have been a variety of prior works on evidence acquisition and automatic diagnosis.
Forexample, Wei et al.
(2018) proposes to use DQN (Mnih et al., 2015) to approach the problem.
Kao et al.
(2018) additionally proposes to create a hierarchy of symptoms and diseases based onanatomical parts and train an agent using Hierarchical Reinforcement Learning (Sutton et al., 1999).
Xu et al.
, Zhao et al.
(2021), and Liu et al.
(2022) propose to encode relations among differentevidences and evidence-disease pairs to enhance the efficiency of an agent trained using DQN.
Penget al.
(2018) uses a policy gradient algorithm (Williams, 1992) and relies on reward shaping functions(Ng et al., 1999; Wiewiora et al., 2003; Devlin and Kudenko, 2012) defined on the state space tofavour positive evidence acquisition.
The proposed approach also makes use of reward shapingfunctions, but unlike the work done in (Peng et al., 2018), they are defined on the action space andused to induce bias regarding doctors’ reasoning in our agent.
In above mentioned approaches, the space of the evidences to be collected and the one of the diseasesto be predicted are merged together.
Janisch et al.
(2019) considers to deal with those spaces separatelyand proposes an agent which has two branches correspondingly, i.e. one that decides which evidenceto inquire, trained using RL, and the other that predicts the disease, trained using supervised learning.
Other approaches enhance the training of the evidence inquiry branch by using information fromthe classifier branch.
For example, Kachuee et al.
(2019) relies on Monte-Carlo dropout (Gal and
Ghahramani, 2016) to estimate the certainty improvement from the classifier branch output and usesthat information as an evidence acquisition reward to train the acquisition branch.
Yuan and Yu (2021)proposes an adaptive method to align the tasks performed by the two branches.
More specifically, theacquisition branch receives an extra reward of the change in entropy of prediction, and the acquisitionprocess ends when the entropy of the prediction is below a dynamically learned, disease-specificthreshold.
Our approach also uses the two-branches setting and designs a mechanism to inform theacquisition process when to stop based on information from the classifier branch.
Unlike Yuan and
Yu (2021), we explicitly introduce a stop action whose Q-value is trained to replicate a reward derivedfrom the classification branch.
The evidence acquisition branch can be trained with learning paradigms different from RL.
Chen et al.(2021) proposes to model the evidence acquisition process as a sequential generation task and trainthe agent in a similar manner to BERT’s Masked Language Modelling (MLM) objective (Devlin et al.
Similarly, Luo et al.
(2020) relies on randomly generated trajectories to train in a supervisedway a system to collect evidences from patients.
Finally, Guan and Baral (2021) makes use of the
Quick Medical Reference belief network (Miller et al., 1986), and applies Bayesian experimentaldesign (Chaloner and Verdinelli, 1995) in the inquiry phase while relying on the Bayes rule to inferthe corresponding disease distribution.
However, most of these approaches are primarily focused on predicting the single ground truthpathology, as opposed to the differential diagnosis in this work.
Also, they have rarely made specificefforts to shape or guide the interaction to resemble how doctors would interact with patients.
As a result, it is debatable how much doctors could trust such systems, even though some may perform wellon benchmark datasets, and therefore casting doubt on their applicability in real-world applications.
Preliminaries
Let E and D be the number of all evidences (i.e., symptoms and antecedents) and diseases underconsideration.
Evidences can be binary (e.g., are you coughing? ), categorical (e.g., what is thepain intensity on a scale of 1 to 10 ), or multi-choice (e.g., where is your pain located ).
The taskof automated evidence collection and diagnosis can be viewed as a sequential decision processwhere each interaction with a patient is formalized using a finite-horizon Markov Decision Process
M = ( S , A , P, R, γ, T ) with a state space S = S ′ ∪ { s ⊥ } ( s ⊥ is the terminal state), an actionspace A , a dynamics P , a reward function R , a discount factor γ , and a maximum episode length
T .
A state s ∈ S ′ encodes socio-demographic data regarding the patient (e.g., age, sex) as well asthe evidences provided by the patient so far.
A is defined as { 1 , · · · , E, E + 1 } where the first Eelements, referred to as acquisition actions , are used to inquire about corresponding evidences, andthe last element is the exit action that is used to explicitly terminate the interaction.
At any point intime, only actions not yet selected in the episode are available for future selection.
P is deterministicand updates the current state based on the current action and the patient response if the current turnis less than T , otherwise, the next state is set to s ⊥ .
Finally, regarding R , each acquisition action ischaracterized by an inquiry cost r i , and depending on whether the underlying patient is experiencingthe corresponding evidence, it may additionally incur a retrieval reward r p or a missing penalty r n .
Upon the termination of the interaction, a prediction regarding the patient’s disease is made based onthe collected evidences.
Method
[Figure] An overview of the proposed approach is depicted in Figure 1.
Our model is made of two branches:an evidence acquisition branch in charge of the policy for interacting with patients and a classifierbranch in charge of predicting the differential at each turn.
Both the classifier and the policy networksrely on a latent representation computed by an encoder from the evidences collected so far fromthe patient.
Importantly, we introduce a reward shaping module which leverages the output of theclassifier to define auxiliary rewards that help induce the desired properties from doctors’ reasoningidentified in § 2 in the agent during training.
More precisely, the exploration and confirmation rewardsare designed to encourage the two-phase based interactions, whereas the severity reward is used toexplicitly handle severe pathologies.
Finally, the classification reward is used to train the agent torecognize when to stop the interaction process.
The proposed agent predicts at each turn t the nextaction a t as well as the belief bel t regarding the patient disease from the current state s t .
We relyon a DQN variant algorithm, Rainbow (Hessel et al., 2018)
(without noisy networks Fortunato et al.(2018)), to search for the optimal policy and the objective of the proposed auxiliary rewards is toencourage trajectories similar to those of experienced medical practitioners.
In the next sections, wethoroughly describe each of these auxiliary rewards.
5.1 Exploration reward: encouraging the evaluation of multiple pathologies
We want our agent to undertake actions that favour considering several pathologies in the differentialdiagnosis during the first phase of the interaction.
In practice, this means that the agent’s belief isconstantly changing after inquiring about a new evidence.
As more evidences are collected, thisfluctuation should gradually reduce until it becomes insignificant.
From this intuition, we derived theexploration reward R Ex as
[Equation]
where JSD is the Jensen-Shannon divergence that computes the dissimilarity between consecutivebeliefs, and w Ex ( t ) ∈ [0 , 1] is a dynamic weight which aims at controlling the importance of thisshaping component as a function of time.
Inspired from the shape of the sigmoid function, wedesign w Ex ( t ) as a translated version in [0 , T ] of the “flipped” sigmoid function within the interval[¯ x min , ¯ x max ] .
In other words, using trans ( t ) = t ∗ (¯ x max − ¯ x min ) T + ¯ x min , we have
[Equation]
where ∆ Ex is a hyper-parameter that controls how fast the function saturates at 0 (cf.
Appendix A.1).
[Figure]
5.2 Confirmation reward: Strengthening the agent’s belief in the pathology
In the second phase, the agent should inquire about evidences that help strengthen its belief regardingthe differential diagnosis.
This means that the more the agent collects evidences, the closer its beliefshould be to the ground truth distribution y .
The importance of this shaping component should gradually increase as the agent moves towards the end of the interaction.
From this intuition and inspiredfrom potential-based reward shaping Ng et al.
(1999), we derived the confirmation reward R Co as
[Equation]
[Equation] where CE is the cross-entropy function, and w Co ( t ) ∈ [0 , 1] controls the importance of this shapingcomponent as a function of time.
Like w Ex ( t ) , w Co ( t ) is a translated version of the sigmoid functionwith the difference that it increases over time.
Thus, we have:
[Equation]
where ∆ Co is a hyper-parameter controlling how fast the function saturates at 1 (cf.
Appendix A.1).
5.3 Severity reward: evidence gathering for ruling out severe pathologies
We want our agent to collect evidences that help ruling out severe pathologies as soon as they becomeplausible.
One proxy to achieve this for the agent is to behave in such a way to monotonically increasethe number SevOut of severe pathologies that are not in both the ground truth differential and itspredictions through time.
As such, we define the severity reward R Sev as
[Equation]
Here, a pathology is not part of a predicted distribution if its probability is below a given threshold.
5.4 Classification reward
This reward is designed to provide the agent feedback regarding how good is its final predicteddifferential with respect to the ground truth differential y when the interaction process is over.
Let
SevIn t be the number of severe pathologies that are part of both the ground truth differentialy and the predicted belief bel t and let Sev y be the number of severe pathologies in y .
We define
V ( s t , y )
= − CE ( bel t , y )
+
w si SevIn t
Sev y as a measure of the quality of the belief prediction from state s twhere CE is the cross-entropy function, and the second term, controlled by the hyper-parameter w si , ismeant to deal with the missing of severe pathologies.
We thus define the classification reward R Cl as [Equation] t
[Equation]
5.5 Training Method
The above mentioned auxiliary rewards are combined together according to the Equation 7 where theα weights are hyper-parameters.
The resulting function F is then combined with the environmentreward leading to a new reward function R ′ =
R + F that is used to train the policy network.
[Equation]
For simplicity, this section relies on DQN to present the loss for training each part of our agent, withthe extension to Rainbow easy to do.
Let θ and ϕ be the agent and target network parameters.
Policy network loss function: The Q-value of the exit action does not depend on any followingstate.
Also, the reward received when explicitly exiting is the classification reward.
Therefore, tosynchronize the agent branches, the exit action Q-value needs to be reconciled with the classificationreward.
This should be done at each interaction time step during the training, given that the agentneeds to have a good estimate of the expected Q-value of the exit action to select this action.
Thus,
[Equation]
where B is a batch from the replay buffer, Q θ ( s t , a exit ) is the predicted Q-value for the pair( s t , a exit ) , Q t = r ′ t + 1 s t +1 ̸
= s ⊥ γ max a ∈A Q ϕ ( s t +1 , a ) is the target Q-value for the pair ( s t , a t ) with 224 r ′ t =
r t + F ( s t , a t , s t +1 ) .
Classifier network loss function: The classifier is updated using the following loss at the end ofan interaction:
[Equation]
bel t ,y ) ] .
process:
Using the loss functions defined in Equations 8 and 9, the two branches arealternatively updated until the stopping criteria are satisfied.
Both updates share the same batch ofdata B which is sampled from the replay buffer.
The training process is described in Appendix A.2.
Experiments
Datasets The datasets used in prior works such as DX (Wei et al., 2018), Muzhi (Xu et al., 2019),
SymCAT (Peng et al., 2018), HPO (Guan and Baral, 2021), and MedlinePlus (Yuan and Yu, 2021) donot provide differential diagnosis information, and are therefore ineligible for validating the approachproposed in this work.
Besides, as supported by Yuan and Yu (2021), the patients simulated using SymCAT are not sufficiently realistic for testing automatic diagnosis systems.
We instead use the DDXPlus dataset (Fansi Tchango et al., 2022) for that purpose.
In addition to providing differentialdiagnosis and pathology severity information, the DDXPlus dataset, unlike the above mentioneddatasets, is not restricted to binary evidences, but also has categorical and multi-choice evidences.
Furthermore, the DDXPlus dataset contains 49 pathologies and 223 evidences.
Finally, the dataset issplit into 3 subsets: a training subset with more than 10 6 synthetic patients, a validation and a testsubsets containing roughly 1 . 4 × 10 5 synthetic patients each.
Baselines We consider 4 baselines, AARLC (short for Adaptive Alignment of Reinforcement Learning and Classification) (Yuan and Yu, 2021), Diaformer (Chen et al., 2021), BED (Guan and
Baral, 2021), and ASD which is an extension on Luo et al.
AARLC demonstrates SOTAresults on the SymCAT dataset while Diaformer shows competitive results on the Muzhi and DXdatasets.
BED, an approach that does not require training, has impressive results on the HPO dataset.
All these methods are designed to only handle binary evidences and had to be modified to deal withthe different evidence types in the DDXPlus dataset.
Moreover, those methods were designed topredict the ground truth pathology, and had to be modified to handle differentials.
Additional detailsare provided in Appendix B.
[Table] Experimental settings Each patient in the DDXPlus dataset is characterized by a chief complaint,which is presented to the models at the beginning of the interaction, a set of additional evidences thatthe models need to discover through inquiry, a differential diagnosis, and a ground truth pathology.
We allow interactions to have a maximum of T = 30 turns.
We tune the hyper-parameters for eachmodel, including the baselines, separately on the validation set and use the resulting optimal set ofparameters to report the performances.
For more details, see Appendix A.4.
Evaluation metrics We report on the interaction length (IL), the recall of the inquired evidences(PER), the ground truth pathology accuracy (GTPA@1), and the F1 score of the predicted differentialdiagnosis (DDF1).
Additionally, we compute the F1 score regarding the prediction of the severepathology (DSF1).
Finally, we report on the total variation distance (TVD), a proxy metric used toquantify the portion of the space of the probability distribution considered during an interaction.
See
Appendix D for the definition of these metrics.
Results
Disease prediction and evidence acquisition Table 1 depicts the performances obtained at theend of the interaction process by each model.
On recovering the ground truth pathology, CASANDEperforms on par with the baselines.
However, when trained with the differential, CASANDE issignificantly better on DDF1 than the baselines, outperforming the second-best model by morethan 10% absolute.
Meanwhile, all methods are on par on that metric when the differential is notused at training time.
Likewise, CASANDE outperforms the baselines in recovering the severepathologies (DSF1) with an absolute margin of more than 4% when trained with the differential,while it behaves similarly as the baselines (except Diaformer) otherwise.
The observed differencebetween the resulting performance for each training setting suggests that relying only on the groundtruth pathology at training time is not enough to accurately predict the differential diagnosis atinference time.
Furthermore, with the highest TVD scores, CASANDE shows its ability to explorewide regions of the probability space throughout the interactions compared to the baselines withoutsacrificing the end performances.
CASANDE also achieves the best performance on the PER metric demonstrating its ability to collectrelevant evidences.
Finally, CASANDE has the longest interaction length .
This is unsurprising, as
CASANDE is trained to emulate doctors’ reasoning.
In particular, the exploration and confirmationphases may lead to the model asking more questions.
Trajectory quality evaluation We plot in Figure 2 the confirmation score versus the explorationscore throughout the trajectories for the different models.
The exploration score captures howdistant two consecutive agent predictions are, while the confirmation score captures how close theagent prediction is w.r.t.
the target distribution (see Appendix D.2 for the definitions).
Ideally, anagent would start with a high (resp. low) exploration (resp. confirmation) score (upper-left corner),gradually decrease (resp.
increase) it, until the score reaches a low (resp. high) value at the end of theinteraction (lower-right corner).
As can be observed, this trend is highly followed by CASANDEwhich exhibits the highest exploration (resp.
confirmation) score at the beginning (resp. end) of theinteraction while, unlike other models, it is consistently moving towards the lower-right corner of thechart.
[Figure] [Figure] throughout trajectories on average for each model on the test set.
For each trajectory, the scores are computed for each turn. We select the score values at turns distant from each other of 5% of the trajectory length (i.e.,positions in total).
Finally, we average the scores obtained for each such position throughout the dataset.
Handling of severe pathologies We areinterested in analysing the pace at whichsevere pathologies are ruled out from orruled in within the predicted differentialsthroughout the trajectories. We show in
Figure 3 the curves corresponding to the average values of (1) the F1 scores, i.e., DSF1,(2) the rule-out rate, and the rule-in rateof severe pathologies throughout the trajectories (see Appendix D.3 for the definitions).
As observed, CASANDE improvessignificantly on the F1 score throughouta trajectory, and eventually performs thebest among all models.
This shows thatthe capability of CASANDE for predicting severe pathologies is heavily groundedon the evidence it gradually collects.
Thisevidence-based behavior is further confirmed by the rule-out and rule-in rates as
CASANDE is the only method that significantly improves on both rates throughoutthe trajectories.
Interestingly, when focusing on the rule-out curve, it is noticeable that CASANDE performs thesecond-worst at the beginning, trailing the best performing model by more than 20% , but it improvesto the second-best at the end, with the gap smaller than 3% .
This suggests that at the beginning,
CASANDE is more lenient on including severe diseases in its differentials, resulting in more falsepositives than others.
However, this is a desired behavior of CASANDE, as during the explorationphase, it is expected to consider unlikely but severe diseases before gathering enough evidences torule them out.
In other words, at the beginning of a trajectory where few evidences are gathered, amodel should err on the side of caution by keeping unlikely but severe diseases into consideration.
Ablation studies We perform an ablation analysis to illustrate the contribution of each auxiliaryreward component of CASANDE to the overall performance.
Table 2 shows the results when somereward components are deactivated.
We observe a drop in DDF1 and DSF1 when most rewards aredisabled. This indicates that each reward function helps the agent capture complementary informationthat is useful for predicting the differential.
Also, as expected, the exploration and confirmationscores contribute in increasing the interaction length.
Finally, we observe a decrease of the DSF1metric when the severity reward is not used.
Additional studies are provided in Appendix E.
[Table]
Limitations and potential negative social impact
Limitations In this work, we set out to include doctors’ trust as part of the desiderata in buildingevidence acquisition and automated diagnosis systems.
In doing so, we focus our efforts on threeessential aspects of doctors’ reasoning, namely differential diagnosis, exploration-confirmation, andsevere pathologies.
We choose to focus on them as advised by our collaborating physicians.
However,to most accurately evaluate doctors’ trust, the proposed method has to be deployed so as to allowdoctors different from the ones we collaborate with to judge.
Additionally, it is reasonable to assumethat doctors in other locations and/or specialties may have different ways of engaging with patients.
Therefore, while we provide evidence in medical literature (§2) to show the identified desiderataare widely applicable, we recognize that there may be cases where this work does not apply.
In thiswork, we conduct experiments on synthetic patients, mainly due to the lack of real-patient datasetsthat contain differential diagnosis.
We recognize that synthetic patients can be different from realpatients in various and important ways, and that therefore results reported on synthetic patients maynot extend to real patients.
Potential negative social impact As we have made clear from the beginning, evidence acquisitionand automated diagnosis systems are not substitutes for human doctors, but rather they are supportivetools for doctors, who should make the final decisions.
However, the predictions of such systemspotentially might be provided to patients as the final medical advice, without human doctors’ intervention.
In such cases, the instructions that patients receive may be misleading or erroneous, and thuscan do more harm than good to patients’ health.
Conclusion
We reflected on the task formulation of evidence acquisition and automatic diagnosis that are essentialfor telemedicine services, and introduced doctors’ trust as an additional desideratum.
Concretely,we argued emulating doctors’ reasoning is critical for gaining doctors’ trust, and we identified threeessential features of doctors’ reasoning that models can mimic.
We proposed a novel RL agent withthese features built in.
We showed empirically existing models are insufficient for imitating doctors’reasoning.
We then demonstrated the importance of the explicit modelling of differential diagnosis,and the efficacy of our model on emulating doctors, while being competitive on conventional metrics.
This work is a first step towards reshaping the research in automatic diagnosis systems, and there isabundant potential for future work to explore.
For instance, incorporating doctors’ reasoning intosupervised approaches is a direction worth investigating. Checklist
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