Artificial Intelligence in Clinical Health Care Applications

Viewpoint

Artificial Intelligence in Clinical Health Care Applications:Viewpoint

Michael van Hartskamp, PhD; Sergio Consoli, PhD; Wim Verhaegh, PhD; Milan Petkovic, PhD; Anja van de Stolpe,

MD, PhD

Philips Research, Eindhoven, Netherlands

Corresponding Author:

Anja van de Stolpe, MD, PhD

Philips Research

HTC11, p247

High Tech Campus

Eindhoven, 5656AE

Netherlands

Phone: 31 612784841

Email: anja.v[email protected]

Abstract

The idea of artificial intelligence (AI) has a long history. It turned out, however, that reaching intelligence at human levels is

more complicated than originally anticipated. Currently, we are experiencing a renewed interest in AI, fueled by an enormous

increase in computing power and an even larger increase in data, in combination with improved AI technologies like deep learning.

Healthcare is considered the next domain to be revolutionized by artificial intelligence. While AI approaches are excellently

suited to develop certain algorithms, for biomedical applications there are specific challenges. We propose six

recommendations—the 6Rs—to improve AI projects in the biomedical space, especially clinical health care, and to facilitate

communication between AI scientists and medical doctors: (1) Relevant and well-defined clinical question first; (2) Right data

(ie, representative and of good quality); (3) Ratio between number of patients and their variables should fit the AI method; (4)

Relationship between data and ground truth should be as direct and causal as possible; (5) Regulatory ready; enabling validation;

and (6) Right AI method.

(Interact J Med Res 2019;8(2):e12100) doi: 10.2196/12100

KEYWORDS

artificial intelligence; deep learning; clinical data; Bayesian modeling; medical informatics

Introduction

The idea of artificial intelligence (AI) has a long history. Since

the 1950s there have been several revolutionary promises of AI

replacing human work within a few decades. It turned out,

however, that reaching intelligence at human levels was more

complicated, which led to several “AI winters,” where interest

in AI disappeared [1]. Currently, we are experiencing a renewed

interest in AI, fueled by an enormous increase in computing

power and an even larger increase in data generation. In

combination with improved algorithms that allow training of

deep neural networks, several high-tech companies have reached

successes in performing tasks that are close to human or even

beyond human performance: playing games like chess and Go,

image recognition and computer vision, natural language

processing, machine translation, and self-driving cars are just

a few examples.

Health care is considered the next domain to be revolutionized

by AI [2-5]. In addition to many academic efforts, companies

are also getting involved. IBM has developed Watson for several

health applications, such as Watson for Oncology and Watson

for Genomics, and there is a large number of start-ups addressing

all possible aspects of the health continuum [6,7].

Artificial Intelligence

The term artificial intelligence is used to indicate development

of algorithms that should execute tasks that are typically

performed by human beings and are, therefore, associated with

intelligent behavior. AI makes use of a variety of techniques,

such as deep learning, but also probabilistic methods like

Bayesian modeling [8,9]; for definitions, see He et al [2].

Colloquially, the term is applied to a machine that mimics

cognitive functions, such as learning and problem solving

[2,4,10,11].

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Use of Artificial Intelligence Methods in

Health Care

It is clear that health care has numerous needs that could benefit

from solutions developed with, or by embedding, artificial

intelligence [2,4,8]. In this brief article, we focus on the

contributions AI can make to clinical health care, a domain that

poses new and sometimes unique challenges to the application

of AI. In the next sections, we discuss some important

challenges and provide our recommendations on how to deal

with them.

While radiology imaging was first in delivering digital data,

digital pathology is a more recent revolutionary development

[4,12,13]. In addition, for many years, hospitals have been

digitizing their medical patient records [14]. Hence, a large and

ever-increasing body of reasonably annotated clinical data has

been collected: partially structured data in machine-readable

formats, such as those from medical imaging, and partially

unstructured data in natural language. As in other industrial

sectors, it is expected that this big data movement can be

leveraged to transform health care and drive unprecedented

improvements in quality of patient diagnostics, treatment, care,

and clinical outcome. Expected results range from identification

of individuals at high risk for a disease, to improved diagnosis

and matching of effective personalized treatment to the

individual patient, as well as out-of-hospital monitoring of

therapy response [15]. Although these opportunities and this

potential are widely acknowledged, it is important to understand

what can be delivered in practice with the current state-of-the-art

AI technologies and which applications require further advances

in AI to become feasible.

Artificial Intelligence Methods: Supervised

or Unsupervised Learning;

Knowledge-Based or Data-Driven?

Multiple AI technologies are available to choose from [2,9,16].

Algorithmic learning-based AI can be performed in a supervised

mode; this means that a ground truth label is available for every

data sample, which guides the AI effort and is based on domain

knowledge. It will be obvious that the correctness of ground

truth labels is a prerequisite for good performance of an AI

solution. The alternative, unsupervised mode, is when no ground

truth is available and only similarities can be found with a yet

undefined meaning [2,16].

Machine learning traditionally involves a human to determine

features of the data, using domain knowledge. In contrast, deep

learning allows finding such features from the data by itself.

The features are subsequently used in various models. Some of

those can be knowledge-based models in which new deep

learning-defined features are integrated according to knowledge

[17,18]. The current interest in AI from industry comes from

the recent breakthroughs in data-driven approaches, such as

deep learning, and their applicability in industrial applications

such as speech recognition, machine translation, and computer

vision. Still, it is expected that combining data-driven and

knowledge-based approaches will bring AI to the next level,

much closer to human intelligence [17,18].

Artificial Intelligence for Well-Defined

Narrow Tasks: Radiology Imaging and

Digital Pathology

One of the more studied and successfully executed AI

opportunities is in imaging. For example, AI technologies can

be applied to problems such as distinguishing cell nuclei or

certain cell types present in a tumor sample on a histopathology

slide, using slide images obtained with a digital pathology

scanner [19-21]. Such images are made with consistent

equipment and acquired in a controlled fashion, generating

images consisting of uniform data and providing very good

representations of the phenomena to be modelled. The problem

domain is limited. For instance, in the training process, the AI

system gets as input raw images with associated labels for

different cell types that are provided by a pathologist. The

pathologist is providing a ground truth , based on existing expert

knowledge (eg, on the different cell types or architecture present

in the tissue slide). Deep learning has been applied to this

problem and AI technologies already outperform manually

crafted tissue analysis technologies [16,20,22,23]. It is expected

that they will soon be on par or better than a human pathologist

on certain well-defined histology feature recognition and

measurement tasks, though not yet for clinical interpretation.

Artificial Intelligence for Wider Purposes:

Coupling Patient Clinical Data to Clinical

Outcome

On the other hand, research projects are ongoing using

multimodal data (ie, a combination of datasets of a different

data type), for example, to enable prediction of prognosis of a

patient or clinical outcome after a certain treatment. One may,

for example, use medical imaging data combined with

histopathology and clinical laboratory data and even lifestyle

data to try to predict survival, risk of rehospitalization within a

certain number of days, etc. Such projects remain challenging

and have typically been proven not to be very successful [5,24].

IBM’s Watson for Oncology claims to integrate all available

cancer patient data and disease information for improved

diagnosis and therapy decision making. In 2013, the MD

Anderson Cancer Center started using IBM Watson technology

to increase effective treatment of cancer patients; however, the

project was stopped in 2017 because it “did not meet its goals”

[25,26]. In contrast, the concordance with respect to clinical

interpretation of single-modality genome sequencing data using

Watson for Genomics versus a clinical genomics expert group

was reportedly quite good, between 77% and 97%, depending

on the type of identified genomic mutations [7].

The most difficult challenge for AI in the coming years will be

to move from successful narrow domains into wider-purpose,

multimodality, data systems. A promising approach here is not

to find one methodology to address every problem but to

separate the wider-purpose AI goal into smaller goals. In this

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approach, subgroups of the data may be processed separately

with suitable AI methods to provide meaningful, clinically

relevant output. For example, for cardiac ultrasound images,

one could zoom in to develop an algorithm with deep learning

to measure left ventricular volume; or for pathology slide

images, one could zoom in to develop an algorithm for

recognition and quantification of a specific cell type (eg,

lymphocytes). To increase chances at success, it is important

to determine a well-defined, focused, and clinically relevant

question for an AI project that can be adequately answered with

the available data.

The conclusion of this section is that a relevant and well-defined

clinical question should come first.

The Relationship Between Patient or

Sample Numbers and Data Variables

Many AI techniques, especially deep learning, rely on the

availability of large datasets or big data [15]. Sometimes domain

knowledge can help to create additional data derived from the

data that are available. It is important, however, to distinguish

the type of data that is needed. In games, such as chess and Go,

it is easy to artificially synthesize additional data of the right

type to increase the size of the dataset. With respect to medical

and histopathology imaging, large amounts of data are available

since samples are defined on an image pixel basis. Using this

type of data, with relatively few images one can create millions

of annotated samples with drawing tools. It is relatively easy

to further augment every sample with artificially generated

variations (eg, mirror copies, rotated versions, modified

intensities, and modified colors) without consequences for the

annotation.

In contrast, in clinical health care the type of data typically is

a pathology or radiology report from a patient, associated with

a clinical annotation such as diagnosis or response to therapy.

In this case, the number of samples is generally equal to the

number of patients. The annotation is often more difficult, as it

requires an expert physician to provide the ground truth . When

using multimodal data to find parameters that, for example,

predict clinical outcome, despite all digital records and digital

health devices, there are not enough data. The number of patients

for which the necessary multimodal data are available is, in

general, the limiting factor for using AI methods on such

combined data sources to create a valid algorithm for risk

prediction, diagnosis, or a therapeutic decision. When the

number of patients of a specific defined disease (sub)type is

low, the often-heard strategy is to extend a study to include

more patients, even all patients worldwide, which requires

addressing various legal and technical barriers. However, this

is still likely to fail in reaching the required patient number;

with efforts to increase the number of patients for inclusion in

data analysis, the amount of variation per patient, including

many unknown features and variables, tends to grow as well,

leading to uncontrolled data variation. This is caused by the

large variation in human individuals: their DNA (ie, just think

of the 3 billion base pairs and the near-infinite combinations of

genomic variations), their lifestyle, family medical history, use

of medication, etc. Moreover, patients are never treated in

exactly the same manner in the various hospitals, bringing in

many additional variables. It is a well-recognized issue in

clinical trials run by pharma companies [24]. The challenge is

to minimize such unwanted variables in the patient or sample

set to analyze. Much of this uncontrolled variation is not

recorded or, at best, only in a very noisy way. The number of

unknown parameters that may have influenced the outcome,

especially if its measurement lies many years after the diagnosis

and treatment, is typically underestimated. Examples of failure

of AI methods caused by these issues include many

genome-wide association studies aimed at identification of

clinically useful genomic risk factors for complex diseases and

genomic studies aimed at identification of biomarkers for cancer

diagnostics and treatment decisions [27].

Similar challenges are present in other domains, but solutions

in those areas can be invoked that are not possible in the health

care domain. In natural language processing, Google Translate

is a well-known example. When it started, translations were of

very poor quality and heavily criticized, but Google decided to

keep the service up and running; online feedback was used to

collect a large amount of translation data, enabling continuous

improvement of the performance of the translation algorithm

[28].

In summary, for applying AI to multimodal patient data, the

number of patients from whom the complete set of multimodal

data is available is frequently too limited to address the curse

of dimensionality. In the scientific community, dimensionality

reduction remains an active research area [29-31]. In clinical

application areas for AI, it remains the main challenge to

address. The first solution lies in reducing data modality and

bringing the number of variables (P) on the right level in relation

to the number of patients or samples (N) for which a ground

truth is available. The desired solution will reduce

high-dimensional data to biologically sound knowledge-based

features. Introducing knowledge-based computational

approaches is expected to provide a way forward to reduce

model freedom and handle high-dimensional data [32].

The conclusion of this section is that the ratio between the

number of patients and their variables should fit the AI method.

Insufficient Data Quality and Many

Subjective Parameters

For the patient data that is available, it turns out that this data

is usually neither 100% complete nor 100% correct. For

example, diagnoses are not always complete or correct, or they

were not correctly entered into the digital domain. The main

diagnosis is, in general, reasonably well-documented; however,

side diagnoses and complications that arise, for example, during

hospital admission or in the home setting, as well as treatment

details are less-accurately or not documented [5]. For many

clinical variables, such as diagnoses, the ground truth comes

from a physician’s judgement and cannot be objectively

measured or quantified. For example, it is documented that

histopathology diagnoses differ to a varying extent among

pathologists that diagnose the same slide [33-35]. As a

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consequence, datasets may be incomplete and noisy, and

presumed ground truths may not always be correct.

The conclusion of this section is that the right data (ie,

representative and of good quality) needs to be obtained.

Causal Relations Versus Correlations: A

Role for Bayesian Reasoning

Any data-driven approach on data for which the ratio of number

of patients (ie, samples) to variables is too low can lead to

multiple spurious correlations [36]. This means that the data

suggest a correlation between two factors, but this is purely due

to chance and there is no underlying explanation or causal

relationship. In machine learning, this can easily lead to

overfitting and finding of irrelevant correlations [16,37]. Also,

for clinical implementation, any interesting correlation (eg, a

feature or combination of features associated with increased

disease risk) needs to be clinically validated at high cost, where

lack of causality generally results in very low success rates.

Therefore, turning an algorithm, based on correlations, into a

successful proposition will, in general, be easier if causal

relations underlie the found correlations. Knowledge-based

reasoning techniques, such as Bayesian network models, can

reduce the number of spurious relations and overfitting problems

by using existing knowledge on causal data relations to eliminate

noisy data [11,14]. As an additional advantage, Bayesian models

can deal very well with uncertainty and missing variables, which

is the rule rather than the exception in clinical data [11]. Not a

coincidence, with respect to patient data interpretation, Bayesian

models reason the way a medical doctor does [38].

The conclusion of the section is that the relationship between

data and ground truth should be as direct and causal as possible.

Validation of Artificial Intelligence-Based

Solutions

For many of the success stories of AI, a robust and reliable

result is usually not necessary. For a free translation service,

the consequence of a wrong decision is at most a dissatisfied

customer. Improvements of those services could happen

relatively quickly because many of those AI applications are

deployed in the field and iteratively improve their performance

on the basis of new data, thus learning from their mistakes. In

sharp contrast to most of these consumer or lifestyle solutions

based on AI, every clinical application, be it hardware or

software, requires a thorough clinical validation in order to be

adopted by the professional clinical community for use in patient

care, such as diagnostics or treatment decisions, and must be

approved by regulatory authorities [24]. The requirements for

clinical validation will be more stringent when errors or mistakes

can have greater consequences. In a clinical trial, it needs to be

demonstrated how accurately the developed AI solution

performs compared to the clinical standard (eg, sensitivity and

specificity of a diagnostic test). Still, it is not completely clear

whether good performance of an algorithm is acceptable if the

solution is a “black box” and not transparent and rationally

explainable [2]. On top of that, it is not obvious what proper

validation of a continuous learning-based solution implies. An

important issue is that because of lack of transparency, deep

learning-based “black box” algorithms cannot be easily

improved, in contrast to, for example, Bayesian models that are

based on a transparent structure. Initial attempts to tackle this

challenge are on the way [39].

Have AI-based solutions already been approved for clinical

use? The earlier mentioned Watson for Oncology system

operates as a “black box” and its advice could not be clinically

validated [26]. On the other hand, in 2017 it was claimed that

the first deep learning-based algorithm, which identifies contours

of cardiac ventricles from a magnetic resonance imaging (MRI)

image to calculate ventricular volume, was validated and

approved by the US Food and Drug Administration (FDA) for

performing the calculation faster than a clinician [40].

Obviously, this system’s scope is far more restricted than

Watson’s; the unimodal imaging data that were used were

directly and causally related to the ground truth provided during

every image analysis by the clinician. Also, it can be considered

a measurement algorithm and does not include a clinical

interpretation claim. Clinical validation and obtaining regulatory

approval are much more difficult for those algorithms for which

such an interpretation claim is added [4].

Several new solutions are ready or able to perform continuous

(ie, incremental) learning [41]. However, within current

regulations, an AI system for clinical applications should be

“frozen” and can, therefore, not learn online and immediately

apply its new knowledge. Rather, it needs to have an offline

validation of the obtained “frozen” model on an independent

series of patient or sample data. Following a next

continuous-learning cycle, the validation process needs to be

repeated again prior to renewed implementation of the model.

Ideally, new clinically acceptable ways to shorten validation

tracks for digital applications in a patient-safe manner should

be found; it is expected that special procedures will be put in

place to facilitate regulatory approval of updated algorithms.

In line with this, the FDA is actively developing a strategy to

deal with AI-based software solutions [42]. Maximal use of

existing knowledge in transparent and causal model algorithms,

as in Bayesian modeling, is expected to facilitate both clinical

validation and obtaining regulatory approval, both for unimodal

as well as for multimodal data.

The conclusion of this section is that procedures must be put in

place to facilitate algorithms to be regulatory ready and to enable

validation.

Methods to Use

Technology-wise, numerous methods from the domain of AI

have been explored for the development of clinical applications

[8,11,16,43]. Some have been more successful than others,

mostly depending on application type. For automating pathology

diagnosis using tissue slide images, deep learning has proven

to be an appropriate technology. When dealing with more

general multimodal problems, such as predicting clinical

outcomes, patient assessments, and risk predictions, other

methods that often include domain knowledge are likely to be

more appropriate choices. Probabilistic methods using

knowledge representation are increasingly used and enable

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reduction of the number of influencing variables, determining

sensible features or latent variables. Probabilistic Bayesian

modeling is well-suited to deal with complex biological (eg,

“omics” data, such as genomics and transcriptomics data) as

well as medical and clinical data; it is finding its way into

diagnostic applications as well as drug development [9,44-49].

However, where knowledge is lacking, knowledge-agnostic AI

approaches become valuable; Bayesian reasoning networks are

thought to have high potential for use in combination with deep

learning, combining the best of two worlds in Bayesian deep

learning [17,18,50].

The conclusion of this section is that the right AI method must

be used for the problem.

Recommendations for Use of Artificial

Intelligence Methods to Develop Clinical

Applications: The Six Rs

In view of the challenges related to the use of AI for health care

and biomedical applications, we believe it will be of value to

have some guidelines when designing a study. They may also

serve to facilitate communication between scientists involved

in AI and medical doctors. From the discussion above, we have

extracted six basic recommendations.

Relevant and well-defined clinical question first. Data

analytics without domain knowledge can be applied in the

health care domain, but at high risk of getting clinically

irrelevant outcomes. For every new AI project, the clinical

questions should be well-defined and reviewed with clinical

experts. The outcome of the analysis should also be

reviewed for clinical and/or biological sense.

Right data (ie, representative and of good quality). Carefully

define the dataset that is needed to answer the clinical

question. A clinical dataset with ground truth should be

sufficiently clean and reliable. Be aware of hidden variation

between samples that is not visible in the dataset. The

dataset should be appropriate for the question at hand as

well as representative for the population under study.

Ratio between number of patients and their variables should

fit the AI method. To obtain useful results, ensure working

with adequately large datasets (ie, numbers of patients or

samples) for the AI method to be used, and reduce patient

variables where possible. Use domain knowledge to limit

spurious correlations.

Relationship between input variables and predicted output

variable, as the dependent value, should be as direct and

causal as possible. The clinical question should as closely

as possible relate the ground truth to the data. Hence,

finding new pathology features that best distinguish between

two different pathology diagnoses can be successful; using

lifestyle information to predict 10-year survival might not.

Regulatory ready; enabling validation. Upfront, consider

how a certain solution can be validated and pass regulatory

requirements. Consider how using domain knowledge could

speed up the validation process, for instance, by breaking

up the AI system into smaller AI systems. This effectively

excludes systems that iteratively change by continuous

learning.

Right AI method. Use the right method for the question at

hand. Data-driven methods can be used if the data available

allows it, and knowledge-based methods can be applied if

there is knowledge available but not enough data; a mixture

of the two, combined in a wise manner, may be highly

productive for development of clinically applicable health

care solutions.

Privacy Issues

Driven by the big data analysis developments in health care,

new privacy regulations were recently implemented in

Europe—General Data Protection and Regulation (GDPR) [51].

To protect privacy, individuals control their own personal data,

and explicit informed consent is required for access to the data

and use in AI. This regulation is expected to make it more

difficult to share patient data between multiple medical centers

and with companies involved in development of AI solutions.

Key Takeaways

While AI approaches are excellently suited to develop

algorithms for analysis of unimodal imaging data (eg,

radiological or digital pathology images), for clinical (ie,

patient-related) applications, major challenges lie in the usually

limited patient or sample numbers (N). This is in comparison

to the number of multimodal variables (P) due to patient

variation, inadequate ground truth information, and a

requirement for robust clinical validation prior to clinical

implementation. Artificial Intelligence solutions that combine

domain knowledge with data-driven approaches are, therefore,

preferable over solutions that use only domain knowledge or

are fully data driven. We introduce the following 6R model to

keep in mind for AI projects in the biomedical and clinical

health care domain:

Relevant and well-defined clinical question first.

Right data (ie, representative and of good quality).

Ratio between number of patients and their variables should

fit the AI method.

Relationship between data and ground truth should be as

direct and causal as possible.

Regulatory ready; enabling validation.

Right AI method.

Acknowledgments

We wish to thank Rien van Leeuwen and Ruud Vlutters for their valuable contributions and Ludo Tolhuizen for thorough reading

and providing valuable suggestions.

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Conflicts of Interest

All authors are regular employees of Royal Philips, Eindhoven, The Netherlands.

References

1. Chouard T, Venema L. Machine intelligence. Nature 2015 May 28;521(7553):435. [doi: 10.1038/521435a] [Medline:

26017441]

2. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in

medicine. Nat Med 2019 Jan;25(1):30-36. [doi: 10.1038/s41591-018-0307-0] [Medline: 30617336]

3. Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA 2013 Apr 03;309(13):1351-1352.

[doi: 10.1001/jama.2013.393] [Medline: 23549579]

4. Dreyer KJ, Geis JR. When machines think: Radiology's next frontier. Radiology 2017 Dec;285(3):713-718. [doi:

10.1148/radiol.2017171183] [Medline: 29155639]

5. Kruse CS, Goswamy R, Raval Y, Marawi S. Challenges and opportunities of big data in health care: A systematic review.

JMIR Med Inform 2016 Nov 21;4(4):e38 [FREE Full text] [doi: 10.2196/medinform.5359] [Medline: 27872036]

6. CB Insights. 2017 Feb 03. From virtual nurses to drug discovery: 106 artificial intelligence startups in healthcare URL:

https://www.cbinsights.com/research/artificial-intelligence-startups-healthcare/ [accessed 2019-03-04] [WebCite Cache

ID 76dBlzMnJ]

7. Itahashi K, Kondo S, Kubo T, Fujiwara Y, Kato M, Ichikawa H, et al. Evaluating clinical genome sequence analysis by

Watson for Genomics. Front Med (Lausanne) 2018;5:305 [FREE Full text] [doi: 10.3389/fmed.2018.00305] [Medline:

30474028]

8. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat

Med 2019 Jan;25(1):24-29. [doi: 10.1038/s41591-018-0316-z] [Medline: 30617335]

9. Zarringhalam K, Enayetallah A, Reddy P, Ziemek D. Robust clinical outcome prediction based on Bayesian analysis of

transcriptional profiles and prior causal networks. Bioinformatics 2014 Jun 15;30(12):i69-i77 [FREE Full text] [doi:

10.1093/bioinformatics/btu272] [Medline: 24932007]

10. Goodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge, MA: MIT Press; 2016.

11. Ghahramani Z. Probabilistic machine learning and artificial intelligence. Nature 2015 May 28;521(7553):452-459. [doi:

10.1038/nature14541] [Medline: 26017444]

12. Williams BJ, Bottoms D, Treanor D. Future-proofing pathology: The case for clinical adoption of digital pathology. J Clin

Pathol 2017 Dec;70(12):1010-1018. [doi: 10.1136/jclinpath-2017-204644] [Medline: 28780514]

13. Griffin J, Treanor D. Digital pathology in clinical use: Where are we now and what is holding us back? Histopathology

2017 Jan;70(1):134-145. [doi: 10.1111/his.12993] [Medline: 27960232]

14. Atasoy H, Greenwood BN, McCullough JS. The digitization of patient care: A review of the effects of electronic health

records on health care quality and utilization. Annu Rev Public Health 2018 Dec 19;40:1. [doi:

10.1146/annurev-publhealth-040218-044206] [Medline: 30566385]

15. Raghupathi W, Raghupathi V. Big data analytics in healthcare: Promise and potential. Health Inf Sci Syst 2014;2:3 [FREE

Full text] [doi: 10.1186/2047-2501-2-3] [Medline: 25825667]

16. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015 May 28;521(7553):436-444. [doi: 10.1038/nature14539]

[Medline: 26017442]

17. Wang H, Yeung DY. Towards Bayesian deep learning: A framework and some existing methods. IEEE Trans Knowl Data

Eng 2016 Dec 01;28(12):3395-3408 [FREE Full text] [doi: 10.1109/TKDE.2016.2606428]

18. Wang H, Yeung DY. arXiv. Ithaca, NY: arXiv; 2016 Apr 07. Towards Bayesian deep learning: A survey URL: https://arxiv.

org/pdf/1604.01662 [accessed 2019-03-06] [WebCite Cache ID 76fuYVJOg]

19. Tizhoosh HR, Pantanowitz L. Artificial intelligence and digital pathology: Challenges and opportunities. J Pathol Inform

2018;9:38 [FREE Full text] [doi: 10.4103/jpi.jpi_53_18] [Medline: 30607305]

20. Robertson S, Azizpour H, Smith K, Hartman J. Digital image analysis in breast pathology: From image processing techniques

to artificial intelligence. Transl Res 2018 Dec;194:19-35. [doi: 10.1016/j.trsl.2017.10.010] [Medline: 29175265]

21. Vink JP, Van Leeuwen MB, Van Deurzen CHM, De Haan G. Efficient nucleus detector in histopathology images. J Microsc

2013 Feb;249(2):124-135 [FREE Full text] [doi: 10.1111/jmi.12001] [Medline: 23252774]

22. Liu Y, Gadepalli K, Norouzi M, Dahl GE, Kohlberger T, Boyko A, et al. arXiv. Ithaca, NY: arXiv; 2017 Mar. Detecting

cancer metastases on gigapixel pathology images URL: https://arxiv.org/pdf/1703.02442 [accessed 2019-03-06] [WebCite

Cache ID 76fv5bego]

23. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with

deep neural networks. Nature 2017 Dec 02;542(7639):115-118. [doi: 10.1038/nature21056] [Medline: 28117445]

24. McShane LM, Cavenagh MM, Lively TG, Eberhard DA, Bigbee WL, Williams PM, et al. Criteria for the use of omics-based

predictors in clinical trials. Nature 2013 Oct 17;502(7471):317-320 [FREE Full text] [doi: 10.1038/nature12564] [Medline:

24132288]

25. IBM Watson Health. URL: https://www.ibm.com/watson/health/ [accessed 2019-03-04] [WebCite Cache ID 76dDBgH6M]

Interact J Med Res 2019 | vol. 8 | iss. 2 | e12100 | p. 6https://www.i-jmr.org/2019/2/e12100/

(page number not for citation purposes)

van Hartskamp et alINTERACTIVE JOURNAL OF MEDICAL RESEARCH

XSL

•

RenderX

26. Herper M. Forbes. 2017 Feb 19. MD Anderson benches IBM Watson in setback for artificial intelligence in medicine URL:

https://www.forbes.com/sites/matthewherper/2017/02/19/

md-anderson-benches-ibm-watson-in-setback-for-artificial-intelligence-in-medicine/ [accessed 2019-03-04] [WebCite

Cache ID 76dDZaVQF]

27. Yaffe MB. The scientific drunk and the lamppost: Massive sequencing efforts in cancer discovery and treatment. Sci Signal

2013 Apr 02;6(269):pe13. [doi: 10.1126/scisignal.2003684] [Medline: 23550209]

28. Li H, Graesser AC, Cai Z. Comparison of Google translation with human translation. In: Proceedings of the Twenty-Seventh

International Florida Artificial Intelligence Research Society Conference. Palo Alto, CA: AAAI Press; 2014 May 03

Presented at: Twenty-Seventh International Florida Artificial Intelligence Research Society Conference; May 21-23, 2014;

Pensacola Beach, FL p. 190-195 URL: https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS14/paper/view/7864/7823

29. Roweis ST, Saul LK. Nonlinear dimensionality reduction by locally linear embedding. Science 2000 Dec

22;290(5500):2323-2326 [FREE Full text] [doi: 10.1126/science.290.5500.2323] [Medline: 11125150]

30. Fodor IK. A Survey of Dimension Reduction Techniques. Oak Ridge, TN: US Department of Energy; 2002 May 09. URL:

https://www.osti.gov/servlets/purl/15002155 [accessed 2019-03-06] [WebCite Cache ID 76fyAJRY1]

31. Lakshmi Padmaja D, Vishnuvardhan B. Comparative study of feature subset selection methods for dimensionality reduction

on scientific data. In: Proceedings of the 2016 IEEE 6th International Conference on Advanced Computing (IACC). New

York, NY: IEEE; 2016 Presented at: 2016 IEEE 6th International Conference on Advanced Computing (IACC); February

27-28, 2016; Bhimavaram, India p. 31-34. [doi: 10.1109/IACC.2016.16]

32. Cooper GF, Bahar I, Becich MJ, Benos PV, Berg J, Espino JU, Center for Causal Discovery team. The center for causal

discovery of biomedical knowledge from big data. J Am Med Inform Assoc 2015 Nov;22(6):1132-1136 [FREE Full text]

[doi: 10.1093/jamia/ocv059] [Medline: 26138794]

33. Mills AM, Gradecki SE, Horton BJ, Blackwell R, Moskaluk CA, Mandell JW, et al. Diagnostic efficiency in digital

pathology: A comparison of optical versus digital assessment in 510 surgical pathology cases. Am J Surg Pathol 2018

Jan;42(1):53-59. [doi: 10.1097/PAS.0000000000000930] [Medline: 28877052]

34. Goacher E, Randell R, Williams B, Treanor D. The diagnostic concordance of whole slide imaging and light microscopy:

A systematic review. Arch Pathol Lab Med 2017 Jan;141(1):151-161. [doi: 10.5858/arpa.2016-0025-RA] [Medline:

27399211]

35. Elmore JG, Barnhill RL, Elder DE, Longton GM, Pepe MS, Reisch LM, et al. Pathologists' diagnosis of invasive melanoma

and melanocytic proliferations: Observer accuracy and reproducibility study. BMJ 2017 Jun 28;357:j2813 [FREE Full text]

[doi: 10.1136/bmj.j2813] [Medline: 28659278]

36. Calude CS, Longo G. The deluge of spurious correlations in big data. Found Sci 2017 Sep;22(3):595-612. [doi:

10.1007/s10699-016-9489-4]

37. Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated with breast cancer

outcome. PLoS Comput Biol 2011 Oct;7(10):e1002240 [FREE Full text] [doi: 10.1371/journal.pcbi.1002240] [Medline:

22028643]

38. Gill CJ, Sabin L, Schmid CH. Why clinicians are natural Bayesians. BMJ 2005 May 07;330(7499):1080-1083 [FREE Full

text] [doi: 10.1136/bmj.330.7499.1080] [Medline: 15879401]

39. Sussillo D, Barak O. Opening the black box: Low-dimensional dynamics in high-dimensional recurrent neural networks.

Neural Comput 2013 Mar;25(3):626-649. [doi: 10.1162/NECO_a_00409] [Medline: 23272922]

40. Marr B. Forbes. 2017 Sep 20. First FDA approval for clinical cloud-based deep learning in healthcare URL: https://www.

forbes.com/sites/bernardmarr/2017/01/20/first-fda-approval-for-clinical-cloud-based-deep-learning-in-healthcare/ [accessed

2019-03-04] [WebCite Cache ID 76dF5Q2HY]

41. Zhu L, Ikeda K, Pang S, Ban T, Sarrafzadeh A. Merging weighted SVMs for parallel incremental learning. Neural Netw

2018 Apr;100:25-38. [doi: 10.1016/j.neunet.2018.01.001] [Medline: 29432992]

42. US Food and Drug Administration. Digital Health Software Precertification (Pre-Cert) Program URL: https://www.fda.gov/

MedicalDevices/DigitalHealth/UCM567265 [accessed 2019-03-04] [WebCite Cache ID 76dFDAef9]

43. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: Past, present and future. Stroke

Vasc Neurol 2017 Dec;2(4):230-243 [FREE Full text] [doi: 10.1136/svn-2017-000101] [Medline: 29507784]

44. Verhaegh W, Van de Stolpe A. Knowledge-based computational models. Oncotarget 2014 Jul 30;5(14):5196-5197 [FREE

Full text] [doi: 10.18632/oncotarget.2276] [Medline: 25114037]

45. Verhaegh W, van Ooijen H, Inda MA, Hatzis P, Versteeg R, Smid M, et al. Selection of personalized patient therapy through

the use of knowledge-based computational models that identify tumor-driving signal transduction pathways. Cancer Res

2014 Jun 01;74(11):2936-2945 [FREE Full text] [doi: 10.1158/0008-5472.CAN-13-2515] [Medline: 24695361]

46. van Ooijen H, Hornsveld M, Dam-de Veen C, Velter R, Dou M, Verhaegh W, et al. Assessment of functional

phosphatidylinositol 3-kinase pathway activity in cancer tissue using forkhead box-O target gene expression in a

knowledge-based computational model. Am J Pathol 2018 Sep;188(9):1956-1972 [FREE Full text] [doi:

10.1016/j.ajpath.2018.05.020] [Medline: 30030980]

Interact J Med Res 2019 | vol. 8 | iss. 2 | e12100 | p. 7https://www.i-jmr.org/2019/2/e12100/

(page number not for citation purposes)

van Hartskamp et alINTERACTIVE JOURNAL OF MEDICAL RESEARCH

XSL

•

RenderX

47. van de Stolpe A, Holtzer L, van Ooijen H, de Inda MA, Verhaegh W. Enabling precision medicine by unravelling disease

pathophysiology: Quantifying signal transduction pathway activity across cell and tissue types. Sci Rep 2019 Feb 07;9(1):1603

[FREE Full text] [doi: 10.1038/s41598-018-38179-x] [Medline: 30733525]

48. Zarringhalam K, Enayetallah A, Gutteridge A, Sidders B, Ziemek D. Molecular causes of transcriptional response: A

Bayesian prior knowledge approach. Bioinformatics 2013 Dec 15;29(24):3167-3173 [FREE Full text] [doi:

10.1093/bioinformatics/btt557] [Medline: 24078682]

49. Gupta SK. Use of Bayesian statistics in drug development: Advantages and challenges. Int J Appl Basic Med Res 2012

Jan;2(1):3-6 [FREE Full text] [doi: 10.4103/2229-516X.96789] [Medline: 23776799]

50. van de Stolpe A, Kauffmann RH. Innovative human-specific investigational approaches to autoimmune disease. RSC Adv

2015;5(24):18451-18463. [doi: 10.1039/C4RA15794J]

51. Official Journal of the European Union. Luxembourg: Publications Office of the European Union; 2016 May 04. Legislation

URL: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=OJ:L:2016:119:FULL&from=EN [accessed 2019-03-06]

[WebCite Cache ID 76g0PXfqh]

Abbreviations

AI: artificial intelligence

FDA: US Food and Drug Administration

GDPR: General Data Protection and Regulation

MRI: magnetic resonance imaging

Edited by T Rashid Soron; submitted 03.09.18; peer-reviewed by A Davoudi, M Lang, X Shen; comments to author 08.10.18; revised

version received 18.01.19; accepted 31.01.19; published 05.04.19

Please cite as:

van Hartskamp M, Consoli S, Verhaegh W, Petkovic M, van de Stolpe A

Artificial Intelligence in Clinical Health Care Applications: Viewpoint

Interact J Med Res 2019;8(2):e12100

URL: https://www.i-jmr.org/2019/2/e12100/

doi: 10.2196/12100

PMID: 30950806

Interactive Journal of Medical Research (http://www.i-jmr.org/), 05.04.2019. This is an open-access article distributed under the

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Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.i-jmr.org/,

as well as this copyright and license information must be included.

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