Highlights
- •Challenges: Data governance, algorithm robustness, stakeholder consensus and legal liability.
- •General Data Privacy Regulation has been published to ensure high quality of data governance.
- •Model transparency, robustness and fairness are important for AI development to increase trust.
- •WHO and FDA published ethical AI technology regulatory framework to ensure safety of the AI system.
Abstract
The idea of using artificial intelligence (AI) in medical practice has gained vast interest due to its potential to revolutionise healthcare systems. However, only some AI algorithms are utilised due to systems’ uncertainties, besides the never-ending list of ethical and legal concerns. This paper intends to provide an overview of current AI challenges in medical imaging with an ultimate aim to foster better and effective communication among various stakeholders to encourage AI technology development. We identify four main challenges in implementing AI in medical imaging, supported with consequences and past events when these problems fail to mitigate. Among them is the creation of a robust AI algorithm that is fair, trustable and transparent. Another issue is on data governance, in which best practices in data sharing must be established to promote trust and protect the patients’ privacy. Next, stakeholders, such as the government, technology companies and hospital management, should come to a consensus in creating trustworthy AI policies and regulatory frameworks, which is the fourth challenge, to support, encourage and spur innovation in digital AI healthcare technology. Lastly, we discussed the efforts of various organizations such as the World Health Organisation (WHO), American College of Radiology (ACR), European Society of Radiology (ESR) and Radiological Society of North America (RSNA), who are already actively pursuing ethical developments in AI. The efforts by various stakeholders will eventually overcome hurdles and the deployment of AI-driven healthcare applications in clinical practice will become a reality and hence lead to better healthcare services and outcomes.
Keywords
Introduction
There is a veritable surge of interest in the development of artificial intelligence (AI) in medical imaging to help detect diseases and provide a prognosis [
[1]
]. A subset of AI known as “deep learning”, which uses convolutional neural networks, has shown outstanding performance in clinical diagnosis, improving hospital workflow efficiency and completing many administrative tasks [[2]
]. Healthcare information technology (IT) systems with AI technology unquestionably reduce personnel burden in healthcare institutions, free up clinicians’ time to provide better care and divert resources efficiently. Research has also focused on improving image reconstruction from sparse data, improving image resolution with reduced radiation dose and reducing image acquisition time using deep learning [3
, 4
]. For example, it was shown that using deep learning image reconstruction algorithms, the image quality reconstructed with 36.2% of radiation dose was better than the image constructed with 50.0% of radiation dose [[5]
]. The public is enthralled by the amazing capabilities of AI, in which some models may even outperform the skill of clinicians/radiologists. However, a recent systematic review of a total of 62 AI models that developed to detect COVID-19 using X-ray and CT images have, unfortunately, shown that none could be translated into clinical use due to methodological flaws [[6]
].Despite the enormous potential shown by AI in research, its deployment in the real world is still limited. Transforming healthcare with AI is not a trivial task, it requires various stakeholders to come together and resolve ethical concerns before it reaches clinical settings. There has been active discussion about ethical adoption in AI technology in healthcare applications [
[7]
] including protocols in managing patient data during AI development, integration of AI technology [[8]
], legal and regulation [[9]
]. Previous papers discuss ethical issues at a high level while we, in this paper, aim to provide a more comprehensive understanding to readers by deliberating on the pitfalls of current state-of-the-art AI algorithm in medical imaging, cybersecurity breaching in healthcare, various stakeholders’ views on AI adoption in healthcare, and guidelines published from various organizations to support AI technology development. We hope, through this reading, all stakeholders such as computer scientists, radiologists, policymakers and developers gain comprehension and knowledge about AI in medical imaging, which thus enable fostering better and effective communication for AI technology development in medical imaging. Implementing AI in clinical settings is a long-haul process, starting from data acquisition, AI model design and development, AI model validation supported with clinical evidence, approval from regulatory bodies and then only move into clinical implementation. Various challenges exist in every process. We devised the paper into four sections, in which each section represents the challenge need to be overcame before AI entering clinical settings. Fig. 1 shows the four challenges. The last section of the paper describes current guidelines in supporting ethical AI development.
Fig. 1Challenges to overcome before AI systems may have clinical application in medical imaging.
Data governance
Current approaches in AI are notoriously data-hungry and require medical data and metadata to develop a robust AI algorithm. Many AI studies adopted a retrospective approach to carry out AI research to leverage large datasets from the database, while a prospective approach is adopted to validate the developed model. For the retrospective study, waiver consent is being implemented while patient consent is required for a prospective study. In 2014, it was suggested to implement an open medical database to facilitate AI technologies developments, however, such suggestion faced a strong rejection by Europeans due to privacy concerns [
[10]
]. The Royal Free NHS Foundation Trust was accused of breaching the UK Data Protection Act 1998 when it provided the data of 1.6 million patients to Google for its DeepMind project without consent [[11]
]. A study conducted in the UK showed that the public is more comfortable sharing their health data with the government sector and university but reserved to share their data with commercial companies [[12]
]. However, if the data is made anonymously, participants are more willing to share their data [[13]
]. The public is anxious because they are not aware of how their data is being used. Improper data sharing to certain parties may cause major harm to people and result in unintended consequences. For example, insurance companies may misuse healthcare data to manipulate coverage, while pharma and biotech companies are known to pay money to collect genetic details of individuals [[14]
].Véliz C. Wellcome Trust–Funded Monographs and Book Chapters. Medical privacy and big data: A further reason in favour of public universal healthcare coverage. In: de Campos TC, Herring J, Phillips AM, editors. Philosophical Foundations of Medical Law. Oxford (UK): Oxford University Press © Carissa Véliz 2019.; 2019.
Cybersecurity in hospitals is the weakest compared with other industries because these institutions tend to invest very little in network system security [
[15]
]. The incidence of cyberattacks in healthcare has risen by 67% in Europe [[16]
] and 55% in the US [[17]
] in 2020 compared with the year before. In 2018, Singapore’s healthcare system was hacked and the prime minister’s private details were compromised [[18]
]. Networks in Malaysia and Ireland have also been targeted by hackers [16
, 19
]. Recently, the UMass Memorial Health system was hit by ransomware and more than 200 k patients' information was stolen [[20]
]. Such attacks are not only an invasion of patients’ privacy but also jeopardise hospital operations that place patients’ health at risk. For instance, the WannaCry attack in the United Kingdom National Health System has delayed critical treatments for many patients as their medical records have been encrypted by ransomware [[21]
]. Surprisingly, it was reported that human error is the main cause of data compromise, as demonstrated by the incident in the University Hospitals of Geneva (HUG) [[22]
]. Conducting training to raise awareness among all healthcare users about the basic digital-hygiene practices could reduce data privacy breaches incidents [[23]
].Data governance includes how data is stored and secured, how long is the data is kept, how does the data quality is maintained, who has access to the database, what are the policies and protocols in ensuring patients’ data confidentiality is safeguarded. Regulations such as General Data Privacy Regulation (GDPR) and California Consumer Privacy Act (CCPA) were just published to regulate AI via ensuring data is utilized appropriately. It is well known that the quality of image and labelling is vital in the development and validation of AI algorithms. The role of medical physicists, thus, is significant to ensure equipment and image acquisition protocols are appropriate to ensure image acquired complied with standards for downstream AI model development. Moreover, radiologists’ and physicians’ expertise are necessary for accurate diagnosis and labelling. A vigorous data governance protocol is thus necessary and crucial not only to protect patients’ privacy but also to warrant data quality to develop a transparent, reliable and fair AI system. Given this, we may also have to consider that the cost of AI development and implementation would be high as laborious back-end work is involved.
Algorithm robustness
Deep learning, which is a subset of AI algorithms, is widely used in medical imaging. The idea of deep learning is designed in a way in which the model consists of many layers where every layer performs mathematics operations such as convolutions, max-pooling and normalizations to extract features from data (radiology image/electronic medical record). Through these operations, useful features that aids in prediction can automatically be identified. A comprehensive review of the state-of-the-art deep learning algorithms in medical imaging applications can be found in [
24
, 25
, 26
]. Although many deep learning models have been proposed for disease screening/diagnosis using X-rays, CT or magnetic resonance imaging images, very few are adopted in clinical settings.Previous studies have shown that deep learning algorithms are vulnerable to distortions and may produce erroneous predictions [
[27]
]. Distortion refers to when inputs (image/audio/texts/other) are altered, the ‘modified’ inputs thus providing misleading information to deep learning algorithm and lead to inaccurate decisions [27
, 28
]. When random noise or patches is added to images, the prediction from the AI model results in opposite classes [[29]
]. This scenario is known as an adversarial attack in the field of computer science. One famous example is Microsoft’s Tay, a Twitter chat box, which is built for conversational purposes. When netizens flooded Tay with offensive words, it turned Tay’s tone and started to generate offensive statements. Microsoft shut down Tay in 16 h after launching []. If such an adversarial attack occurs in the healthcare system, it could lead to catastrophic consequences that may threaten patients’ life. Furthermore, adversarial attacks could also make the fabrication of false medical billings, claims or other deceptive scenarios possible [[31]
].According to a report by Fair Isaac Corporation (FICO), an analytic data company, 65% of companies are unable to give an explanation how the AI algorithm reaches its’ prediction or decision [
[32]
]. Most deep learning algorithms are designed to have deep architectures and features are expressed in high dimensions that are highly non-linear, which makes their inner mechanism difficult to dissect and understand [[33]
]. In medicine, clinicians can provide evidence and explanation as to how he/she comes to a certain diagnosis. Unfortunately, such reasoning is lacking in the deep learning algorithm. A recent study showed that that deep learning algorithm might not be learning the genuine medical pathology but other confounding features such as text in the image [[34]
]. The “non-explainability” nature of deep learning algorithms discourages and deters clinicians from adopting AI systems. Explainable AI (XAI), an emerging field in AI medicine, is attempting to decipher the reasoning and logic behind the algorithms [[35]
]. The common technique in XAI is the attention/saliency map which shows the regions that are deemed to be important for predictions [[36]
]. However, attention maps sometimes will highlight illogical regions. For example, in the COVID-19 radiograph, the attention map highlights regions outside the lung that deem to be important for predicting COVID-19 [[37]
].Real-world data intrinsically exhibit bias and using such data to train the model, inevitably, the model may be biased towards a certain group of population. This has been shown in a commercial algorithm that is widely used to guide healthcare decisions in the US where it was found to be discriminative against Black patients [
[38]
]. In fact, according to a global survey, model fairness is the most ubiquitous principle in developing AI systems [[39]
] to avoid unfair outcomes based on race and socioeconomic class [, 41
, 42
]. Furthermore, AI models were found to have poor generalisability [[34]
]. When input data from different devices/institutions/regions were used, model performance reduced significantly. AI model developed by IBM Corp to provide recommendations on cancer treatment did not perform well in poor and middle-income regions, like India and Southeast Asia, due to variation of medical practices between developing and developed nations [[43]
].During AI development, data often requires pre-processing before training the AI model. Generally, data will be split into training and testing datasets in which the testing data will be used to evaluate the model performance. It is critical to provide clear descriptions of the data source, data characteristics and data handling process to ensure sufficient transparency. 85% of the international AI challenges did not provide data source information [
[44]
] and improper splitting of the data will result in the over-optimistic performance of up to 41% [[45]
].Data source reporting is thus crucial to ensure model transparency and enable evaluation of the fairness and generalisability that may assist in comprehending the potential benefits and harm of the AI systems Recently, the Radiological Society of North America (RSNA) has released a “Checklist for AI in Medical Imaging” (CLAIM) to promote standardised reporting. This checklist is supposed to be viewed as a “best practice” for authors and reviewers in future journal publications to improve the quality of AI papers in medical imaging [
[46]
]. With clear model transparency, it will also empower clinicians to assess the reliability of results produced by the algorithms.Stakeholder consensus
Understanding the concerns and needs from different stakeholders’ perspectives is an important aspect to drive value creation to the healthcare system and social benefits. Among the stakeholders involved are those who directly deal with patients, such as radiologists, clinicians and healthcare workers, and those who work “behind the scenes” like hospital managers and policymakers (lawyers/ethicists/computer scientists/government).
Sun and Medaglia published a report on the opinion of three main stakeholders in China (government policymakers, hospital management and IT firm managers) to elucidate the challenges of AI adoption in the healthcare system [
[47]
]. Their findings indicate that different stakeholders recognised AI challenges differently. IT firm managers are worried about the data being misused for other commercial purposes, while government policymakers and hospital management are concerned about the trustworthiness of the AI algorithm [[47]
]. In Europe, a short survey by Güngör showed that business entities have positive feedback towards AI deployment but society perceived AI as bringing more risk than value [[48]
]. Puaschunder conducted a survey, on a group of stakeholders consisting of academia, business, healthcare professionals, public and legal representations, to investigate the use of AI, robotics and big data in healthcare. They showed that all parties perceived positively towards the introduction of AI and big data and neutral towards policy recommendations [[49]
].Failure in implementing AI systems in hospitals was largely due to the lack of socio-technical consideration instead of technical factors [
[50]
]. Clinicians are concerned about the loss of autonomy and the capacity to make critical decisions due to a lack of legal and policy framework on data privacy and accountability [[51]
]. Surveys showed that more than 50% of radiologists have limited knowledge of AI [52
, 53
]. Similar findings were also reported in Yang et al. in which their survey showed that most stakeholders expressed education on AI is needed to enhance collaborative work in AI implementation [[54]
]. Furthermore, some studies reported that healthcare workers are hesitant to accept new technologies due to the fear of altering their routine that may lead to increased workload [55
, 56
, 57
, 58
]. At the same time, medical physicists express strong interest in improving their AI skills [[59]
]. EFOMP recently published a paper discussing the aspects in which how medical physicists can equip themselves to familiarize themselves with AI technology [[60]
] and effort has been made to introduce AI in medical physics curriculum [- Kortesniemi M.
- Tsapaki V.
- Trianni A.
- Russo P.
- Maas A.
- Källman H.-E.
- et al.
The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession.
Physica Medica: European Journal of Medical Physics. 2018; 56: 90-93
[61]
].Technology is advancing faster than others such as legal matters that they cannot keep up. However, to drive AI technology into clinical settings, efforts from all stakeholders are inevitable to develop a holistic approach in designing a proper legal AI regulation and policies to safeguard individual empowerment and equity. Recently, Lebcir et al. started gathering perspectives from different organisations in affecting AI adoption in healthcare, which is still ongoing [
[62]
]. The findings from their review may eventually help to identify organisational factors that may improve AI implementation in healthcare settings.Legal liability
Traditionally, under tort law, patients may sue for monetary compensation if they are harmed or injured due to the negligence of medical practitioners. However, medical device companies are protected from such action due to the “learned intermediary doctrine”. As such, the onus is on clinicians to fully understand the potential risk that entails the use of medical devices because they have the capability to weigh the risk and benefits of using those devices. Failure to properly assess the use of a medical device may become a basis for patients to take legal action in cases of medical negligence [
[63]
]. Note that if clinicians followed the standards of providing care, they will likely not be liable for the outcome of patients.The potential of AI in revolutionizing the healthcare system is enormous and centuries-old legal and regulation must keep pace with the technological advancement. Given the non-explainability and non-transparency of AI models, it is difficult to identify where is the fault originated from when erroneous decisions are made by the algorithm and cause adverse impacts on patients’ health. When lawsuits are filed by the patients, legal questions like this example have been raised – “Who will be responsible when there is medical malpractice due to the defective AI technology?”.
Current guidelines for ethical AI development
Many organisations have published guidelines to support the ethical development of AI in healthcare settings. The World Health Organisation (WHO) has issued its first guidelines in using AI in healthcare entitled “The Ethics and Governance of Artificial Intelligence in Health Care” [
[64]
]. The United States’ Food and Drug Administration has also published its guidelines entitled “Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning-Based Software as a Medical Device – Discussion Paper and Request for Feedback” to support the development of AI algorithms as a medical device [[65]
]. Medical institutions in Europe and North America (the American College of Radiology, the European Society of Radiology, the Radiological Society of North America, the Society for Imaging Informatics in Medicine, the European Society of Medical Imaging Informatics, the Canadian Association of Radiologists and the American Association of Physicists in Medicine) have published a joint statement relating to AI ethics in radiology, in which they highlighted that AI should be used to help patients for the common good and not be used for commercial purposes [FDA. Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback. Available at: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device. Accessed at Dec 01, 2021.
[66]
]. Australia [[67]
], Canada [[68]
], France [[69]
], and the UK [[70]
] have also engaged in the development of a national AI ethics framework.The European Union has published a guideline for the ethical development of AI and summarised seven requirements [
[71]
]. The requirements are:- (i)Humans should have autonomy over the system and intervention is possible to monitor the decisions made by the system;
- (ii)Robust and reliable algorithm. The AI system should be able to withstand adversarial attacks;
- (iii)Ensuring data privacy and good governance;
- (iv)Model transparency. The data and algorithms used to create an AI system should be transparent, and the decisions made by the systems are justifiable.
- (v)Fair output and results, in which all decisions made by an AI system must be diverse and not biased;
- (vi)Environmentally sustainable, where the output must take ecological and societal well-being into account to enhance positive social change; and,
- (vii)Model accountability, in which AI systems should be auditable.
Conclusion
We have seen many breath-taking AI applications that show encouraging results in terms of diagnosis, prognosis and facilitation of clinical workflow. However, we need to be cognisant of the fact that AI in medical imaging must overcome the challenges but not limited to those outlined in this paper before it can be implemented in clinical practice. With the contributions of researchers, novel, robust, secure, and safe AI innovations will continue to emerge.
Medical physicists apply knowledge of physics in medicine and have expertise in computing. They have contributed to computerization and advancing digital techniques in imaging and therapy. In AI, they too could contribute to the design and develop a relevant and appropriate AI model to ensure data quality, model fairness and model transparency. They could advise and collaborate with clinicians in AI. Furthermore, the proposal of including AI in the medical physics curriculum will be beneficial for better preparing the next generation of medical physicists to design and develop a clinically relevant AI model that can create impact.
Various organisations, such as WHO and FDA, are also making proactive efforts in the pursuit of developing trustworthy AI policies and regulatory frameworks to foster trust and adoption of AI in society. There are already 29 FDA-approved AI-based medical technologies [
[72]
]. Undoubtedly, we will be seeing more AI inventions in the near future and when AI algorithm is applied in clinics, there will be a paradigm shift in healthcare management. AI-driven healthcare technology will certainly empower radiologists and clinicians to be more efficient and has the potential to improve human well-being.Funding
This research was supported by Dr. Ranjeet Bhagwan Singh Grant (GA017, PI: Saw).
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Article Info
Publication History
Published online: June 14, 2022
Accepted:
June 11,
2022
Received in revised form:
April 26,
2022
Received:
December 6,
2021
Identification
Copyright
© 2022 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
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