# ACIPS: A Framework for Evaluating Patient Perception in the Introduction of
AI-Enabled Healthcare

Chinasa T. Okolo,1 Michelle González Amador 2

###### Abstract

In healthcare, the role of AI is continually evolving and understanding the
challenges its introduction poses on relationships between healthcare
providers and patients will require a regulatory and behavioural approach that
can provide a guiding base for all users involved. In this paper, we present
ACIPS (Acceptability, Comfortability, Informed Consent, Privacy, and
Security), a framework for evaluating patient response to the introduction of
AI-enabled digital technologies in healthcare settings. We justify the need
for ACIPS with a general introduction of the challenges with and perceived
relevance of AI in human-welfare centered fields, with an emphasis on the
provision of healthcare. The framework is composed of five principles that
measure the perceptions of acceptability, comfortability, informed consent,
privacy, and security patients hold when learning how AI is used in their
healthcare. We propose that the tenets composing this framework can be
translated into guidelines outlining the proper use of AI in healthcare while
broadening the limited understanding of this topic.

## Introduction

One of the defining characteristics of modern societies is the embeddedness of
technology in most aspects of human life. Understanding how humans respond to
technological innovations is essential for the successful introduction of
digital tools in human-welfare centered fields such as healthcare, education,
and labor markets. In healthcare, the role of artificial intelligence (AI) is
continually evolving and understanding the paradigm shift of traditional
medical relationships from physician-patient to physician-AI-patient will
require a comprehensive awareness of existing human-human interactive
structures in medicine and the challenges the introduction of AI poses to
them. We find that a technological approach to healthcare comes with its own
set of challenges. In particular, we focus on the complex social structure
that emerges from physician-AI-patient interaction. Studies that look at
users’ response to the introduction of AI-enabled tools in human-welfare
centered fields, indicate the outcome of the interaction is contingent on the
level of complexity and type of the task, the expertise of the user, and the
mental models users have formed about the fairness of the tool (Kaufmann and
Budescu 2020; Lee 2018a; Sundar and Nass 2001; Cowgill 2018). Begetting mental
models that ignite a cooperative interaction between healthcare providers and
patients will require a regulatory and behavioural approach that can provide a
guiding base for all users involved (Emanuel and Wachter 2019; Terry 2019).

We expand work exploring the ethical concerns of AI in healthcare, patient
perception of AI software, and the impact of trust and privacy in technology
acceptance for healthcare by developing ACIPS, a framework to guide the
introduction of AI-enabled technologies in healthcare settings. Our framework
aims to standardize the practice of evaluating patient reception to AI
integration within healthcare and establish the foundation for regulatory
policies that will enforce the fair use of AI systems in accordance with
existing healthcare standards such as Health Insurance Portability and
Accountability Act of 1996 (HIPAA) in the United States and the UK’s Data
Protection Act 2018.

## Related Work

### AI in healthcare

In the provision of healthcare services, artificial intelligence (AI) is
ubiquitous. With applications in medical imaging (Zhou et al. 2019; Kwan,
Cheung, and Gibson 2000; Roblot et al. 2019; Bhanumurthy and Anne 2014),
personalized medicine (Awwalu et al. 2015; Schork 2019), drug discovery
(Altae-Tran et al. 2017; Jing et al. 2018; Klucznik et al. 2018; Feinberg et
al. 2018; Wu et al. 2019), epidemiology (Barrett, Eubank, and Marathe 2008;
Apolloni et al. 2009; Wakamiya, Kawai, and Aramaki 2018), and operational
efficiency (Rigby 2019), AI is undeniably changing the healthcare landscape
for the better. Within this domain, literature discussing the ethical
concerns, perceptions of, and implications of AI in healthcare have become
more prominent. For example, McCradden et. al analyze perspectives from adult
patients with brain cancer regarding issues of consent around their health
data, the allocation of health resources through computational methods, and
privacy concerns associated with using their data for health research
(McCradden et al. 2020). Liyanage et. al survey healthcare informaticians and
clinicians on the benefits, risks, potential of adoption, implications, and
the future of AI in primary care (Liyanage et al. 2019). Lai et. al interview
a range of French professionals with an interest in or experience with AI for
healthcare (physicians, AI researchers, AI consultants, ethicists, public
health researchers, etc.) to gain insight into the needs, opportunities, and
challenges arising from the use of AI in healthcare (Laï, Brian, and Mamzer
2020). Further work done by Xiang et. al examines the difference in AI
sentiment between healthcare and non-healthcare workers regarding the safety
of AI, consistency between physician and AI-based diagnosis, receptivity to AI
being used in their care, the respective demand for AI in healthcare, and
opportunities for AI to improve operational aspects of patient care (Xiang et
al. 2020). This corpus shows that as AI becomes more persistent throughout
healthcare, it is equally as important to study the social and ethical impact
AI has within healthcare along with the technical specifications of AI-enabled
technologies.

### Clinician-patient interaction with AI

The debate surrounding the effect of technological innovations on the
clinician-patient relationship is not new (Boucher 2010; Botrugno 2019).
Studies show a mixed perspective, with most research agreeing on the
management, coordination and efficiency benefits of technology in healthcare
(Mandl, Kohane, and Brandt 1998; Boucher 2010; Gustafson et al. 2001), while
the strand of research interested in the interaction between physicians and
patients documents the challenges that emerge with digital means of healthcare
communication(Mandl, Kohane, and Brandt 1998) and healthcare provision (Murray
et al. 2003). For instance, Botrugno discusses that while Information
Technologies (IT) may ease communication between a clinician and her patient,
it could also unintendedly dehumanize it and worsen it, since the lack of
physical presence decreases the number of cues a doctor has to evaluate her
patient’s health (Botrugno 2019).

In the same vein, concerns arise about service providers’ ability to engage in
positive cooperation between clinicians, AI, and patients (Taddeo and Floridi
2018; Terry and Cain 2016; Rigby 2019; Aminololama-Shakeri and López 2019).
Terry and Cane put forth the idea that the increasing use of digital means of
healthcare provision is eroding clinician-patient empathy and the associated
positive health outcomes that come from this interaction. Moreover, Morley et
al., warn that an over-reliance or mismanagement of AI can lead to the
impersonalization of healthcare provision, and consequently a decrease in
trust and clinician-patient empathy (Morley et al. 2020; Juengst et al. 2016).
This is compounded by the fact that the line that separates AI tools in
healthcare as “decision support systems” and “decision making systems” can be
blurry under certain circumstances. Emanuel and Watcher argue that the promise
of AI in healthcare is deliverable only if we are able to establish a positive
behavioural approach. For example, by developing strategies that encourage
empathy and trust in the digital realm (Terry and Cain 2016), such as
identifying and designing a set of fundamental ethical guiding principles
(Taddeo and Floridi 2018; Terry 2019), and utilizing AI in a strategic way
such that not only doctor-AI-patient interactions become more efficient, but
doctor-patient interactions improve as well (Shirado and Christakis 2017;
Aminololama-Shakeri and López 2019). Recent work has examined the potential of
patient-centered approaches in AI-based healthcare (Findley et al. 2020),
showing that clinicians have a significant role in facilitating trust to
normalize the use of AI systems.

### Trust in AI

Trust is generally understood as an essential element for the well-functioning
of healthcare systems and clinician-patient relationships. Low trust levels
are associated with a host of negative health and healthcare outcomes such as
poorer health status, decreasing adherence to medications, shorter
relationships with doctors, among others (Graham et al. 2015). Conversely,
higher levels of trust are associated with better healthcare utilization and
health outcomes, such as increased commitment to appointments and outpatient
visits, and decreased emergency visits (Whetten et al. 2006; Bodenlos et al.
2007).

Canalizing trust in AI-enabled healthcare has great potential for clinicians
and patients alike. In terms of operational efficiency, it can decrease wait
time for patients (Kennedy 2018) and decrease health-related information
asymmetries (Sillence et al. 2004; Weaver III et al. 2009; Factors and Society
2014; Murray et al. 2003). The latter is a step in the right direction towards
increasing patient adherence to prescribed treatment, in combination with a
healthy relationship with the prescribing physician (Weaver III et al. 2009;
Emanuel and Wachter 2019; Murray et al. 2003). Moreover, the use of AI in
clinical support systems has clear advantages in diagnosis, treatment
selection and monitoring (Ngiam and Khor 2019); what is more, there is a
positive consequence of the usage of support systems that is not often talked
about. This form of AI offers relative anonymity to users and, along with
other means of digital communication, decreases the need for face-to-face
interaction between patients and carers. Anonymity has been proven to increase
users’ self-disclosure on otherwise sensitive topics, including emotions and
concerns (Clark-Gordon et al. 2019). This secondary feature is especially
relevant for settings where health-related topics might be taboo and therefore
difficult to discuss with carers for fear or shame. For instance, in India and
Bangladesh (among other developing nations), sexual and reproductive health is
very much a taboo, and shame and stigma around it contribute to young girls’
negative health outcomes (Tellier and Hyttel 2018). Preliminary studies on the
usage of health digital applications (e.g. mHealth) show that these tools are
widely accepted by users in Bangladesh and India; however, digital literacy is
still low and in person consultation still plays a crucial role in the
development of better health service provision that is in accordance with
local norms and patients’ ethics and values (Alam et al. 2017; Messinger et
al. 2017).

### Technology Acceptance Models

The theory behind the Technology Acceptance Model (TAM) was introduced by
Davis et. al and outlines how users perceive and eventually come to accept
technology (Davis, Bagozzi, and Warshaw 1989). This model proposes that users
rely on two factors when evaluating new technologies: perceived usefulness
(PU) of the technology and the perceived ease-of-use (PEOU) of the technology.
Additionally, Davis et. al find that external variables such as social
influence have an impact on users attitudes concerning a respective
technology.

Figure 1: The original Technology Acceptance Model (TAM) as outlined by Davis
et. al (Davis, Bagozzi, and Warshaw 1989).

While TAM has been expanded to account for various social and cultural factors
that impact technology acceptance, it continues to serve as a guide for
technology integration into fields ranging from agriculture to education
(Scherer, Siddiq, and Tondeur 2019). Work by Hu et. al, perhaps some of the
earliest literature on technology acceptance for healthcare, expands on the
TAM framework to examine physician reasoning behind their intentions to use
telemedicine technologies (Hu et al. 1999). This work finds that as a whole,
TAM reasonably captures physician acceptance of new technologies, but some
factors such as perceived ease-of-use did not determine attitudes. This
finding, along with work done by Emad et. al (Emad, El-Bakry, and Asem 2016)
show the need for a technology acceptance model able to capture the unique
subtleties associated with technology acceptance within healthcare. Emad et.
al propose a modified technology acceptance model tailored towards healthcare
informatics and run quantitative experiments in the UK and Iraq using their
model. Again, perceived usefulness is found to be a significant factor for
indicating the likelihood of technology acceptance. However, this work from
Emad et. al highlights the need to examine cultural influences impacting the
acceptance of novel technologies (Emad, El-Bakry, and Asem 2016). Nadri et. al
use the extended Technology Acceptance Model (TAM2) by Venkatesh and Davis
(Venkatesh and Davis 2000) in studies in Iran, further highlighting the need
for TAMs to be tailored towards the respective environment and target
populations of a healthcare setting where new technologies will be introduced
(Nadri et al. 2018). Continuing with the trend of TAM in healthcare, Alhashmi
et. al futher extend TAM for a study in the United Arab Emirates and find that
factors such as the managerial, operational, and organizational makeup of
healthcare facilities, along with their IT infrastructure impacts the
integration of technology in this domain (Alhashmi, Salloum, and Mhamdi 2019).
Dhaggara et. al shift from approaches taken by similar work, and focus on the
acceptance of technology in healthcare by patients rather than physicians
(Dhaggara, Goswami, and Kumar 2020). Their work augments TAM and shows that
privacy and trust are also significant factors that impact technology
acceptance, providing a solid base for the ACIPS framework introduced in this
paper. As TAM continues to expand to different domains, more recent work has
focused on the acceptance of AI as a technology. In their work, Sohn and Kwon
test a variety of technology acceptance theories, finding that the acceptance
of AI-based technologies are more easily modeled by VAM, a values-based
technology acceptance model incorporating factors such as enjoyment,
usefulness, and economic feasibility (Kim, Chan, and Gupta 2007), rather than
the popular TAM framework (Sohn and Kwon 2020). Our work contributes to prior
literature on technology acceptance and expands on its applications to both
healthcare and AI, which have not been explored as often.

Figure 2: The Value-based Adoption Model by Kim, Chuan, and Gupta (Kim, Chan, and Gupta 2007). Table 1: AI Typology | Administrative | Diagnostic |  |
---|---|---|---|---
Patient-facing |  | \- Virtual Assistants for waiting rooms
---
\- Health monitoring
\- Medication management
\- AI-driven robots assisting with care
| \- Computer-aided diagnosis (CADx, image processing)
---
\- Digital consultations (clinical decision support systems, CDS)
\- Brain-Computer interfaces (BCI)
\- Group-level disease prevention
\- Individualized precision medicine (with AI)
\- Robot-assisted surgery
|
Non patient-facing |  | \- Bed allocations
---
\- Scanning of Electronic Patient Records
\- Staffing optimization: level and hiring
\- Simulations for Medical Education
\- Forecasting demand for medical resources
\- Fraud detection
\- Cybersecurity for health data
| \- Medical research and drug discovery (e.g. electrophysiology)
---
\- Analyzing clinical laboratory results
|
Note: based on (Jiang et al. 2017), (Arora 2020) and (Kalis, Collier, and Fu
2018).

### Explainable AI

Within the field of AI, explainability has become a major topic of interest.
With the complexity of the models used to train AI increasing, it has become a
struggle to comprehend decisions made by AI from the side of the practitioner
and end user. Within the field of explainable AI, a growing body of work is
focused on examining the decisions made by AI and improving methods of making
AI explainable to both technical experts and novice users. Work by Hoffman et.
al addresses the question of the effectiveness in explaining AI systems to
users and propose measures that gauge the quality of explanations, user
satisfaction of the explanations, user comprehension, and other factors such
as the suitability of user trust and reliance on AI systems (Hoffman et al.
2018). Paul Voosen highlights the challenge of embedding explainability into
AI systems, demonstrating the tradeoffs that often occur between accuracy and
ensuring that AI systems are transparent (Voosen 2017). XAI, a system
developed by DARPA consists of three approaches to assist in the development
of explainable AI models that maintain accuracy as the level of explainability
increases (Gunning 2017). Additionally, this system will ensure that human
users both understand and trust AI. As the need for explainable AI heightens,
a growing body of work has begun to focus on explainable AI for healthcare.
Lamy et. al extend methods for analogical reasoning to propose visual
techniques for classifying similar breast cancer cases that include both
quantitative and qualitative similarities (Lamy et al. 2019). Other work in
the healthcare domain has explored the development of explainable AI tools for
simulation training in surgery (Mirchi et al. 2020), predicting acute critical
illnesses from electronic health records (Lauritsen et al. 2020), predicting
low blood oxygen levels during surgery (Lundberg et al. 2018), digital
pathology (Tosun et al. 2020; Jaume et al. 2020; Uehara et al. 2020), and much
more. Motivated by the legal and privacy aspects of deploying AI within
healthcare, Holzinger et. al propose methods for explainable AI including
expanding the capability of machine learning techniques to learn models that
contain interpretable and causal features, introducing participatory methods
where AI decisions would not overrule human-based medical decisions, and
developing constructive user interfaces (Holzinger et al. 2017).

The explainability of AI should also consider the incentives and targets the
particular algorithm is trying to optimise. In the context of image
recognition it is clear that the tools outcome is (or at the most, should be)
to precisely identify a particular pattern of pixels, linked to a medical
condition. In the case of developing a diagnostic, however, it is possible for
an AI application to include cost-cutting mechanisms in its target function.
This means that a possible diagnosis or recommendation might push a patient in
a specific direction without further consideration by a human.

Becker (Becker 2019), expanded by Bohr and Memarzadeh (Bohr and Memarzadeh
2020) suggests the four following goals for any healthcare-related artificial
intelligence tool:

  1. 1.

Assessment of disease onset and treatment success.

  2. 2.

Management or alleviation of complications.

  3. 3.

Patient-care assistance during a treatment or procedure.

  4. 4.

Research aimed at discovery or treatment of disease.

Indicating that AI tools for healthcare aim to improve a patient’s life.
Kakarmath et al. (Kakarmath et al. 2020) propose a list of best practices for
research using AI. Our proposed framework aligns with these authors,
incorporating techniques to make AI deployed within healthcare contexts
”explainable” and expands work on explainable AI in healthcare, with a focus
improving both healthcare provider and patient understanding of AI.

## AI Typology

The more common AI typology in healthcare follows a classification based on
input data type (structured and unstructured) and subsequent analytical
method, or departmental use of AI (e.g. workflow, care). However, when it
comes to implementing an ethical framework around AI structures, we need to
understand when AI comes in direct contact with patients, or when it affects
them indirectly, through its role as an enabling technology. We present a
typology for AI in healthcare based on directedness of contact with patients,
ergo facilitating ethical framework implementation in general, and ACIPS
specifically.

We believe that the introduction of our AI typology system will allow us to
focus on separate AI-enabled tools based on their functions in the healthcare
context and how it changes physician-AI-patient interaction and allow us to
specify how we would apply ACIPS to each respective context. For example, when
selecting the pool of hospitals that we could work with in our experimental
evaluation of the framework, using this system would first let us categorize
what types of AI systems each hospital has implemented and allow us to focus
on applying our ACIPS framework to comparable subsets.

## ACIPS Framework

In their AIES paper, Impacts on Trust of Healthcare AI, LaRosa and Danks
acknowledge the need for the establishment of standards that assist in
maintaining trust between patients and healthcare providers (LaRosa and Danks
2018). An extensive search of prior literature reveals a lack of
standardization regarding the introduction of AI in healthcare and other
domains. For AI-enabled technologies to have a significant impact within
healthcare, we believe it is necessary for it to both be understood and
accepted by patients. In this paper, we propose a framework to measure patient
perception surrounding AI-enabled technologies and assist in setting
guidelines for how AI should be formally introduced to patients in healthcare
settings. The core principles of this framework are broken down into 5 parts:
Acceptability, Comfortability, Informed Consent, Privacy, and Security. For
brevity, the framework will be referred to as ACIPS. The steps to implement
the principles within ACIPS are detailed below and are also summarized within
Figure 3.

Figure 3: An outline of the five principles that construct the ACIPS framework
and their respective implementation steps.

### Acceptability

The condition of acceptability within healthcare contexts can provide
significant insight into the effectiveness of a specific intervention, but has
been defined ambiguously throughout healthcare literature (Sekhon, Cartwright,
and Francis 2017). In their work, Sekhon et al. develop a comprehensive
theoretical framework for acceptability that encompasses the subjective
evaluations made by patients who experience and healthcare practitioners who
deliver a respective intervention. With respect to the healthcare domain, Dyer
and Robinson synthesize the term acceptability into a definition that embraces
the aspects of experiential healthcare treatment and social validity (Dyer,
Owens, and Robinson 2016). With this prior work in mind, we craft the
principle of Acceptability in the ACIPS framework to elucidate how AI-enabled
technologies conform to a patient’s ethical values and expectations. It has
been shown that patients are more receptive to treatment recommendations,
leading to improved clinical outcomes (Hommel et al. 2013). If patients are
given the right to explicitly accept that a healthcare intervention enabled
with AI will be used in their care, this will lead to greater trust of these
respective systems and possibly enhanced quality of care.

#### Acceptability Procedure

To begin the deployment of the ACIPS framework in a real-life setting, the
Acceptability principle will be evaluated to measure how accepting a patient
is of a proposed AI-enabled intervention being introduced for use in their
healthcare. The evaluation methodology will include at least five questions
where the possible range of answers will be based on a modified Likert scale
with five options: Acceptable, Slightly acceptable, Neutral, Slightly
unacceptable, and Unacceptable. A sample list of questions is depicted below:

  1. 1.

How acceptable is having your medical professional use AI on you?

  2. 2.

How acceptable is this technology being used in your treatment?

  3. 3.

How acceptable is this technology being used in place of your primary care
physician?

  4. 4.

How acceptable is this technology taking data from you?

  5. 5.

How acceptable is this technology having access to your personal health data?

### Comfortability

Another important aspect of the ACIPS framework is considering how comfortable
patients are with AI-enabled technologies being incorporated and used within
their healthcare. While comfortability is often overlooked in the informed
consent process, it is a key part of the patient experience (Wensley et al.
2017). Pelvic examinations are particularly known to be challenging
experiences for both physicians and female patients and can be eased by using
a plastic speculum (an instrument used to widen an orifice for inspection)
over a metal one (Kozakis, Vuddamalay, and Munday 2006). Unfortunately, when
receiving gynecological exams, selection of a speculum is often left to the
preference of the provider with little regard to patient choice (Bates,
Carroll, and Potter 2011). We believe that if patients are given the
opportunity to express their level of comfort in how AI-enabled technologies
are used in their treatment, this will lead to improved patient healthcare
outcomes and a stronger understanding of perceptions of AI in these domains.

#### Comfortability Procedure

The Comfortability stage provides a place where a patient can express their
respective feelings of comfort regarding the AI-enabled healthcare
intervention to the medical professional. In this process, the medical
professional will administer a qualitative survey consisting of a modified
Likert scale to gauge how comfortable the patient is with the AI intervention
and its associated implications. Similarly to the principle of Acceptability,
in this survey, the medical professional will ask the patient five questions
with the possible range of answers being Comfortable, Slightly comfortable,
Neutral, Slightly uncomfortable, and Uncomfortable:

  1. 1.

How comfortable are you with this medical professional using AI on you?

  2. 2.

How comfortable are you with this technology being used in your treatment?

  3. 3.

How comfortable are you with this technology being used in place of your
primary care physician?

  4. 4.

How comfortable are you with this technology taking data from you?

  5. 5.

How comfortable are you with this technology having access to your personal
health data?

### Informed Consent

The informed consent process in healthcare has traditionally been opaque due
to the lack of medical knowledge the average person has, but recent
advancements to improve this process have included multimedia interventions
such as videos and interactive computer software or written information like
informational leaflets or pamphlets (Paton et al. 2018). More novel approaches
such as eConsenting (the practice of using digital devices such as mobile
phones or computers in the informed consent process) have come into prominence
across multiple areas of healthcare, but standards of this practice have yet
to be implemented (Lunt et al. 2019). However, the process of informed consent
is further convoluted by the lack of transparency provided when attempting to
understand how or why certain equipment is being used in patient care. When
consenting to receiving medical treatment, patients generally consent to the
overall process of receiving a respective treatment and have no liberty in the
selection of the type of medical equipment or brands of medical supplies
involved in their care. While considerable effort is taken upon physicians and
other medical personnel to apprise patients of the possible benefits and/or
detriments of a respective procedure, there is little transparency about the
tools used in these procedures. When introducing the principle of Informed
Consent in the ACIPS framework, ensuring that patients are informed about the
presence and utility of AI within software or equipment being used in their
care and that they consent to these technologies was of high importance.

#### Informed Consent Procedure

The principle of Informed Consent is one of the most important within the
ACIPS framework. In the implementation of this principle, the medical
professional will begin by ensuring that the patient has been informed about
the presence of AI in the equipment and/or software being directly used in
their care. Following this, the patient will then be briefed on the purpose of
the AI system and the exact functions it will perform throughout this process.
After this briefing, the medical professional will begin the process of
seeking consent from the patient to proceed with using the AI-enabled
technology in their care. It is important for the medical professional to
acknowledge that consent is dynamic process and can be revoked at any time. If
the patient chooses to not go forward with this respective AI-enabled
intervention, their choice should be respected and a non-AI equivalent should
be made available for use. Throughout this process, we stress that the medical
professional is transparent with the patient at all times in regards to how
they illustrate the respective capabilities and risks of the AI intervention.
Based on the available resources of the medical facility and operating
procedures for obtaining authorization for medical care, consent from the
patient will be confirmed through either a digital or handwritten (on paper)
signature.

Table 2:  A breakdown of the principles within the ACIPS framework and questions guiding their implementation. Principle |  Related Question(s)
---|---
Acceptability |  Does the patient accept that these technologies will be used on them/incorporated into their treatment?
Comfortability |  Is the patient comfortable with these technologies being used on them/incorporated into their treatment?
Informed Consent |  Does the patient understand the benefits and possible risks of the AI-enabled technology being used in their healthcare? Does the patient consent to having these AI-enabled technologies used on them/incorporated into their treatment?
Privacy |  What data is collected from the AI-enabled technology? How is the data kept private? Who has access to this data (patient, provider, technology company, insurer, etc.)? How will this data be used and where will it be stored? What agency does the patient have over this data?
Security |  How is the data collected from the AI-enabled technology kept secure?

### Privacy and Security

The issues of privacy and security in the healthcare context are of extreme
concern. Over the past few years, data breaches such as hacking, malware
attacks, and phishing have affected healthcare systems around the world almost
incessantly (McLeod and Dolezel 2018). As the need for machine learning
systems to be trained on extraordinarily large amounts of data increases and
the opportunities for contributing personal health data to these systems grow,
it is imperative that patients are aware of the implications associated with
engaging in AI-enabled technologies. When designing the principles of Privacy
and Security in the ACIPS framework, it was important to establish that
patients would know what and how personal data is collected from AI-enabled
technologies, how this data is securely and privately kept, and the autonomy
that can be exercised in the management of their personal data. What is most
important is that patients understand that they maintain agency over their own
data. To maintain trust, especially when interacting with emerging
technologies like artificial intelligence that are relatively complex, it may
be important for patients to have ownership of their personal healthcare data,
where they are allowed to control privileges associated with the access, use,
storage, and possible deletion.

#### Privacy and Security Procedure

To implement the principles of Privacy and Security in real-life settings,
there are a few steps that medical professionals should complete to ensure
they are in compliance with the ACIPS framework. In this stage, the medical
professional will begin by informing patients of what data is being collected
from them while the artificially intelligent system is being used in their
care. During this step, the patient should be encouraged to ask questions
pertaining the data collection process. Following this, the medical
professional will explain how this data is collected and will describe where
this respective data will be stored (locally in hospital data storage centers,
in the cloud, etc.). Finally, the medical professional will ensure that the
patient understands what access they have to their own data along with
explaining how this data can and will be used outside of their direct care.
With respect to the Informed Consent principle of the ACIPS framework,
patients will again have the option to consent to the use of their data by
second or third parties. This option is exclusive to the consent given for the
use of this technology, meaning that patients have the right to object to
their data being used outside of their direct care while still receiving
treatment from the AI-enabled technology.

## Case Study

To conceptualize how ACIPS would be deployed and to prepare for the various
contexts in which ACIPS could be deployed in, we will present three
hypothetical scenarios below:

### Scenario I

Client A is a large research hospital in the United States that recently
bought a $1.5 million AI system to install into their CT scanners to
automatically scan for brain tumors. The radiologists and technicians employed
in this hospital are well-versed in AI techniques and welcome its use within
their respective diagnostic workflows. The patients who visit this hospital
are from middle to high-income backgrounds and are generally college-educated.
The patients themselves are not experts of AI, but have heard or read about it
the news. Some patients work in technical fields as software engineers, data
architects, etc. and may have firsthand experience with artificial
intelligence through their professions. Client A has decided to adopt the
ACIPS framework in alignment with recently mandated government regulations
outlining the use of AI in medical equipment.

### Scenario II

Client B is a clinic in the Philippines that has been collaborating with
researchers from an industry research lab in their country. The researchers
have developed an AI system to automatically scan for eye diseases and want to
pilot it in the clinic. We assume that the nurses, the healthcare workers in
charge of administering the eye scans, are unfamiliar with AI and have never
used it before asides from apps with recommendation systems like Facebook or
YouTube. The patients who visit this clinic work low to medium-wage jobs and
have experience using technological devices such as mobile phones or
computers. On average, the patients have no prior knowledge of AI and have
similar experiences with using AI in apps to the nurses. In this pilot, the
researchers from the industry lab want to test the ACIPS framework to gauge
the potential of their system as a viable diagnostic tool for the clinic.

### Scenario III

Client C is a community health program in rural Kenya that has received
funding from a non-governmental organization to deploy AI-enabled smartphone
applications that can screen for tuberculosis in their mHealth program. The
smartphones are operated by community health workers (CHWs) who have the
equivalent of a high school education and have received training from the NGO
to conduct simple medical procedures such as measuring patient vital signs
(breathing rate, blood pressure, body temperature, etc.) and screening for
infectious diseases. The CHWs are familiar with using mobile phones through
their exposure working with them on a daily basis. However, their knowledge of
AI is extremely low or non-existent. On average, the patients involved in this
community health program have the equivalent of a grade school-level education
and many of them work as day laborers in the agricultural sector or stay at
home to take care of their children. The patients in this community have
little experience with technology and have at most been exposed to basic
(feature) mobile phones. The patients have had no exposure to AI and are
unaware of it as a potential tool for disease diagnosis. The community health
program has received guidance from the NGO to integrate ACIPS into the care
routines of the CHWs and has received training from facilitators at the
organization to do so.

### Ethical Concerns

One ethical concern arises when discussing the implementation of the empirical
study. We acknowledge the disproportionate effects that could occur between
the treatment and control groups. Providing training on the ACIPS framework to
the treatment group will most likely put the hospitals in this group at an
(unfair) advantage. For the control group, we expect patients to exit the
study with a less thorough understanding of the AI system compared to their
counterparts in the treatment group. We also expect that introducing ACIPS
without the workshop will not have any detrimental effects on the healthcare
outcomes of the patients in these respective medical facilities. At the
moment, this is the primary ethical concern that we expect to face throughout
our evaluation. As we iterate through developing this framework, we will
ensure that experts with both medical and ethics backgrounds are actively
involved. During the evaluation stage, we will also ensure that if available,
compliance officers are present during these respective trainings to monitor
the progress of these experiments.

## Limitations

While the goal of ACIPS is to facilitate the transition of AI into healthcare,
we understand that not all healthcare institutions have the capacity or
ability to integrate these systems. We recognize the potential for those
institutions with larger operating budgets and higher skilled physicians to
have higher levels of access to these technologies, thus widening the already
large healthcare gap seen between countries in the Global North and South and
even within urban and rural areas within “developed” countries. However, we
hope that as AI becomes more commonly integrated into healthcare systems
around the world, no matter how and where, ACIPS is used as the guiding
framework. Additionally, while ACIPS is meant to be used for all kinds of AI
systems, we find that the framework may have to be expanded for contexts where
smaller devices such as mobile phones and IoT devices such as monitoring
bands, smart speakers, and sensors are commonly used in healthcare.

#### Bias in AI Technologies for Healthcare

Over the past few years as the field of AI ethics has developed, concerns
about bias within AI systems used for healthcare have become more prominent
(Obermeyer et al. 2019; Evans et al. 2020; Vyas, Eisenstein, and Jones 2020).
Algorithmic bias is an issue that is more likely to affect marginalized
populations and compound on existing inequities within fields such as
healthcare, education, housing, and policing (Lee 2018b; Benjamin 2019; Garcia
2016). As AI systems continue to evolve, we believe that it is imperative to
address these issues to ensure that the decisions and solutions provided by
these systems are in fact fair and transparent. At the moment, we believe
auditing systems for AI-powered technologies (Raji et al. 2020; Arslan 2000)
or frameworks for assessing ethical considerations in healthcare technologies
(Char, Abràmoff, and Feudtner 2020; Fiske et al. 2020; Overton 2020; Bærøe,
Jansen, and Kerasidou 2020) could be a better solution for directly addressing
concerns of algorithmic bias within these systems. While the ACIPS framework
does not directly address the issue of bias within AI software that is used
directly for healthcare, we believe integrating the principles and methods
from existing approaches will provide much more robustness within our
respective framework. This is an area of future interest, but not within the
scope of our work at the moment.

### Socio-cultural Values

While ACIPS provides a novel approach to the concepts of acceptability,
explainability, and privacy in AI for healthcare contexts, these concepts may
not translate well to non-Western contexts. In regions where healthcare
services are scarce due to the low number of medical professionals and
facilities, the prospect of receiving urgent care an AI intervention may trump
the need for patients to accept or be fully informed about the implications of
these technologies. Attempting to inform patients about why concepts such as
data privacy or security may be another task within itself to realizing the
full potential of ACIPS. Additionally, with both of the authors living in
Western countries (the United States and Germany, respectively) our values
around privacy have been shaped by ideals that differ from those held by
people living within the Global South. Working to understand how cultural
perceptions of privacy impact the implementation of certain principles within
ACIPS is an imperative task and will help shape future iterations of this
framework.

### Measurement Methodologies

At the moment, there are no known scales to measure the levels of acceptance,
comfortability, or knowledge one has of AI-enabled technologies and
applications, which introduces a bit of challenge to our approach. However,
this shortcoming presents an opportunity to initiate the development of such
methodologies. The metrics used to measure incorporate commonly used
psychometric scales such as the Likert scale, which may not be complex enough
to comprehensively capture the level of nuance needed to evaluate patient
perception of AI. As we continue to develop the ACIPS framework and refine the
experimental approach for our pilot evaluation, we will work to ensure that a
scale that meets our needs is developed with these concerns in mind.

### Regulatory Limitations

Few countries have regulations that guide the use of AI in healthcare
settings. An additional hurdle that may impact the integration of ACIPS is the
lack of regulatory structures in many countries outlining the use of AI in
healthcare and other contexts. With no prior guide to the use of AI, it is
unclear if hospitals and clinics would wholly welcome ACIPS. However, this
lack of regulation may benefit the ACIPS framework and lead to its widespread
adoption within healthcare facilities and in medical school curricula. We find
that ACIPS has the potential to bridge these policy gaps and could inform
policy making in this context.

### Facility Limitations

While our pilot evaluation will be limited to hospitals that have the
technological capacity to integrate AI-enabled systems within their respective
facilities, there are no explicit restrictions on the medical professionals
who will be allowed to participate. A basic assumption from the former
criteria is that if a hospital is using artificial intelligence in their
systems, then at the very least, the healthcare professionals involved in
operating these systems will have basic training on these systems. However,
the training healthcare professionals receive on these systems may not amount
to the level of technical knowledge needed to successfully deploy the ACIPS
framework. Aside from the training needed to inform the participants in our
experimental study of the ACIPS framework, we expect that additional training
on topics in AI itself will need to be conducted to ensure that medical
professionals are conveying accurate information to patients. However, a
report by the Directorate General for Parliamentary Research Services for the
European Parliament suggests that even experts might have a hard time
understanding how an algorithm reached a conclusion from a dataset,
establishing a high bar for clear and transparent messaging (for Parliamentary
Research Services. 2020).

#### Consent

When it comes to consent in agreeing to be treated with tools that use or rely
on artificial intelligence, being admitted into a hospital can be akin to
subscribing to a new online service: if you do not accept the terms and
conditions, you do not get the service. This means accepting the terms and
conditions of the AI tool(s) the hospital is planning on using during your
stay, or not getting any kind of service at all.

## Future Work

Our proposed framework provides a strong foundation to expand literature
regarding the study of public concerns with AI-based technologies and human-
centered design of these systems. To continue our work, we plan on deploying
the empirical study outlined in this paper to test our proposed framework and
evaluate its effectiveness in real-life settings. The insights of physicians,
nurses, and other medical workers will be invaluable as we progress in
expanding our research and we plan on forging a range of collaborative
partnerships in this domain. Additionally, we find that there is no set order
in which ACIPS should be applied, but as we experiment with the framework and
improve the specifications of each principle, we may provide users with
stricter guidelines dictating the application order of the principles within
this framework.

## Conclusion

The field of humans and technology interaction, especially in the domain of
artificial intelligence for healthcare, is still in its infancy. In
particular, exploring the need for technology to be used in a complementary
manner, more than as a replacement for human care in medicine could be
beneficial for the successful introduction of AI in the field. Moreover, in
developing countries, using a complementary approach in the management and
daily use of AI-driven applications or healthcare solutions has the potential
of igniting trust in patients to discuss typically taboo topics and bringing
more people to seek medical advice where before they would not dare. Ensuring
that AI does not exacerbate existing disparities within healthcare begins with
instituting standards to monitor and guide its use. Our approach is not to
understand why humans react so differently to reliance on technological aid in
different sectors, but to accept that such is the case and seek to learn about
how to diminish any concerns that may arise from these interactions. Public
concerns for the use and misuse of AI or other digital tools may be overcome
with blended human-technology approaches and with ex-ante data on the local
concerns regarding a particular instrument.

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