Introduction
As medical care evolves, clinicians and researchers are exploring the use of technology to improve the quality and effectiveness of medical care. In this regard, technology is being used to deliver precision medicine. This form of medicine is a new approach that focuses on using genomic, environmental and personal data to customize and deliver precise form of medical treatment. Hence the name ‘precision medicine’. One of the most influential factors, in recent years, in delivering precision medicine has been Artificial Intelligence (AI). In specific one of its forms Machine Learning (ML). ML, which uses computation to analyze and interpret various forms of medical data to identify patterns and predict outcomes has shown increasing success in various areas of healthcare delivery. In this article, I discuss how computer vision and natural language processing, which use ML can be used to deliver precision medicine. I also discuss the technical and ethical challenges associated with the approaches and what the future holds if the challenges are addressed.

Image Analysis

Various forms of medical imaging techniques like X-rays, CT, MRI and Nuclear imaging techniques are being used by clinicians to assist their diagnosis and treatment of various conditions ranging from cancers to simple fractures. The importance of these techniques in devising specific treatments has become critical in recent years. However, the dependency on a limited subset of trained medical specialists (Radiologists) to interpret and confirm the images has meant in many instances increase with the diagnosis and treatment times. The task of classifying and segmenting medical images can not only be tedious but take a lot of time. Computer Vision (CV), a form of AI that enables computers to interpret images and relate what the images are, has in the recent years shown a lot of promise and success. CV is now being applied in medicine to interpret radiological, fundoscopic and histopathological images. The most publicized success of recent years has been the interpretation of retinopathy images to diagnose diabetic and hypertensive retinopathy. The use of CV, powered by neural networks (an advanced form of ML), is said to take over the tedious task of segmenting and classifying medical images and enable preliminary or differential diagnosis. This approach is stated not only to accelerate the process of diagnosis and treatment but also provide more time for the radiologists to focus on complex imaging interpretations.

Natural Language Processing
As with CV, Natural Language Processing (NLP) has had a great impact on society in the form of voice assistants, spam filters, and chat-bots. NLP applications are also being used in healthcare in the form of virtual health assistants and in recent years have been identified to have potential in analyzing clinical notes and spoken instructions from clinicians. This ability of NLP can lessen the burden for busy clinicians who are encumbered by a need to document all their patient care in electronic health records (EHRs). By freeing up the time in writing copious notes, NLP applications can enable clinicians to focus more of their time with patients. In the recent period NLP techniques have been used to analyze even unstructured (free form and written notes) data, which makes it useful in instances where written data is not available in the digital form or there are non-textual data. By integrating NLP applications in EHRs, the workflow and delivery of healthcare can be accelerated.

Combination of Approaches
Precision medicine is premised on customization of medical care based on individual profile of patients. By combining NLP and CV techniques, the ability to deliver precision medicine s greatly increased. For example, NLP techniques can scroll through past medical notes to identify previously diagnosed conditions and medical treatment and present the information in a summary to doctors even as the patient presents to the clinic or to the emergency department. Once in the clinic or emergency department, NLP voice recognition applications can analyze conversation between the patient and clinicians and document it in the form of patient notes for the doctor to review and confirm. This process can free up time for the doctor and ensure accuracy of notes. As the doctor identifies the condition affecting the patient and relies on confirmation through relevant medical imaging, automated or semi-automated CV techniques can accelerate the confirmation process. Thus, a cohesive process that can accelerate the time in which the patient receives necessary medical treatment.
Let us see how this works in a fictitious example. Mr Carlyle, an avid cyclist, meets with an accident on his way to work when an automobile swerves into the bike lane and flings him from his bicycle. The automobile driver calls in an ambulance when he notices Mr Carlyle seated and grimacing with pain. The ambulance after arrival having entered his unique patient identifier number, which is accessed from his smartwatch, rushes him to the nearest emergency department. The AI agent embedded in the hospital’s patient information system identifies Mr Carlyle through his patient identifier number and pulls out his medical details including his drug allergies. This information is available for the clinicians in the emergency department to review even as Mr Carlyle arrives. After being placed in an emergency department bay, the treating doctor uses an NLP application to record, analyze and document the conversation between her and Mr Carlyle. This option allows the doctor to focus most of her time on Mr Carlyle. The doctor suspects a fracture of the clavicle and has Mr Carlyle undergo an X-ray. The CV application embedded in the imaging information system has detected a mid-shaft clavicular fracture and relays the diagnosis back to the doctor. The doctor, prompted by an AI clinical decision support application embedded in the patient information system, recommends immobilization and a sling treatment for Mr Carlyle along with pain killers. His pain killer excludes NSAIDs as the AI agent has identified he is allergic to aspirin.

Challenges
The above scenario while presenting a clear example of how AI, in specific CV and NLP applications, can be harnessed to deliver prompt and personalized medical care is yet contingent on the technologies to deliver such outcomes. Currently, CV techniques have not achieved the confidence of regulatory authorities nor clinicians to allow automated medical imaging diagnosis (except in minor instances such as diabetic retinopathy interpretation) and neither are NLP applications embedded in EHRs to allow automatic recording, analysis and recording of patient conversations. While some applications have been released in the market to analyze unstructured data, external validation and wide acceptance of these type of applications are some years away. Coupled with this technical and regulatory challenges is the ethical challenges of enabling autonomy of non-human agents to guide and deliver clinical care. Further issues may arise due to the use of patient identifiers to extract historical details even if it is for medical treatment if the patient hasn’t consented so. Yet, the challenges can be overcome as AI technology improves and governance structures to protect patient privacy, confidentiality and safety are established. As focus on the ethics of application of AI in healthcare increases and technological limitations of AI application get resolved, the fictitious scenario may become a reality not too far into the future.

Conclusion
There is a natural alignment between AI and precision medicine as the power of AI methods such as NLP and CV can be leveraged to analyze bio-metric data and deliver personalize medical treatment for patients. With appropriate safeguards, the use of AI in delivering precision medicine can only benefit both the patient and clinician community. One can based on the rapidly evolving AI technology predict the coming years will see wider adaption of precision care models in medicine and thus AI techniques.

In recent years, there has been a great deal of coverage about the dearth of PhD qualified AI data-scientists and the level of salaries qualified candidates can gain. One such piece can be found here: NYTimes article. Then you have universities complaining how their PhD qualified AI scientists are being poached by the industry thus demonstrating the demand for PhD qualified AI scientists: Guardian Article. Also, you have many universities opening numerous funded AI PhD positions such as this university: Leeds University Isn't it then obvious, a PhD in AI technology should be on all data scientists to do list. Well, as one who contemplated briefly to do a second PhD (focusing on swarm intelligence and multi-agent system in Healthcare) and who spent some time researching the necessity of completing a PhD to be across AI, I found it detrimental to undertake a PhD focusing on a specific AI algorithmic approach. Let me explain why?

1. The first and foremost reason why a PhD (relying on a particular AI algorithmic approach) is futile to pursue is the pace at which the mathematical and algorithmic translation of intelligence is occurring. We are quickly realizing the limitations of deep learning in replicating human intelligence. Deep learning, which was even as late as last year heralded by some as a greater invention than electricity. Also, newer AI algorithmic approaches are emerging that are better suited to address real world problems than traditional AI algorithms pushed by certain AI academics/data-scientists (who likely based their PhD's on these techniques). More importantly, it is very likely we will see authorities and industry seeking approaches that can achieve general intelligence rather than narrow intelligence that current algorithmic methods are capable of. This move will require a causal deterministic approach, which current neural network algorithms seem to be incapable of delivering. Therefore, spending three years of time (at the least) studying and applying a particular algorithmic approach that can quickly become outdated seems a surefire way of washing your time and money down the drain.
2. The second reason is the changing higher education landscape with disruptors like MOOCs and short courses delivering condensed educational content to a student cohort that is time-conscious and not willing to spend years pursuing a doctoral degree. Increasingly, the technical industry having a scarcity of PhD qualified AI data scientists has become open to accepting candidates who have completed machine learning/deep learning/AI courses through one of the many popular online education platforms. Many research scientists in big technological firms and start-up founders,without PhD's but with expertise in AI, have gone to pursue successful AI careers. With a fast changing AI technological field, a short course covering current AI technical approaches would make more practical sense.
3. The third reason is the opportunity costs in undertaking a PhD as opposed to a shorter version like a Masters or a short course. While PhD does impart on you a expert status and the ability to rely on an university framework to pursue an in-depth education in a sought after domain, it also significantly limits your earning and social possibilities during the course of your PhD studies. The industry is hungry for AI professionals but do all the open positions need PhD qualified AI data scientists or would someone who has the expertise suffice? I suspect the latter.

This article is not meant to signal an obituary for PhD's focusing AI , but a cautionary note for someone contemplating a PhD focusing on a specific AI technique. On the other hand, if one was to choose a broad PhD topic that isn't reliant on a particular AI algorithmic approach while keeping oneself abreast with latest trends (through short technical courses during the course of their PhD studies), they will be future proofing themselves. They will also be not wedded to a particular statistical/mathematical approach and perhaps even be contributing to the development of AI-General Intelligence! Another option is to pursue a Masters course in AI that are increasingly being offered by academic institutions and online education platforms. This path would not only give you the academic and technical credibility in AI but also limit the opportunity costs that one may incur with a PhD route. So if you are thinking of undertaking a PhD in AI, think carefully as to what would suit you best!

On 11th February, the US administration formalized the proposals made during last year's White House summit on AI through an executive order but there was no mention of the amount that will be set aside for investment in AI (except for statements about prioritization of investment in AI). This compared to official commitments by other countries/regions:

With the tendency nowadays for many 'software as service' providers to label their software as 'AI' or add a component labelled as AI, it is hard to distinguish what is a real AI based software and what is not? Some vendors state only machine learning (including deep learning) based software is real AI. However, AI is much more than machine learning and involves other forms such as reinforcement learning, swarm intelligence, genetic algorithms and even some forms of robots. To make it simple for clients/purchasers/organizations, I have developed this simple chart to identify an AI and Machine Learning based software. This chart does not cover all forms of AI software's as currently most AI software's being offered in the market are machine learning based software's (I am creating a more comprehensive chart, which encompasses all the schools and tools of AI including symbolism, connectionism, analogism, bayesianism and evolutionism approaches). I also wanted to keep this chart simple so it is easy to use by buyers (so you data scientists and mathematicians, feel free to comment but be forgiving). Anyway, here you are:

Recently a twitter war (OK, maybe a vigorous debate) erupted between proponents of deep learning models and long-standing critics of deep learning approaches in AI (I won't name the lead debaters here, but a simple Google search will help you identify them). After following the conversation and my own study of the maths of probabilistic deep learning approaches, I have been thinking about alternative approaches to current bayesian/back-propagation based deep learning models.

The versatility and deep impact (pun intended) of neural network models in pattern recognition, including in medicine, is well documented. Yet, the statistical approach/maths of deep learning is largely based on probability inference/Bayesian approaches. The neural networks of deep learning remember classes of relevant data and recognise to classify the same or similar data the next time around. It is kind of what we term 'generalisation' in research lingo. Deep learning approaches have received acclaim because they are able to better capture invariant properties of the data and thus achieve higher accuracies compared to their shallow cousins (read supervised machine learning algorithms). As a high-level summary, deep learning algorithms adopt a non-linear and distributed representation approach to yield parametric models that can perform sequential operations on data that is fed into it. This ability has had profound implications in pattern recognition leading to various applications such as voice assistants, driver-less cars, facial recognition technology and image interpretation not to mention the development of the incredible AlphaGo, which has now evolved into AlphaGo zero. The potential of application of deep learning in medicine, where big data abounds, is vast. Some recent medical applications include EHR data mining and analysis, medical imaging interpretation (the most popular one being diabetic retinopathy diagnosis), medical voice assistants, and non-knowledge based clinical decision support systems. Better yet, the realisation of this potential is just beginning as newer deep learning algorithms are developed in the various Big-Tech and academic labs. Then what is the issue?

Aside from the often cited 'interpretability/black-box' issue associated with neural networks (I have previously written even this may not be a big issue with solutions like attention mechanism, knowledge injection and knowledge distillation now being available) and it's limitations in dealing with hierarchal structures and global generalisation, there is the 'elephant in the room' i.e. no inherent representation of causality. In medical diagnosis, probability is important, but causality is more so. In fact the whole science of medical treatment is based on causality. You don't want to use doxycycline instead of doxorubicin to treat Hodgkin's Lymphoma or the vice versa for Lyme Disease. How do you know if the underlying is Hodgkin's Lymphoma or Lyme Disease? I know Different diseases and different presentations, of course. However, the point is the diagnosis is based on a combination of objective clinical examination, physical findings, sero-pathological tests, medical imaging. All premised on a causation sequence i.e. Borrelia leads to Lyme Disease and EB virus/family history leads to Hodgkin's. This is why we adopt Randomised Control Tests, even with its inherent faults, as the gold standard for incorporation of evidence-based treatment approaches. If understanding of causal mechanisms is the basis of clinical medicine and practice, then there is so much deep learning approaches can do for medicine. This is why I believe there needs to be a serious conversation amongst AI academics and the developer community about adoption of 'causal discovery algorithms' or better yet 'hybrid approaches (a combination of probabilistic and causal discovery approaches)'.
We know 'correlation is not causation', yet under appropriate conditions we can make inferences from correlation to causation. We now have algorithms that search for causal structure information from data. They are represented by causal graphical models as illustrated below:







Figure I. Acyclic Graph Models (Source: Malinsky and Danks, 2017)
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While the causal search models do not provide you a comprehensive causal list, they provide enough information for you to infer a diagnosis and treatment approach (in medicine). There have been numerous successes documented with this approach and different causal search algorithms developed over the past many decades. Professor Judea Pearl, one of the godfathers of causal inference approaches and an early proponent of AI has outlined structural causal models, a coherent mathematical basis for the analysis of causes and counterfactuals. You can read an introduction version here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2836213/

I think it is probably a time now to pause and think, especially in the context of medicine, if a causal approach is better in some contexts than a deep learning gradient informed approach. Even better if we can think of a syncretic approach. Believe me, I am an avid proponent of the application of deep learning in medicine/healthcare. There are some contexts, where back-propagation is better than hypothetico-deductive inference especially when terrabytes/petabytes of unstructured data accumulates in the healthcare system. Also, there are limitations to causal approaches. Sometimes probabilistic approaches are not that bad as outlined by critics. You can read a defence of probabilistic approaches and how to overcome its limitations here: http://www.unofficialgoogledatascience.com/2017/01/causality-in-machine-learning.html

However, close-mindedness to an alternative approach to probabilistic deep learning models and reliance on this approach is a sure-bet to failure of realisation of the full potential of AI in Medicine and even worse failure of the full adoption of AI in Medicine. To address this some critics of prevalent deep learning approaches are advocating for a Hybrid approach, whereby a combination of deep learning and causal approaches are utilised. I strongly believe there is merit in this recommendation, especially in the context of application of AI in medicine/healthcare.

A development in this direction is the 'Causal Generative Neural Network'. This framework is trained using back-propagation but unlike previous approaches, capitalises on both conditional independences and distributional asymmetries to identify bivariate and multivariate causal structures. The model not only estimates a causal structure but also differential generative model of the data. I will be most certainly following the developments with this framework and more generally hybrid approaches especially in the context of it's application in medicine but I think all those interested in the application of AI in Medicine should be having a conversation about this matter.
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Reference:
Malinsky, D. & Danks, D. (2017). Causal discovery algorithms: A practical guide. Philosophy Compass 13: e12470, John Wiley & Sons ltd.

The most common AI systems that are currently emerging are machine learning, natural language processing, expert systems, computer vision and robots. Machine Learning is when computer programs are designed to learn from data and improve with experience. Unlike conventional programming, machine learning algorithms are not explicitly coded and can interpret situations, answer questions and predict outcomes of actions based on previous cases. These processes typically run independently in the background and incorporate existing data, but also “learn” from the processing that they are doing.  There are numerous machine learning algorithms, but they can be divided broadly into two categories: supervised and unsupervised.  In supervised learning, algorithms are trained with labelled data, i.e. for every example in the training data there is an input object and output object and the learning algorithm discovers the predictive rule. In unsupervised learning, the algorithm is required to find patterns in the training data without necessarily being provided with such labels. A leading form of machine learning termed Deep Learning is considered particularly promising.

Natural Language Processing (NLP) is a set of technologies for human-like-processing of any natural language, oral or written, and includes both the interpretation and production of text, speech, dialogue etc. NLP techniques include symbolic, statistical and connectionist approaches and have been applied to machine translation, speech recognition, cross-language information retrieval, human-computer interactions and so forth. Some of these technologies, and certainly the effects, we see in normal societal IT products such as through email scanning for advertisements or auto-entry text for searches. 

”Expert systems” is another established field of AI, in which  the aim is to design systems that carry out significant tasks at the level of a human expert. Expert systems do not yet demonstrate general-purpose intelligence, but they have demonstrated equal and sometimes better reasoning and decision making in narrow domains compared to humans, while conducting these tasks similar to how a human would do so. To achieve this function, an expert system can be provided with a computer representation of knowledge about a particular topic and apply this to give advice to human users. This concept was pioneered in medicine the 1980s by MYCIN, a system used to diagnose infections and INTERNIST an early diagnosis package. Recent knowledge based expert systems combine more versatile and more rigorous engineering methods.  These applications typically take a long time to develop and tend to have a narrow domain of expertise, although they are rapidly expanding.  Outside of healthcare, these types of systems are often used for many other functions, such as trading stocks.
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Another area is Computer Vision systems, which capture images (still or moving) from a camera and transforms or extract meanings from them to support understanding and interpretation. . Replicating the power of human vision in a computer program is no easy task, but it attempts to do so by relying on a combination of mathematical methods, massive computing power to process real-world images and physical sensors. While great advances have been made with Computer Vision in such applications as face recognition, scene analysis, medical imaging and industrial inspection the ability to replicate the versatility of human visual processing remains elusive.

The final technique we cover here are Robots, which have been defined as “physical agents that perform tasks by manipulating the physical world” for which they need a combination of sensors (to perceive the environment) and effectors (to achieve physical effects in the environment). Many organisations have had increasing success in limited Robots which can be fixed or mobile. Mobile “autonomous” robots that use machine learning to extract sensor and motion models from data and which can make decisions on their own without relying on an operator are most relevant to this commentary, of which “self-driving” cars are well known examples.
 

To borrow Professor Dan Ariely's quote about Big Data, AI is like like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.

However, this may be a too harsh commentary about the state of AI affairs. Not withstanding the overstatements about the capabilities AI by some; there is substance behind the hype with the demonstrated benefits of deep learning, natural language processing and robotics in various aspects of our lives. In Medical Informatics, the area I can credibly comment, neural networks and data mining have been employed to enhance the ability of human clinicians to diagnose and predict medical conditions, and in some instances like medical imaging and histo-pathological diagnosis, AI applications have met or exceeded the accuracy of human clinicians. In terms of economics, AI deserves the attention it is getting. The top 100 AI companies have raised more than US$11.7 billion in revenue and even back in 2015, about 49 billion US$ revenue was generated just in the North American Market. Countries like China have invested massively in AI research and start-ups, with some forecasting the Chinese AI industry exceeding US$150 billion by 2030. In other instances like the political sphere, AI has received high profile recognition with the appointment of the first AI Minister in the world in U.A.E.

However, one has to be aware of the limitations with AI and the amount of research and analysis that is yet to be undertaken for us to confidently accept a ubiquitous AI system our lives. I state this as a vocal proponent of application of AI techniques especially in Medicine (as my earlier articles and book chapter indicate) but also as one who is aware of the 'AI Winter' and 'IBM Watson/MD Anderson' episodes that have occurred in the past. Plus there is the incident that happened yesterday where a driverless Uber car was responsible for a fatality in Arizona. So in this article I list, from a healthcare perspective, three main limitations in terms of adoptability of AI technologies. This analysis is based on current circumstances, which considering the rapid developments that are occurring in AI research may not apply going into the future.

1) Machine Learning Limitations: The three main limitations I see with machine learning are Data-feed, Model Complexity and Computing Times.

In machine learning the iterative aspect of learning is important. Good machine learning models rely on data preparation and ongoing availability of good data. If you don't have good data to train the machine learning model (in supervised learning) and there is no new data, the pattern recognition ability of the model is moot. For example in the case of radiology, if the images being fed into the deep learning algorithms tend to come with underlying biases (like images from a particular ethnic group or images from a particular region) the diagnostic abilities and accuracy rates of the model would be limited. Also, reliance on historical data to train algorithms may not be particularly useful for forecasting novel instances of drug side-effects or treatment resistance. Further, cleaning up and capturing data that are necessary for these models to function will provide a logistical challenge. Think of the efforts required to digitize handwritten patient records.

With regards to model complexity, it will be pertinent to describe deep learning (a form of machine learning) here. Deep Learning in essence is a mathematical model where software programs learn to classify patterns using neural networks. For this learning, one of the methods used is backpropagation or backprop that adjusts mathematical weights between nodes-so that an input leads to right outputs. By scaling up the layers and adding more data, deep learning algorithms are to solve complex problems. The idea is to match the cognition processes a human brain employs. However, in reality, pattern recognition alone can't resolve all problems, especially so all medical problems. For example, when a decision has to be made in consultation with the family to take off mechanical ventilation for a comatose patient with inoperable intra-cerebral hemorrhage. The decision making in this instance is beyond the capability of a deep learning based program.

The third limitation with machine learning is the current capabilities of computational resources. With the current resources like GPU cycles and RAM configurations, there are limitations as to how much you can bring down the training errors to reasonable upper bounds. This means the limitation impacts on the accuracy of model predictions. This has been particularly pertinent with medical prediction and diagnostic applications, where matching the accuracy of human clinicians in some medical fields has been challenging. However, with the emergence of quantum computing and predicted developments in this area, some of these limitations will be overcome.

2) Ethico-legal Challenges: The ethico-legal challenges can be summarized as 'Explainability', 'Responsibility' and 'Empathy'.

A particular anxiety about artificial intelligence is that decisions made by complex opaque algorithms cannot be explained even by the designers (the black box issue). This becomes critical in the medical field, where decisions either made directly or indirectly through artificial intelligence applications can impact on patient lives. If an intelligent agent responsible for monitoring a critical patient incorrectly interprets a medical event and administers a wrong drug dosage, it will be important for stakeholders to understand what led to the decision. However, if the underlying complexity of the neural network means the decision making path cannot be understood; it does present a serious problem. The challenge in explaining opaque algorithms is termed as the interpretability problem. Therefore, it is important for explainable AI or transparent AI applications to be employed for medical purposes. The medical algorithm should be fully auditable when and where (real-time and after the fact) required. To ensure acceptability of AI applications in the healthcare system, researchers /developers need to work on accountable and transparent mathematical structures when devising AI applications.

When a robotic radical prostatectomy goes wrong or when a small cell pulmonary tumor is missed in an automated radiology services, who becomes responsible for the error? The developer? The hospital? The regulatory authority(which approved the use of the device or program)? As AI applications get incorporated in medical decision making and interventions, regulatory and legal bodies need to work with AI providers to set up appropriate regulatory and legal frameworks to guide deployment and accountability. Also, a thorough process of evaluation of new AI medical applications ,before they can be used in practice, will be required to be established especially if autonomous operation is the goal. Further, authorities should work with clinical bodies to establish clinical guidelines/protocols to govern application of AI programs in medical interventions.

One of the important facets of medical care is the patient-clinician interaction/interface. Even with current advances in Robotics and intelligent agent programming, the human empathetic capabilities far exceed that those of AI applications. AI is dependent on a statistically sound logic process that intends to minimize or eliminate errors. This can be termed by some as cold or cut-and-dry unlike the variable emotions and risk taking approach humans employ. In medical care, clinicians need to adopt a certain level of connection and trust with the patients they are treating. It is hard to foresee in the near future, AI driven applications/robots replacing humans in this aspect. However, researchers are already working on classifying and coding emotions (see: https://www2.ucsc.edu/dreams/Coding/emotions.html) and robots are being developed with eerily realistic facial expressions (http://www.hansonrobotics.com/robot/sophia/), so maybe the cynicism about AI's usefulness in this area is not all that deserved?

3) Acceptability and Adoptability: While I think the current technological limitations with machine learning and robotics can be addressed in the near future, it will be a harder challenge for AI providers to convince the general public to accept autonomous AI applications, especially those that make decisions impacting on their lives. AI to an extent is pervasive already with availability of voice-driven personal assistants, chatbots, driverless cars, learning home devices, predictive streaming..etc. There hasn't been a problem for us in accepting these applications in our lives. However, when you have AI agents replacing critical positions that were previously held by humans ; it can be confronting especially so in medical care. There is thus a challenge for AI developers and companies to ease the anxiousness of public in accepting autonomous AI systems. Here, I think, pushing explainable or transparent applications can make it easier for the public to accept AI agents.

The other challenge, from a medical perspective, is adoption of AI applications by clinicians and healthcare organizations. I don't think the concern for clinicians is that AI agents will replace them but the issues are rather the limited understanding clinicians have about AI techniques (what goes behind the development), apprehensiveness about the accuracy of these applications especially in a litigious environment and skepticism as to whether the technologies can alleviate clinician's stretched schedules. For healthcare organizations, the concerns are whether there are cost efficiencies and cost-benefits with investment into AI technologies, whether their workforce will adopt the technology and how clients of their services will perceive their adoption of AI technologies. To overcome these challenges, AI developers need to co-design algorithms with clinicians and proactively undertake clinical trials to test the efficacy of their applications. AI companies and healthcare organizations also need to have a education and marketing strategy to inform public/patients about the benefits in adopting AI technologies.

Don't rule out AI

I outline the above concerns largely to respond to the misconceptions and overhyping of AI by media and those who are not completely conversant with the mechanics behind AI applications. Overhyping AI affects the acceptability of AI especially if it leads to adoption of immature or untested AI technologies. However, there is much to lose healthcare organizations rule out adoption of AI technologies. AI technologies can be of immense help in healthcare delivery:

• where complex data (clinical and financial) accumulates beyond the analytical capabilities of humans,
• where recurrent costs are blowing up healthcare budgets,
• where the computational ability of AI programs can uncover clinical patterns that humans may miss,
• where AI prowess can mitigate medical errors that are a product of human constraints,
• where current advances in computational resources and algorithms can identify new/novel drug treatments,
• where AI population health/surveillance programs can prevent the emergence of epidemics,
• when barriers for access to specialized medicine exist because of lengthy periods involved in training human clinicians and
• when intelligent communication and medical support devices can enhance the quality of senior citizens.

Imagine all this lost because of skepticism and resistance to adoption of AI technologies. Therefore, it is important that AI developers work with end-users in the development phases of AI applications and AI companies work with the media and relevant others in containing the hype and relaying realistic information about the capabilities of AI technologies. On the other hand, healthcare organizations and clinicians need to be open to what benefits AI adoption can bring to their services or they may be at the margins of a society where AI-assisted living has become the norm.