The Pros and Cons of AI in Healthcare: the Latest Thinking
Artificial intelligence (AI) in healthcare refers to the use of advanced computer systems to replicate human thinking and decision-making – with the potential to act as a powerful extension of the physician’s intellect.
The use of AI and machine learning can analyze an infinite amount of digital patient data from multiple data sources in a fraction of a second to discover complex associations and recognize patterns. This generates actionable insights that can significantly improve both the quality of patient care and experience, resulting in improved health outcomes and greater cost effectiveness.
AI-powered tools are transforming the healthcare sector and have already entered mainstream medical practice, to become a reality in many medical fields and specialties. Yet, as with any innovation, there are clear benefits, but also uncertainties and risks; it must integrate with current medical practices, address the challenges of data confidentiality, diagnostic reliability and ethical considerations, to win the trust of physicians and patients alike to increase the rate of AI adoption.
What are the pros of AI in medicine today?
1. Early disease detection and diagnosis – aid in precision diagnosis
AI uses deep learning algorithms to analyze vast amounts of medical records and images, such as X-rays, MRIs and CT scans, faster and more accurately than humans. These tools help healthcare professionals to detect diseases and conditions earlier, in particular to spot unusual or atypical features in diagnostic images, thereby reducing the risk of error while increasing the likelihood of successful treatment and recovery. Here are just a few examples of how AI is being integrated in medicine.
In pathology, AI assists in the diagnosis of diseases by analyzing digital pathology slides. It identifies cancer cells by browsing through several thousand images from a large number of patients, thereby improving the precision, accuracy and consistency of the diagnosis.
In cardiology, AI enhances early heart disease detection and management. AI algorithms identify arrhythmias, heart failure or coronary heart disease, and can predict events such as the risk of sudden cardiac death from computer readings of electrocardiograms and cardiac MRI images. It can also detect strokes from CT scans.
In ophthalmology, AI analyzes retinal images and detects indicators of diabetic retinopathy, age-related macular degeneration or glaucoma early and with high accuracy and it allows for timely intervention and personalized treatment plans.
2. Accelerated research and drug development
The development of drugs is a notoriously costly endeavor. Many of the analytical, clinical and regulatory processes involved in drug development can be made more efficient with machine learning. AI can rapidly process and analyze massive amounts of data, improving accuracy and shortening research time. It can also run simulations to predict how well new drugs will work and how they may interact with other medications. This means new treatments and medicines can be developed faster and more efficiently.
3. Highly personalized treatment plans
Patients differ in their response to drugs and treatment regimens. AI models can analyze detailed patient histories, genetic data, lifestyle and other relevant datasets to assess risk factors and identify characteristics that predict the likelihood of a patient's response to a given treatment. This personalization of care can result in more effective treatments with fewer side effects, improving the overall patient experience at lower costs.
4. Provides "real-time" health monitoring
Wearable AI-powered devices, such as continuous glucose monitors, smartwatches and implantable sensors, capture real-time data on a patient's vital signs and health parameters. AI algorithms then analyze the data collected to spot patterns of deteriorating health conditions and any deviations from the patient's baseline values. Mobile alerts can notify doctors and nurses to urgent changes in a patient's condition and to emergency situations.
With AI-driven chatbots and virtual assistants, patients can have their symptoms and concerns assessed and receive tailored advice without the need for in-person visits to healthcare facilities. This not only improves access to care, but also eases the burden on hospital and clinic capacity, particularly for minor health problems. Patients can receive quality care in the comfort of their homes, which is particularly beneficial in rural areas or healthcare deserts.
5. Streamlines administrative tasks and improves resource management
Healthcare institutions often grapple with complex administrative tasks that tie up staff and resources to the detriment of patient care. AI can take over routine tasks such as billing, appointment scheduling and responding to patient inquiries. It also assists in managing insurance reviews, tracking patient histories and providing care recommendations by analyzing large databases.
It is estimated that around $200 billion is wasted every year in the healthcare sector. Much of this excessive spending is attributable to administrative burdens and determining patients’ needs. New natural language processing (NLP) and deep learning (DL) algorithms can assist doctors in reviewing medical records to precisely assess patients' needs.
The use of AI will enable medium- and large-sized medical facilities to increase their productivity and therefore achieve considerable cost savings.
What are the cons of AI in medicine today?
1. High implementation costs and integration challenges
Setting up AI in large healthcare practices can be very costly. You need to consider the price of developing and installing the technology, plus the cost of upgrading infrastructure and training staff. These high initial expenses can be a big challenge for healthcare organizations, especially those with tight budgets.
Integrating AI into medical services can be difficult because it doesn’t always work well with existing systems. Different hospitals and clinics use various technologies, making it hard for AI to connect and share data smoothly. These problems can slow down the adoption and effectiveness of AI.
2. Ethical issues
Data ethics form the foundation of AI; key areas include patient informed consent and autonomy, data privacy and security concerns, as well as objectivity, reliability and transparency of data.
Patient informed consent and autonomy
AI raises ethical concerns, as it may interfere with patients' personal wishes or values. For example, AI-driven advice could prioritize certain outcomes, such as survival, based on general standards of care, over patients' expressed preferences for improved quality of life rather than longer life expectancy. This risks undermining patients' autonomy in decision-making.
Data privacy and security concerns
AI systems process vast amounts of sensitive patient data, which raises significant privacy and security risks. The misuse, unauthorized access or exposure of this data can have serious personal, ethical and legal consequences.
As AI systems are connected to data networks, the risk of cyber-attack is real. If these systems are compromised, patient data could be exposed publicly. To prevent these problems, medical establishments must invest in robust cybersecurity measures.
Potential for bias and discrimination
Medical AI relies heavily on the reference data available from millions of catalogued cases. Regardless of the system used, data on specific diseases, demographic characteristics or environmental factors are always missing, potentially compromising subsequent analyses and leading to misdiagnoses. If the diversity of training data is insufficient, an AI system could make treatment recommendations that are inappropriate for real-world use, and sometimes even unfair. Unfairness caused by biases in data sources is one of the most common ethical problems. For example, a study in the US found that some clinicians had overlooked positive outcomes in African Americans on the assumption that the model's positive predictive value for African Americans was low.
There is a significant risk for algorithmic biases to exacerbate and perpetuate existing disparities in healthcare delivery, resulting in sub-optimal care for marginalized populations.
AI systems are constantly evolving and improving to address data gaps. However, it should be noted that certain populations may still be excluded from existing domain knowledge.
Transparency and accountability
The deployment of AI in healthcare raises complex ethical issues, with unclear liabilities and accountabilities. When an error occurs with an AI system, it’s not always clear whether the problem originates from the technology, the data it uses, or the people managing it. To address this situation, medical practices need to adopt well-defined rules and protocols to ensure that errors are dealt with promptly and define individual accountabilities.
AI systems, particularly those using deep learning, often operate as “black boxes”, making it difficult for users to understand the inner workings of algorithms, and therefore the logic behind their recommendations or who is responsible in the event of an error. As these algorithms were designed to learn and improve their performance over time, sometimes even their designers may not know precisely how they arrive at a recommendation or diagnosis, raising significant concerns about transparency and trust in clinical decision-making.
While explainable AI (XAI) methods have been developed to offer insights into how these systems generate their recommendations, these explanations often fail to capture the reasoning process entirely. This is akin to using a pharmaceutical medicine without clearly understanding its mechanism of action.
An inability to ‘unpack the black box’ and to clarify how a specific dataset leads to a diagnosis/prognosis might have an impact on the probability that the FDA will approve a dossier that relies on an AI-based trial. One of their key concerns is to ensure that AI and machine learning applications work as advertised in multiple-use settings. The US FDA has approved various assistive algorithms, but no universal approval guidelines currently exist.
3. The need for human oversight
While AI systems can be highly accurate, they are not infallible. This is why the use of AI must be supervised by qualified healthcare professionals. Physicians are ultimately accountable for their patients' medical care, whether partially or fully assisted by AI systems.
Final thoughts
I'm really optimistic about the future of AI, despite the many challenges to overcome.
AI and machine learning applications have the potential to significantly improve the quality of patient care and clinical outcomes, while making care more accessible and affordable; but they also carry obvious uncertainties and risks, which are crucial to address, such as data privacy, diagnostic accuracy and ethical considerations.
As we progress along this transformative journey, it will be essential to strike a careful and intentional balance between AI and the human intervention to responsibly harness the full potential of this revolutionary technology in healthcare.
