How is artificial intelligence (AI) becoming more advanced in healthcare, helping doctors and medical professionals work faster and at lower costs? Considering that 46% of healthcare organizations in the UK were using AI technology in 2019, what are the potential benefits and challenges of AI in this field, including both its pros and cons?
As AI continues to grow in healthcare, why is it important for us to understand its impact, current applications, and what the future might look like?
What are some key areas we should pay attention to as AI evolves in the medical profession?
Tracing the Roots of AI in Healthcare
Artificial Intelligence (AI) has a fascinating history that dates back to ancient times when the Greeks imagined lifelike, intelligent beings. However, modern AI really began in 1956 at a conference at Dartmouth College, where the term “Artificial Intelligence” was first used.
Over the years, there were ups and downs in AI research, including periods known as “AI Winters” when interest and funding waned.
Major breakthroughs came in the late 20th century, like when IBM’s Deep Blue defeated chess champion Garry Kasparov in 1997, and Watson won the quiz show “Jeopardy!” in 2011.
AI’s potential in healthcare started taking shape in the 1960s with systems like MYCIN, but it wasn’t until the 1980s and 1990s, with advancements in computers and data networks, that AI began to thrive in medicine.
Today, AI is essential in healthcare, helping with everything from processing medical records to enabling robot-assisted surgeries, and it continues to evolve rapidly.
Understanding Artificial Intelligence in Healthcare
Artificial Intelligence (AI) in healthcare is an exciting blend of technology and medicine, where computers mimic human thinking to improve patient care. AI helps analyze complex medical data, leading to better disease diagnosis, prevention, and treatment.
It creates advanced diagnostic tools, designs treatment plans, aids in drug development, and personalizes patient care by processing large amounts of information quickly.
For example, AI can analyze X-rays to assess the age of bones, making interpretations more efficient. Studies show that integrating Clinical Decision Support (CDS) can save about $1,000 per patient encounter while also resulting in shorter hospital stays and reducing the chances of complications and readmissions within 30 days.
However, as AI becomes more common in medicine, it raises important ethical questions about data privacy and job impacts. Overall, AI is transforming healthcare, helping doctors provide better care while also saving costs and improving patient outcomes.
Also read 15 Ways AI is Being Used in Healthcare
The Upsides and Downsides of AI in Healthcare
Artificial Intelligence (AI) is transforming healthcare by improving diagnostics, personalizing treatments, and streamlining processes. While it offers significant benefits, such as enhanced decision-making and efficiency, there are also concerns about data privacy, algorithmic bias, and job displacement.
Understanding the pros and cons of AI in healthcare is crucial to navigating its impact on the medical field.
Evidence-based Pros
Advancements in Diagnostic Accuracy
There is a lot of evidence showing that AI can improve healthcare, especially in diagnosing diseases. For example, a study published in JAMA Oncology in 2019 found that an AI system was better than human radiologists at detecting breast cancer in mammograms.
The AI achieved an accuracy rate of 94.0%, which was higher than the accuracy of the doctors. This shows how AI can help make diagnoses more accurate, reduce mistakes, and ultimately lead to better outcomes for patients.
Virtual nursing assistants
A study revealed that 64% of patients are comfortable using AI for continuous access to nursing support. AI virtual nurse assistants such as chatbots, applications, and other digital interfaces can provide medication information, relay reports to physicians, and assist patients in scheduling appointments.
By handling these routine tasks, AI reduces the workload of clinical staff, allowing them to focus more on direct patient care, where human judgment and interpersonal interaction are essential.
Drug prescription and administration error detection
Using artificial intelligence (AI) in healthcare helps medical professionals identify prescription, dosage, and administration errors before they can cause serious harm. In the U.S., about 9,000 people die each year due to prescription errors, often caused by mistakes in electronic health record (EHR) systems, like doctors selecting the wrong medications or confusing dosage units.
AI can improve this by analyzing historical EHR data and flagging prescriptions that deviate from typical patterns for doctors to review. For example, Brigham and Women’s Hospital implemented an AI system that detected 10,668 potential prescription errors in one year, saving the hospital $1.3 million in costs.
Additionally, AI can monitor how patients take their medications, alerting doctors when patients struggle with self-administration. For instance, a study used an AI-powered sensor to track how patients use inhalers and insulin pens, helping to identify any mistakes in their usage.
Personalized Medicine Success Stories
AI has transformed healthcare by enabling personalized medicine, which tailors treatments to individual patients based on their unique genetics, medical history, and lifestyle.
A notable example is the “100,000 Genomes Project” in the UK, where AI was used to identify genetic mutations linked to rare diseases.
By uncovering the genetic causes of these conditions, AI has facilitated the creation of targeted therapies that improve treatment effectiveness while reducing side effects and costs. This shift represents a significant advancement in healthcare, focusing on delivering more precise and efficient care to patients.
Drug Discovery
Developing new drugs is resource-intensive, often costing millions of dollars and taking years, sometimes decades. Only about 5 out of every 5,000 drugs that enter initial testing make it to human trials, and of those, only 1 in 5 is deemed safe enough for the market.
These high costs can deter companies from investing in drug discovery, especially for rare diseases. However, artificial intelligence (AI) can significantly reduce time and costs. AI quickly organizes large datasets, enabling insights from complex information.
For example, Valo Health uses its Opal Computational Platform to streamline drug discovery and eliminate animal testing, while Atomwise’s AtomNet analyzes 10 to 20 million genetic compounds each day, providing results 100 times faster than traditional methods.
This AI-driven approach allows for repurposing existing drugs for new conditions, making it a cost-effective alternative to developing new treatments from scratch.
Evidence-based Cons
Vulnerable to Cybersecurity Threats
AI systems rely heavily on data networks, which makes them vulnerable to security risks. With the rise of Offensive AI, there is a growing need for improved cybersecurity to ensure that this technology remains safe and sustainable.
According to Forrester Consulting, 88% of security industry decision-makers believe that Offensive AI poses an emerging threat. As AI continues to enhance its capabilities, cyberattacks are likely to use AI as well, learning from both their successes and failures.
This means that these attacks will become more sophisticated and harder to predict or prevent. Once these advanced threats manage to bypass security defenses, it will be much more difficult to address the damage they cause.
Ethical Concerns and Algorithmic Bias
While AI is a powerful tool, it is not free from bias and can sometimes reinforce existing prejudices. For instance, a 2016 investigation by ProPublica found that an AI tool used in the criminal justice system unfairly labeled black defendants as high-risk more often than white defendants.
This raises important ethical concerns and emphasizes the need for careful oversight and ongoing efforts to reduce bias in AI algorithms.
Job Displacement
While AI can lower costs and ease the workload for healthcare professionals, it may also eliminate some jobs, creating challenges for those who have invested in their education.
A 2018 World Economic Forum report projected that AI would create 58 million jobs by 2022 but also displace 75 million jobs during the same period, mainly affecting roles with repetitive tasks. Although AI promises improvements in healthcare, we must consider its social impacts as it becomes more integrated into the industry.
Lack of emotion and creativity
A key disadvantage of AI is its inability to incorporate emotion and creativity into decision-making. While AI can generate “novel” ideas, it lacks the ability to create truly original solutions, making it less effective in artistic fields and problem-solving.
Humans are generally better at providing innovative solutions, especially when considering the emotional consequences of decisions.
For example, a study showed that AI often assigned negative emotions to people of races other than white, leading to biased outcomes. Since compassion and kindness are inherently human traits, they cannot be programmed into AI, further limiting its capacity for sensitive decision-making.
Data availability
Training AI systems requires vast amounts of data from sources like electronic health records, pharmacy records, insurance claims, and consumer-generated information such as fitness trackers. However, health data often poses challenges due to its fragmentation across different systems.
Patients frequently see various providers and switch insurance companies, which leads to data being spread across multiple formats. This fragmentation increases the risk of errors, reduces the completeness of datasets, and raises the costs of data collection, limiting the ability of organizations to develop effective healthcare AI solutions.
Striking a Balance: The Future of AI in Healthcare
In conclusion, AI in healthcare offers significant potential, with projections suggesting it could create 58 million jobs by 2022 and reduce treatment costs by up to 50%. It can enhance diagnosis accuracy and speed up drug discovery, but it also poses challenges like data privacy concerns and the risk of displacing 75 million jobs.
As we move forward, it’s essential for healthcare leaders to emphasize responsible development and rigorous oversight. By striking a balance between using AI’s benefits and addressing its limitations, we can improve patient care while safeguarding their privacy, paving the way for a brighter future in healthcare.
