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What industries can benefit from Intelligent Automation?

Intelligent automation combines artificial intelligence with machine learning and robotics



How ego and fear fuelled the rise of artificial intelligence

Did you know that by the year 2030, it’s estimated that over 800 million jobs could be taken over by robots? Sounds like something straight out of a science fiction movie, right? Well, brace yourself, because intelligent automation is no longer just a distant possibility – it’s becoming a reality, and it’s changing the game for many industries.

What is Intelligent Automation?

Intelligent Automation is a cutting edge approach that combines advanced technologies such as artificial intelligence, machine learning, and robotics to automate and optimize processes. It takes traditional automation to the next level by integrating cognitive capabilities, enabling the execution of complex tasks with minimal human intervention.

The Power of Artificial Intelligence

Artificial intelligence (AI) plays a vital role in intelligent automation. By leveraging AI algorithms, systems can learn from data, recognize patterns, and make intelligent decisions. This enables them to perform tasks that traditionally required human expertise. Some examples of AI-powered intelligent automation include:

  • Customer Service Chatbots: AI-powered chatbots can interact with customers, understand their queries, and provide appropriate responses. These chatbots can handle a wide range of inquiries, improving customer satisfaction and freeing up human agents for more complex issues.
  • Predictive Maintenance: AI algorithms can analyze large amounts of data collected from sensors to identify patterns and predict when equipment is likely to fail. This allows businesses to proactively schedule maintenance, reducing downtime and optimizing resources.

Machine Learning for Continuous Improvement

Machine learning is another crucial component of intelligent automation. It enables systems to learn from data and improve their performance over time. Here are a few examples of machine learning in action:

  • Recommendation Engines: Machine learning algorithms analyze user behavior and preferences to suggest personalized recommendations. Platforms like Netflix and Amazon use this technology to suggest movies, shows, or products that users are likely to enjoy.
  • Fraud Detection: Machine learning models can analyze historical data to detect patterns indicative of fraudulent activity. This helps financial institutions identify and prevent fraudulent transactions, protecting both themselves and their customers.

Robotics for Enhanced Efficiency

Robotics, often referred to as Robotic Process Automation (RPA), is a crucial component of intelligent automation. It involves the use of software robots or “bots” to automate repetitive, rule-based tasks. Here are some examples of RPA:

  • Invoice Processing: Bots can extract relevant information from invoices, validate it against predefined rules, and update the company’s financial systems. This streamlines the process, reduces errors, and frees up employees for more value-added tasks.
  • Data Entry: Robotic process automation can automate data entry tasks, such as transferring data from one system to another. This eliminates manual errors and speeds up the overall process.

Benefits of Intelligent Automation

Intelligent automation offers numerous benefits to organizations across various industries. Here are some key advantages:

  • Increased Efficiency: By automating repetitive tasks, organizations can significantly improve productivity and efficiency. This allows employees to focus on more strategic and creative tasks, driving innovation.
  • Enhanced Accuracy: Intelligent automation minimizes the risk of human error, leading to greater accuracy and quality in processes like data entry and analysis.
  • Cost Savings: By automating processes, organizations can reduce labor costs and optimize resource allocation. This can result in significant cost savings in the long run.
  • Improved Customer Experience: Intelligent automation can streamline customer interactions, providing faster response times and personalized experiences. This leads to higher customer satisfaction and loyalty.

In conclusion, intelligent automation is revolutionizing the way businesses operate by combining advanced technologies like artificial intelligence, machine learning, and robotics. By leveraging these capabilities, organizations can achieve increased efficiency, accuracy, cost savings, and an improved customer experience. Embracing intelligent automation can give businesses a competitive edge in today’s fast-paced digital landscape.

Industries that can benefit from intelligent automation

Intelligent automation is revolutionizing industries across the board, offering a wide range of benefits and opportunities for growth. In this blog post, we will explore three key industries that can benefit greatly from intelligent automation: manufacturing, healthcare, and financial services.

Manufacturing: Streamlining Production and Boosting Efficiency

Intelligent automation has the potential to transform the manufacturing industry by streamlining production processes and improving overall efficiency. Here are some ways in which manufacturers can leverage intelligent automation:

  • Automated Assembly Lines: Robots equipped with artificial intelligence (AI) and machine learning algorithms can perform repetitive tasks with precision and speed, leading to increased productivity and reduced human error.
  • Inventory Management: Intelligent automation can optimize inventory management by monitoring stock levels, automating reordering processes, and even predicting demand patterns, saving time and reducing costs.
  • Quality Control: With the help of intelligent automation, manufacturers can improve quality control processes by using smart sensors and computer vision systems to detect defects and ensure consistent product quality.

Real-life examples of intelligent automation in manufacturing include:

  • ABB’s YuMi: This dual-arm collaborative robot is widely used in the automotive industry for tasks such as assembling small parts and performing intricate operations that require high precision.
  • Siemens’ Simatic IT: This intelligent automation software solution enables manufacturers to optimize production processes, improve product quality, and reduce time to market.

Healthcare: Enhancing Patient Care and Operational Efficiency

Intelligent automation has the potential to revolutionize healthcare by enhancing patient care, optimizing operations, and reducing errors. Here are some ways in which the healthcare industry can benefit from intelligent automation:

  • Patient Monitoring: Intelligent automation systems can continuously monitor patients’ vital signs, alerting healthcare professionals to any abnormalities and enabling timely intervention.
  • Medical Imaging Analysis: AI-powered algorithms can analyze medical images such as X-rays, MRIs, and CT scans, helping radiologists detect abnormalities and make more accurate diagnoses.
  • Administrative Tasks: Intelligent automation can streamline administrative tasks, such as appointment scheduling, billing, and medical record management, allowing healthcare professionals to focus more on patient care.

Real-life examples of intelligent automation in healthcare include:

  • IBM Watson Health: This AI-powered platform helps healthcare professionals analyze large amounts of medical data to make more informed treatment decisions and improve patient outcomes.
  • Intuitive Surgical’s da Vinci Surgical System: This robotic surgical system enables surgeons to perform minimally invasive procedures with enhanced precision and control.

Financial Services: Automating Tasks and Enhancing Customer Experience

Intelligent automation offers immense potential for the financial services industry, enabling organizations to automate repetitive tasks, improve customer experience, and detect fraudulent activities. Here are some ways in which intelligent automation can benefit the financial services sector:

  • Customer Service: Intelligent chatbots and virtual assistants can provide personalized support, answer customer queries, and guide them through various financial processes, improving customer satisfaction and saving time.
  • Risk Management: Advanced analytics and AI algorithms can help financial institutions identify potential risks, detect fraudulent activities, and prevent financial losses.
  • Back-Office Operations: Intelligent automation can automate back-office tasks such as data entry, document processing, and compliance checks, reducing manual errors and increasing operational efficiency.

Real-life examples of intelligent automation in financial services include:

  • JP Morgan Chase’s COIN: This intelligent automation platform uses machine learning to automate contract review processes, significantly reducing the time and effort required for manual reviews.
  • Ant Financial’s Alipay: This mobile payment platform uses intelligent automation to provide seamless and secure payment services, leveraging facial recognition and fraud detection algorithms.

In conclusion, intelligent automation has the potential to transform various industries, including manufacturing, healthcare, and financial services. By streamlining processes, enhancing patient care, improving customer experience, and detecting fraudulent activities, intelligent automation offers numerous benefits and opportunities for growth. Embracing intelligent automation can help organizations stay competitive in today’s rapidly evolving business landscape.

The impact on retail and logistics

Intelligent automation is transforming the retail and logistics sectors, revolutionizing the way businesses operate and improving overall efficiency. By leveraging automation technology, retailers can streamline inventory management, optimize supply chain operations, and enhance customer experiences. In this blog post, we will explore the various ways in which intelligent automation is making a significant impact on the retail and logistics industries.

Enhanced Inventory Management

Efficient inventory management is crucial for retailers to meet customer demands and minimize costs. Intelligent automation tools, such as advanced inventory management systems, utilize data analytics and artificial intelligence to optimize inventory levels. Here’s how automation can enhance inventory management:

  • Demand Forecasting: By analyzing historical data and market trends, intelligent automation systems can accurately predict customer demand. This enables retailers to optimize their inventory levels, avoiding stockouts or excess inventory.
  • Automated Reordering: With automation, retailers can automate the reordering process by setting up predefined thresholds. When stock levels fall below a certain point, the system can automatically trigger the purchase order, ensuring a continuous supply of products.
  • Real-time Tracking: Intelligent automation enables real-time tracking of inventory levels, allowing retailers to monitor stock movements accurately. This helps prevent theft, identify discrepancies, and optimize the replenishment process.

Optimized Supply Chain Operations

Efficient supply chain management is essential for retailers to ensure timely deliveries, minimize costs, and meet customer expectations. Intelligent automation can significantly optimize supply chain operations, resulting in improved efficiency and reduced errors. Let’s explore some key benefits:

  • Automated Order Fulfillment: Automation technology enables retailers to automate the order fulfillment process, from picking and packing to shipping. This reduces human error, improves order accuracy, and enhances overall speed.
  • Route Optimization: By utilizing intelligent automation systems, retailers can optimize delivery routes, considering various factors such as traffic conditions, fuel efficiency, and delivery time windows. This results in faster and more cost-effective deliveries.
  • Warehouse Automation: Intelligent automation tools, like robotic systems, can automate repetitive tasks in warehouses, such as sorting, picking, and packing. This increases operational efficiency, reduces labor costs, and minimizes errors.

Personalized Customer Experiences

Intelligent automation also plays a crucial role in delivering personalized customer experiences, which are essential for customer satisfaction and loyalty. By leveraging automation technology, retailers can tailor their offerings to individual customers, providing a seamless and personalized shopping experience. Here’s how automation enables personalized customer experiences:

  • Recommendation Engines: Intelligent automation systems analyze customer behavior and purchase history to provide personalized product recommendations. This enhances the customer’s shopping experience and increases the likelihood of making additional purchases.
  • Chatbots and Virtual Assistants: Automation technology, such as chatbots and virtual assistants, can provide real-time assistance to customers, answering their queries, and guiding them through the purchasing process. This improves customer satisfaction and reduces the need for human intervention.
  • Targeted Marketing Campaigns: Intelligent automation allows retailers to segment their customer base and create targeted marketing campaigns. By delivering personalized promotions and offers, retailers can attract and retain customers more effectively.

Factors to consider before implementing intelligent automation

Intelligent automation has the potential to revolutionize the way organizations operate, streamline processes, and increase efficiency. However, before diving into implementation, there are several important factors that need to be carefully considered. Taking the time to evaluate these factors will help ensure a successful and smooth transition to intelligent automation. Let’s explore these factors in detail:

1. Complexity and Compatibility of Existing Systems

One of the crucial factors to consider before implementing intelligent automation is the complexity and compatibility of your existing systems. It is important to assess whether your current infrastructure can seamlessly integrate with the new automated processes. Compatibility issues can lead to delays, increased costs, and operational disruptions.

  • Example: A retail company might have a legacy inventory management system that relies on manual data entry. Before implementing intelligent automation, they need to ensure that the new automation technology can easily integrate with their existing system, allowing for a smooth flow of information and data.

2. Employee Reskilling and Training

Intelligent automation is designed to augment human capabilities, not replace them. Therefore, it is crucial to consider the impact on employees and their need for reskilling and training. By providing adequate training and upskilling opportunities, organizations can empower their workforce to embrace automation and thrive in their new roles.

  • Example: A manufacturing company plans to implement robotic process automation (RPA) to automate repetitive tasks on the assembly line. Before deployment, they invest in training programs to ensure that employees are equipped with the necessary skills to work alongside the robots and perform higher-value tasks that require human judgment.

3. Potential Impact on Job Roles

Intelligent automation brings about changes in job roles and responsibilities. It is important to evaluate how automation will affect the existing workforce and ensure a smooth transition. Clear communication and transparency about the impact on job roles can help alleviate concerns and foster a positive work environment.

  • Example: A customer support center decides to implement a chatbot to handle basic customer inquiries. Instead of eliminating jobs, the chatbot frees up customer service agents to focus on complex issues and provide personalized support, enhancing the overall customer experience.

4. Return on Investment (ROI)

Before implementing intelligent automation, organizations need to assess the potential return on investment. This includes evaluating the cost of implementation, the expected improvements in efficiency, productivity gains, and the overall impact on the bottom line.

  • Example: An insurance company decides to implement natural language processing (NLP) technology to automate claims processing. By reducing manual effort and improving accuracy, they anticipate cost savings in terms of reduced processing time and lower error rates.

5. Scalability and Flexibility

Intelligent automation should be scalable and flexible to accommodate future growth and changing business needs. Assessing the scalability of the chosen automation solution is crucial to ensure that it can handle increasing workloads and adapt to evolving requirements.

  • Example: A logistics company implements an automated inventory management system. They choose a solution that can easily scale with their business as they expand their operations and handle larger volumes of inventory.

In conclusion, before implementing intelligent automation, organizations should consider the complexity and compatibility of existing systems, the need for employee reskilling, the potential impact on job roles, the return on investment, and the scalability and flexibility of the chosen solution. By carefully evaluating these factors, organizations can make informed decisions and successfully navigate the transition to intelligent automation.

Discover the potential of intelligent automation for various industries

In conclusion, the possibilities for intelligent automation to benefit various industries are vast. Whether you’re in manufacturing, healthcare, financial services, retail, or logistics, embracing this technology can lead to increased efficiency, cost savings, and better customer experiences. Just remember to thoroughly assess the unique needs and challenges of your industry before implementing intelligent automation. With the right approach, it can propel your organization towards a more productive and competitive future. So, what are you waiting for? Start exploring the potential of intelligent automation in your industry today!

Artificial Intelligence

China Restricts Use of AI in Scientific Research

New guidelines issued by the Ministry of Science and Technology in China prohibit the use of generative AI in research declaration materials.



China Restricts Use of AI in Scientific Research

New guidelines issued by the Ministry of Science and Technology prohibit researchers from using Generative AI to directly generate declaration materials for their research or having Artificial Intelligence be listed as a co-author of research results.  

Released on Dec. 21, the research code of conduct applies to researchers in scientific institutions, higher education institutions, medical institutions, and enterprises.

The ministry said the guidelines are a response to new challenges in research data processing and intellectual property rights that have arisen from the rapid development of AI.

The guidelines require all AI generated content to be clearly labeled as such, with information provided as to how the content was generated. 

Zhang Xin, director of the Digital Economy and Legal Innovation Research Center at the University of International Business and Economics in Beijing, believes the guidelines will help promote more responsible use of generative AI in scientific research. 

“If researchers use reference materials generated by AIGC (AI-generated content) without verification, it may not only jeopardize the quality of research outcomes but also intensify the spread of false information, posing various risks to society,” Zhang told Sixth Tone.

The prohibiting of generative AI as a co-author aligns with broader academic practice in China currently, Zhang added.

In September, the Institute of Scientific and Technical Information of China, a research institute under the Ministry of Science and Technology, collaborated with world leading academic publishers Elsevier, Springer Nature, and John Wiley & Sons to release guidelines on the use of AI-generated content in academic papers, which also required clear labeling of such content. 

In August, authorities released an updated draft law on academic degrees specifying that students caught using AI to write dissertations will have their degrees revoked. While the draft has yet to be finalized, some domestic academic journals are already rejecting papers produced with the help of generative AI.  

Further clarification of rules surrounding the use of AI in research is needed as AI becomes an important research tool, said Zhang. 

As AI tools continue to proliferate in China, authorities have been proactive in regulating various applications, including recommendation algorithms and “deepfakes” — fake videos or recordings of people manipulated through AI. 

In April 2023, the Cyberspace Administration of China unveiled specific rules for generative AI, becoming the first in the world to do so.

Meanwhile, major social media platforms such as Douyin, the Chinese version of TikTok, and video streaming platform Bilibili have also started requiring labeling of AI-generated videos.

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AI’s Biggest Challenges Are Still Unsolved

AI’s Biggest Challenges are still unsolved. Three researchers weigh in on the issues that artificial intelligence faces in 2024



AI’s Biggest Challenges Are Still Unsolved

The following essay is reprinted with permission from artificial intelligence The ConversationThe Conversation, an online publication covering the latest research.

2023 was an inflection point in the evolution of Artificial Intelligence and its role in society. The year saw the emergence of Generative AI, which moved the technology from the shadows to center stage in the public imagination. It also saw boardroom drama in an AI startup dominate the news cycle for several days. And it saw the Biden administration issue an executive order and the European Union pass a law aimed at regulating AI, moves perhaps best described as attempting to bridle a horse that’s already galloping along.

We’ve assembled a panel of AI scholars to look ahead to 2024 and describe the issues AI developers, regulators and everyday people are likely to face, and to give their hopes and recommendations.

Casey Fiesler, Associate Professor of Information Science, University of Colorado Boulder

2023 was the year of AI hype. Regardless of whether the narrative was that AI was going to save the world or destroy it, it often felt as if visions of what AI might be someday overwhelmed the current reality. And though I think that anticipating future harms is a critical component of overcoming ethical debt in tech, getting too swept up in the hype risks creating a vision of AI that seems more like magic than a technology that can still be shaped by explicit choices. But taking control requires a better understanding of that technology.

One of the major AI debates of 2023 was around the role of ChatGPT and similar chatbots in education. This time last year, most relevant headlines focused on how students might use it to cheat and how educators were scrambling to keep them from doing so – in ways that often do more harm than good.

However, as the year went on, there was a recognition that a failure to teach students about AI might put them at a disadvantage, and many schools rescinded their bans. I don’t think we should be revamping education to put AI at the center of everything, but if students don’t learn about how AI works, they won’t understand its limitations – and therefore how it is useful and appropriate to use and how it’s not. This isn’t just true for students. The more people understand how AI works, the more empowered they are to use it and to critique it.

So my prediction, or perhaps my hope, for 2024 is that there will be a huge push to learn. In 1966, Joseph Weizenbaum, the creator of the ELIZA chatbot, wrote that machines are “often sufficient to dazzle even the most experienced observer,” but that once their “inner workings are explained in language sufficiently plain to induce understanding, its magic crumbles away.” The challenge with generative artificial intelligence is that, in contrast to ELIZA’s very basic pattern matching and substitution methodology, it is much more difficult to find language “sufficiently plain” to make the AI magic crumble away.

I think it’s possible to make this happen. I hope that universities that are rushing to hire more technical AI experts put just as much effort into hiring AI ethicists. I hope that media outlets help cut through the hype. I hope that everyone reflects on their own uses of this technology and its consequences. And I hope that tech companies listen to informed critiques in considering what choices continue to shape the future.

Kentaro Toyama, Professor of Community Information, University of Michigan

In 1970, Marvin Minsky, the AI pioneer and neural network skeptic, told Life magazine, “In from three to eight years we will have a machine with the general intelligence of an average human being.” With the singularity, the moment artificial intelligence matches and begins to exceed human intelligence – not quite here yet – it’s safe to say that Minsky was off by at least a factor of 10. It’s perilous to make predictions about AI.

Still, making predictions for a year out doesn’t seem quite as risky. What can be expected of AI in 2024? First, the race is on! Progress in AI had been steady since the days of Minsky’s prime, but the public release of ChatGPT in 2022 kicked off an all-out competition for profit, glory and global supremacy. Expect more powerful AI, in addition to a flood of new AI applications.

The big technical question is how soon and how thoroughly AI engineers can address the current Achilles’ heel of deep learning – what might be called generalized hard reasoning, things like deductive logic. Will quick tweaks to existing neural-net algorithms be sufficient, or will it require a fundamentally different approach, as neuroscientist Gary Marcus suggests? Armies of AI scientists are working on this problem, so I expect some headway in 2024.

Meanwhile, new AI applications are likely to result in new problems, too. You might soon start hearing about AI chatbots and assistants talking to each other, having entire conversations on your behalf but behind your back. Some of it will go haywire – comically, tragically or both. Deepfakes, AI-generated images and videos that are difficult to detect are likely to run rampant despite nascent regulation, causing more sleazy harm to individuals and democracies everywhere. And there are likely to be new classes of AI calamities that wouldn’t have been possible even five years ago.

Speaking of problems, the very people sounding the loudest alarms about AI – like Elon Musk and Sam Altman – can’t seem to stop themselves from building ever more powerful AI. I expect them to keep doing more of the same. They’re like arsonists calling in the blaze they stoked themselves, begging the authorities to restrain them. And along those lines, what I most hope for 2024 – though it seems slow in coming – is stronger AI regulation, at national and international levels.

Anjana Susarla, Professor of Information Systems, Michigan State University

In the year since the unveiling of ChatGPT, the development of generative AI models is continuing at a dizzying pace. In contrast to ChatGPT a year back, which took in textual prompts as inputs and produced textual output, the new class of generative AI models are trained to be multi-modal, meaning the data used to train them comes not only from textual sources such as Wikipedia and Reddit, but also from videos on YouTube, songs on Spotify, and other audio and visual information. With the new generation of multi-modal large language models (LLMs) powering these applications, you can use text inputs to generate not only images and text but also audio and video.

Companies are racing to develop LLMs that can be deployed on a variety of hardware and in a variety of applications, including running an LLM on your smartphone. The emergence of these lightweight LLMs and open source LLMs could usher in a world of autonomous AI agents – a world that society is not necessarily prepared for.

These advanced AI capabilities offer immense transformative power in applications ranging from business to precision medicine. My chief concern is that such advanced capabilities will pose new challenges for distinguishing between human-generated content and AI-generated content, as well as pose new types of algorithmic harms.

The deluge of synthetic content produced by generative AI could unleash a world where malicious people and institutions can manufacture synthetic identities and orchestrate large-scale misinformation. A flood of AI-generated content primed to exploit algorithmic filters and recommendation engines could soon overpower critical functions such as information verification, information literacy and serendipity provided by search engines, social media platforms and digital services.

The Federal Trade Commission has warned about fraud, deception, infringements on privacy and other unfair practices enabled by the ease of AI-assisted content creation. While digital platforms such as YouTube have instituted policy guidelines for disclosure of AI-generated content, there’s a need for greater scrutiny of algorithmic harms from agencies like the FTC and lawmakers working on privacy protections such as the American Data Privacy & Protection Act.

A new bipartisan bill introduced in Congress aims to codify algorithmic literacy as a key part of digital literacy. With AI increasingly intertwined with everything people do, it is clear that the time has come to focus not on algorithms as pieces of technology but to consider the contexts the algorithms operate in: people, processes and society.

This article was originally published on The Conversation. Read the original article.

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Artificial Intelligence

Emotional Intelligence Must Guide Artificial Intelligence

Discover the role of AI in patient care. While it has its limitations, it is crucial to combine AI with emotional intelligence for better outcomes.



ai in healthcare

Artificial intelligence — Together they can ensure quality and holistic patient care

By: Arthur Lazarus, MD, MBA

Arthur Lazarus is an adjunct professor of psychiatry and a regular commentator on the practice of medicine.

I don’t understand the brouhaha about artificial intelligence (AI). It’s artificial — or augmented — but in either case, it’s not real. AI cannot replace clinicians. AI cannot practice clinical medicine or serve as a substitute for clinical decision-making, even if AI can outperform humans on certain exams. When put to the real test — for example, making utilization review decisions — the error rate can be as high as 90%.

Findings presented at the 2023 meeting of the American Society of Health-System Pharmacists showed that the AI chatbot ChatGPT provided incorrect or incomplete information when asked about drugs, and in some cases invented references to support its answers. Researchers said the AI tool is not yet accurate enough to answer consumer or pharmacist questions. Of course it’s not. AI is only as smart as the people who build it.

What do you expect from a decision tree programmed by an MBA and not an actual doctor? Or a large language model that is prone to fabricate or “hallucinate” — that is, confidently generate responses without backing data? If you try to find ChatGPT’s sources through PubMed or a Google search you often strike out.

The fact is the U.S. healthcare industry has a long record of problematic AI use, including establishing algorithmic racial bias in patient care. In a recent study that sought to assess ChatGPT’s accuracy in providing educational information on epilepsy, ChatGPT provided correct but insufficient responses to 16 of 57 questions, and one response contained a mix of correct and incorrect information. Research involving medical questions in a wide range of specialties has suggested that, despite improvements, AI should not be relied on as a sole source of medical knowledge because it lacks reliability and can be “spectacularly and surprisingly wrong.”

It seems axiomatic that the development and deployment of any AI system would require expert human oversight to minimize patient risks and ensure that clinical discretion is part of the operating system. AI systems must be developed to manage biases effectively, ensuring that they are non-discriminatory, transparent, and respect patients’ rights. Healthcare companies relying on AI technology need to input the highest-quality data and monitor the outcomes of answers to queries.

What we need is more emotional intelligence (EI) to guide artificial intelligence.

EI is fundamental in human-centered care, where empathy, compassion, and effective communication are key. Emotional intelligence fosters empathetic patient-doctor relationships, which are fundamental to patient satisfaction and treatment adherence. Doctors with high EI can understand and manage their own emotions and those of their patients, facilitating effective communication and mutual understanding. EI is essential for managing stressful situations, making difficult decisions, and working collaboratively within healthcare teams.

Furthermore, EI plays a significant role in ethical decision-making, as it enables physicians to consider patients’ emotions and perspectives when making treatment decisions. Because EI enhances the ability to identify, understand, and manage emotions in oneself and others, it is a crucial skill set that can significantly influence the quality of patient care, physician-patient relationships, and the overall healthcare experience.

AI lacks the ability to understand and respond to human emotions, a gap filled by EI. Despite the advanced capabilities of AI, it cannot replace the human touch in medicine. From the doctors’ perspective, many still believe that touch makes important connections with patients.

Simon Spivack, MD, MPH, a pulmonologist affiliated with Albert Einstein College of Medicine and Montefiore Health System in New York, remarked, “touch traverses the boundary between healer and patient. It tells patients that they are worthy of human contact … While the process takes extra time, and we have precious little of it, I firmly believe it’s the least we can do as healers — and as fellow human beings.”

Spivack further observed: “[I]n our increasingly technology-driven future, I am quite comfortable predicting that nothing — not bureaucratic exigencies, nor virtual medical visits, nor robots controlled by artificial intelligence — will substitute for this essential human-to-human connection.”

Patients often need reassurance, empathy, and emotional support, especially when dealing with severe or chronic illnesses. These are aspects that AI, with its current capabilities, cannot offer. I’m reminded of Data on Star Trek: The Next Generation. Data is an artificially intelligent android who is capable of touch but lacks emotions. Nothing in Data’s life is more important than his quest to become more human. However, when Data acquires the “emotion chip,” it overloads his positronic relays and eventually the chip has to be removed. Once artificial, always artificial.

Harvard medical educator Bernard Chang, MD, MMSc, remarked: “[I]f the value that physicians of the future will bring to their AI-assisted in-person patient appointments is considered, it becomes clear that a thorough grounding in sensitive but effective history-taking, personally respectful and culturally humble education and counseling, and compassionate bedside manner will be more important than ever. Artificial intelligence may be able to engineer generically empathic prose, but the much more complex verbal and nonverbal patient-physician communication that characterizes the best clinical visits will likely elude it for some time.”

In essence, AI and EI are not competing elements but complementary aspects in modern medical practice. While AI brings about efficiency, precision, and technological advancements, EI ensures empathetic patient interactions and effective communication. The ideal medical practice would leverage AI for tasks involving data analysis and prediction, while relying on EI for patient treatment and clinical decision-making, thereby ensuring quality and holistic patient care.

There was a reason Jean-Luc Picard was Captain of the USS Enterprise and Data was not.

Data had all the artificial intelligence he ever needed in his computer-like brain and the Enterprise’s massive data banks, but ultimately it was Picard’s intuitive and incisive decision-making that enabled the Enterprise crew to go where no one had gone before.

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