AI vs. MD: Should Patients Trust AI-Assisted Doctors

 

 

AI technology is rapidly becoming integral to the medical field, allowing doctors to quickly find relevant information from patient interactions and summarize it in electronic medical records within seconds. AI is already playing a role in 30% of medical decisions, helping doctors analyze medical data more efficiently than any human and aiding in life-or-death decisions.

Experts forecast that medical AI will pervade 90% of hospitals and replace as much as 80% of what doctors currently do. Additionally, the AI healthcare market, valued at USD 11 billion in 2021, is expected to reach USD 187 billion by 2030, indicating significant changes in healthcare.

Several factors have contributed to the increasing application of AI in healthcare, including better machine learning (ML) algorithms, more access to data, cheaper hardware, and the availability of 5G. These advancements have accelerated the pace of change. AI and ML technologies can sift through enormous volumes of health data—from health records and clinical studies to genetic information—and analyze it much faster than humans.

Real-time AI assistants are transforming operating rooms by enhancing precision, reducing complications, and democratizing access to advanced surgical care. From cutting-edge technology to ethical considerations, AI is paving the way for smarter, safer, and more accessible healthcare.

But can we truly rely on machine-driven recommendations? Can a machine fully understand human health? Does this speed translate to trust? Will the AI-assisted healthcare system overcome patients’ distrust of AI and the doctors who use it? Let's discuss...

What Are AI-Assisted Doctors?,

 

AI-assisted doctors are human doctors who use AI tools and technologies to enhance their practice. By leveraging AI, doctors can improve diagnostic accuracy, streamline workflows, identify patterns, and gain insights that support precise and efficient decision-making. AI tools assist in various aspects of healthcare, such as performing surgeries, supporting diagnosis, handling complex medical cases, and aiding clinical decision-making. In recent years, healthcare providers have begun using AI for repetitive clinical tasks, reducing stress on doctors, speeding up treatment, and potentially spotting mistakes.

As of 2024, approximately 30% of doctors worldwide familiar with AI technologies reported using AI for work-related purposes. In India, AI adoption in healthcare is growing rapidly, though specific statistics on usage are not readily available.

 

Commonly Used AI Tools by Doctors and How They Help,

 

Healthcare professionals use various advanced AI tools to enhance their practice:

Merative (formerly IBM Watson Health): Helps medical professionals make better decisions, automate tasks, and enhance productivity by analyzing medical data in real-time.

Viz.ai: Assists in the detection of strokes and other critical conditions by analyzing medical imaging.

ChatGPT: An OpenAI tool that helps medical students, doctors, and patients by providing explanations on medical concepts, treatments, and conditions. It supports healthcare professionals by summarizing relevant literature, drafting emails, managing schedules, and handling other administrative tasks.

Consensus AI: A specialized AI search engine that helps doctors quickly find and understand research papers across various medical topics.

Regard: Assists in diagnosing and managing patients by analyzing clinical data and providing treatment recommendations.

Twill: Helps healthcare providers manage and streamline administrative tasks, such as scheduling and reminders.

AI in healthcare can perform with expert-level accuracy and deliver cost-effective care at scale. For instance, IBM's Watson diagnoses heart disease better than cardiologists, chatbots dispense medical advice for the UK's NHS, and smartphone apps detect skin cancer with expert accuracy. Algorithms identify eye diseases as well as specialized physicians.

Other AI solutions, such as big data applications, machine learning algorithms, and AI technologies like natural language processing (NLP), predictive analytics, and speech recognition, enhance communication with patients. AI can provide specific information about treatment options, enabling meaningful conversations between healthcare providers and patients for shared decision-making. It also helps identify errors in patient self-administration of medication.

 

Examples of Successful AI-Assisted Doctors,

 

Massachusetts General Hospital and MIT Collaboration: Developed AI algorithms for radiology, achieving a diagnostic accuracy rate of 94% in detecting lung nodules, significantly outperforming human radiologists.

Gen AI-Assisted Healthcare: Utilized AI to auto-generate SOAP notes from doctor-patient conversations, improving patient care decisions, optimizing referral processes, and generating drug recommendations.

Philips Healthcare: Implemented AI-enabled camera technology for precise patient positioning in CT scans, reducing radiation dose and improving image quality.

Bengaluru Doctors: Launched an AI-powered chatbot for personalized care, streamlining diagnosis, accurately identifying symptoms, and instantly connecting with specialists.

Royal Free London NHS Foundation Trust: Used AI tool "Streams" developed by DeepMind to detect acute kidney injury (AKI) early, leading to timely interventions and improved patient outcomes.

Although AI-assisted healthcare, ensuring that AI tools are both effective and safe for patient care. One study found that 64% of patients are comfortable with AI-assisted doctors and nurses for round-the-clock access to answers.

We can't ignore the fact that most of the patients are still hesitant to build trust in AI-assisted healthcare professionals. According to a paper published in a journal of consumer research, when healthcare was provided by AI rather than by a human provider, patients were still skeptical about utilizing the service and wanted to pay less for it.

 

Trust in AI-Assisted Diagnosis

 

A survey by Innerbody Research found that 64% of patients would trust a diagnosis made by AI over that of a human doctor. This percentage varies by generation, with 82% of Gen Z, 66% of Millennials, 62% of Gen X, and 57% of Baby Boomers expressing trust in AI diagnoses. On the other hand, a survey by Carta Healthcare revealed that three out of four patients (75%) do not trust AI in a healthcare setting. Additionally, 52% of participants in a University of Arizona study preferred human doctors over AI for diagnosis and treatment.

AI-assisted doctors face mistrust from patients for several reasons:

Lack of Personal Connection: Patients often value the human touch and personal interaction they receive from human doctors. AI systems, despite their efficiency, lack empathy and emotional understanding, which can make patients feel less comfortable.

Over Reliance on AI: Healthcare providers may overly rely on AI recommendations without applying their clinical judgment, resulting in suboptimal patient care. The absence of clear regulatory frameworks for AI in healthcare can also lead to inconsistent and unsafe use of AI tools. Unlike a human doctor, an AI system can diagnose patients without having confidence in its prediction, especially when working with insufficient information.

Concerns About Accuracy: While AI can analyze data quickly and accurately, there have been instances of misdiagnoses due to data quality issues, algorithm biases, and technical limitations. These errors can lead to significant mistrust.

Lack of Understanding: Many patients are not familiar with how AI works and may feel uneasy about relying on a system they don't fully understand. This lack of knowledge can breed fear and skepticism.

Misdiagnosis Due to Data Quality Issues: Predictive algorithms can misdiagnose if they fail to consider important factors, such as a patient's family history. In one case, an AI algorithm's oversight led to a patient's tragic death from cardiac arrest.

Algorithm Bias: AI systems trained on biased data may not perform well for underrepresented groups, exacerbating health disparities. For example, an AI tool biased against Black patients assigned them lower risk scores compared to White patients, potentially leading to unequal care.

Privacy Breaches: AI tools require access to vast amounts of sensitive medical data, raising privacy concerns. A survey by the National Library of Medicine revealed that 80% of respondents were concerned about AI's impact on privacy.

Integration Challenges: Healthcare professionals may struggle to integrate AI tools into their workflows, leading to potential errors. For example, 55% of medical professionals believe AI isn't ready for medical use due to adoption and integration challenges.

Technical Limitations: AI systems can have software bugs, hardware failures, or algorithm limitations, leading to incorrect predictions or recommendations. These technical issues can result in misdiagnosis or inappropriate treatments.

Although specific statistics on the failure rates of AI tools used by doctors are not readily available, it is known that AI projects in healthcare have a high failure rate, with up to 80% failing due to data quality issues, technical limitations, and other factors.

Ultimately, trust in AI-assisted doctors depends on balancing the benefits with the potential risks. As AI technology continues to evolve and become more integrated into healthcare, transparency, education, and rigorous standards will play crucial roles in building patient trust.

 

Examples of AI-Related Negative Incidents in Healthcare,

 

Germany (2019): A study revealed that an AI algorithm used for diagnosing skin cancer had a high error rate, misdiagnosing several cases and causing unnecessary anxiety and treatments.

Australia (2021): An AI tool used in a hospital for predicting patient falls was found to be inaccurate, resulting in inadequate preventive measures and increased fall incidents.

Canada (2020): A predictive analytics tool used to identify patients at risk of sepsis failed to detect several cases, leading to delayed treatment and adverse outcomes.

Japan (2019): An AI system used for analyzing medical images was found to have software bugs, causing incorrect diagnoses and inappropriate treatments.

United States (2019): A study revealed that an AI algorithm used in healthcare was biased against Black patients, assigning them lower risk scores compared to White patients, leading to disparities in care.

United Kingdom (2020): The Royal Free London NHS Foundation Trust faced scrutiny over its use of an AI tool called "Streams" developed by DeepMind. The tool raised concerns about patient data privacy and consent.

India (2021): An AI-based diagnostic tool used in an Indian hospital misdiagnosed several patients with tuberculosis, leading to unnecessary treatments and delays in proper diagnosis.

These examples highlight the potential risks and challenges associated with the belief in AI-assisted doctors and the use of AI in healthcare, emphasizing the need for rigorous testing, validation, and continuous monitoring to ensure patient safety and trust.

 

Thus It Is Important To Maintain A Balancing Trust Between Patients and AI-Assisted Doctors By,

 

Informed Use: Patients should be informed about how AI is being used in their care and the benefits and risks involved.

Collaboration: AI should be used as a tool to support, not replace, human doctors. The combination of AI's analytical capabilities and a doctor's expertise can provide the best care.

Transparency: Healthcare providers should be transparent about the capabilities and limitations of AI tools.

Patient Involvement: Patients should be involved in the decision-making process, ensuring they understand and are comfortable with the role of AI in their treatment.

In conclusion, while there are valid reasons to trust AI-assisted doctors, it is essential to approach this trust with informed caution. AI can significantly enhance healthcare, but it should always be used in partnership with human expertise to ensure the best patient outcomes.