Good communication between clinicians and patients has always been important for quality healthcare. This relationship is based on trust, respect, and empathy. It affects how happy patients are, whether they follow treatment plans, and their overall health. Francis Peabody, a doctor from 1927, once said, “The treatment of a disease may be entirely impersonal; the care of a patient must be completely personal.” This idea still holds true even as AI becomes more common in healthcare.
Many clinicians find it hard to have tough or emotional conversations with patients. This is partly because medical training often teaches doctors to stay emotionally detached to stay objective. Many doctors feel unsure about handling mental and emotional parts of patient care. This can make it harder for them to connect well with patients, even though AI tools are meant to help reduce their workload.
Emotional intelligence means being able to notice, understand, and respond to emotions. Having this skill can help clinicians talk with patients better. It allows doctors to handle complicated talks, listen to patient concerns, and show real care. To get better at this, focused training in communication is very important.
In healthcare education, Objective Structured Clinical Examinations (OSCEs) use actors called standardized patients to help clinicians practice communication skills. A study in Japan looked at fourth-year pharmacy students and found that OSCEs made a big difference. Students improved in controlling their reactions, accepting expectations, managing their facial expressions, communicating feelings, handling power dynamics, keeping good relationships, and managing disagreements. These skills are part of the ENcode, Decode, Control, and REgulate (ENDCORE) framework, which helps measure communication abilities.
After training with standardized patients, students also tried AI-based training. This helped in some general communication areas but did not improve all the skills from the traditional training right away. This shows that AI might be a good extra tool. With longer AI training, students might get better at reacting quickly in real patient talks. Using both kinds of training could help clinicians manage the complex talks they face every day.
Health leaders and teachers in the United States might think about using both standardized patient programs and AI-based training. This mix can help doctors develop how they express themselves and how they adjust their communication. It also helps them handle stress, patient worries, and emotional topics well.
AI systems help by doing boring tasks like keeping records and analyzing data. This should give clinicians more time to care for patients. But more time with patients does not always happen. In many U.S. clinics, appointment times stay the same even though cases get more complicated. Many healthcare places expect doctors to see lots of patients instead of spending extra time talking with them.
AI also brings new problems. It gives doctors more information and treatment options based on large amounts of data and machine learning. Doctors have to explain these AI suggestions carefully. Patients might not trust AI because they don’t understand how it works. AI can seem like a “black box,” where no one knows how it made a decision. This makes patients unsure about trusting AI’s advice.
Because of this, doctors need to explain AI results in ways patients can understand. This requires strong communication skills and being emotionally present. Without good training, doctors may struggle with patient questions. This can weaken the patient-clinician bond.
Healthcare organizations should help their doctors build better communication skills so AI can help without reducing care quality. Bryan Sisk, an expert, points out several ways to do this in the U.S. healthcare system:
AI helps doctors mostly by handling administrative tasks and front-office work. This is important for clinic administrators and IT managers who want smooth operations and easier patient access.
A company called Simbo AI uses AI to automate phone calls and answering services. It helps patients reach clinics, sets appointments, and provides common info. This reduces waiting times and makes patients happier. It also lessens the work for reception staff, letting them focus on harder or more sensitive issues.
Inside clinical work, AI tools like Microsoft’s Dragon Copilot help by turning spoken words into written notes. This cuts down the time doctors spend on paperwork and helps reduce burnout. It also frees up more time for direct care.
AI also helps by predicting patient risks and guiding decisions. DeepMind’s AI can read eye scans to spot diseases like an expert. In the UK, AI stethoscopes can find heart problems quickly.
By automating these tasks, AI lets clinicians spend more time focused on patients. But these systems need to fit well with doctors’ work; otherwise, they can cause delays or confusion. This has been a problem in many electronic health record (EHR) and AI setups.
Researchers like Adewunmi Akingbola warn that AI’s “black box” style and biased training data might hurt patient care. This could make health inequalities worse, especially for groups already facing challenges in U.S. healthcare. If AI suggests treatments based on limited or unfair data, it might keep problems going.
Being clear and honest about AI’s role is key to keeping patient trust. Doctors need support so they can explain AI results fairly and openly. This shows why communication skills and emotional intelligence are so important.
AI is changing healthcare, but clinicians are still very important in patient care. Using AI in medical teaching and practice must focus on helping doctors keep strong communication skills. Training with standardized patients has already shown good results in many areas. AI training can add to this by giving many chances to practice and receive feedback.
A 2025 survey by the American Medical Association (AMA) showed that 66% of U.S. doctors use AI tools, and 68% think AI helps patient care. But many doctors still feel unsure about explaining AI advice to patients. This shows a clear need for more communication training designed for the AI age.
Training that combines old methods with AI-based lessons can prepare doctors to use AI’s benefits while keeping the personal touch patients expect. These classes should teach both how AI works and how to talk about care choices with patients.
Artificial intelligence can help make healthcare more accurate and efficient. But it works best when clinicians connect well with patients, handle emotional talks, and build trust in AI results. Focused training on communication and emotional skills, along with smart AI use, can help medical practices in the U.S. improve patient-doctor relationships in this changing healthcare world.
AI can off-load tedious administrative and data analysis tasks, potentially allowing clinicians more time to engage relationally with patients and provide personalized care, enhancing shared decision making and communication.
The key assumptions are that AI will off-load tedious work, clinicians will use the extra time for relationship building, and clinicians have the skills to engage meaningfully with patients using richer data.
AI could analyze vast clinical data faster and more accurately, reduce manual charting through voice recognition, and streamline ordering tests, thereby reducing clinicians’ administrative burden and allowing focus on patient interaction.
Structural barriers like stable visit lengths with increased complexity, business-driven pressures to see more patients, and personal barriers such as discomfort with emotional communication may limit time spent on relationship building.
More treatment options and data increase interpersonal demands, requiring clinicians to educate patients extensively, explain AI decisions (often opaque), and spend more time on shared decision making.
Lack of confidence in handling difficult conversations, avoidance of psychosocial topics, discomfort with emotional presence, and cultural or training emphasis on emotional detachment can hinder trust building.
Through selective medical school admissions emphasizing empathy, ongoing training in communication and relationship-building, addressing burnout, and providing feedback on interpersonal skills to maximize AI benefits.
Healthcare systems might increase patient volume to maximize efficiency gains, reducing individual visit times, which can diminish opportunities for meaningful patient-clinician engagement and trust formation.
Patients may distrust AI due to its ‘black-box’ nature, requiring clinicians to explain and vouch for AI recommendations to maintain confidence and trust in treatment decisions.
While AI can enhance care accuracy and efficiency, preserving the healing patient-clinician relationship through trust, respect, and personal connection remains critical; all stakeholders should intentionally maintain this balance in AI integration.
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