When robot doctors appeared on screen in Star Wars: Episode III – Revenge of the Sith, the situation felt surreal to most movie fans. Today, more than 20 years later, this fiction seems to be turning into reality. But are we really ready to entrust our health and medical decisions to AI systems?
The First AI Hospital in China: A Glimpse into the Future
With the “Agent Hospital” in China, another milestone in medicine has been achieved. Researchers from the university and 40 human medical specialists have trained the robot doctors, which have been carrying out medical history taking, diagnoses, therapies, and follow-up care since 2025.
This pilot project impressively demonstrates what the combination of artificial intelligence and robotics is already capable of today. However, alongside positive factors such as increased efficiency and the relief of medical staff, there are also critical voices warning against an excessive dependence on algorithms.
One of the main arguments against the increased use of AI in hospitals is the lack of human empathy in direct patient interactions. In addition, there is a dependence on training data that cannot always represent all medical edge cases, as well as concerns about incorrect decisions caused by algorithmic bias. Whether these concerns are truly justified will become clear through the long-term insights gained from the “Agent Hospital.”
AI Partial Solutions Already in Use in Many Hospitals
The idea that robot doctors will soon stand at patients’ bedsides in many hospitals or perform surgeries is still a vision of the future. However, artificial intelligence is already being used in medical care today. One reason for this is the enormous strain placed on doctors, nurses, and hospitals themselves.
In addition to cost pressure, the most significant challenges in everyday hospital life include an acute shortage of skilled staff, a rising number of patients, long working hours, and a high administrative burden. Doctors and nursing staff spend a considerable portion of their time on documentation, billing, and organizational tasks instead of focusing on actual patient care.
For this reason, AI for hospitals is being used more and more frequently. It takes over tasks such as digital medical documentation, including the automatic recording of doctor–patient conversations and the structured creation of medical reports and findings. In addition, it can better coordinate billing processes. The automatic assignment of diagnostic and billing codes reduces errors and saves medical staff a significant amount of time, as the manual search for the correct codes is no longer necessary.
AI solutions for hospitals also support patient intake and appointment management. Digital patient registration, for example, reduces waiting times. Automated appointment scheduling and reminder notifications help reduce missed appointments, improve planning, and make more efficient use of medical resources. At the same time, administrative staff are relieved, while patients benefit from smoother and more transparent processes.
Diagnoses with a Lower Error Rate
Misdiagnoses by human doctors occur repeatedly. A frequent cause is the heavy workload and time pressure in everyday hospital life. In addition, many physicians have limited opportunities to analyze large amounts of data simultaneously or to consistently incorporate the latest study results and clinical guidelines into their decisions. As a result, diagnoses made by human doctors often depend on individual experience and subjective judgment.
This is precisely where AI can make an important contribution. The technology analyzes thousands or even millions of comparable cases worldwide within just a few seconds. There is no fatigue, no stress, and no distraction. Instead, data evaluation is carried out according to fixed and standardized criteria. The result is more precise diagnoses, a lower error rate, and a reliable decision-making basis for doctors, who can use AI as a supportive tool without relinquishing medical responsibility.
Outperforming Human Decisions: Medical Examples from Practice
How speed and precise diagnoses can influence chances of recovery is already demonstrated by numerous real-world examples. Researchers at Charité – Universitätsmedizin Berlin and its partners have developed an AI model for tumor diagnostics. The system is able to reliably detect more than 170 types of cancer and has already proven successful in practical testing.
For brain tumors, the model achieves an accuracy of 99.1%. For tumors across all organs, accuracy is approximately 97.8%. The AI uses epigenetic patterns (molecular fingerprints) of tumors, which can be obtained from biopsies or non-invasive samples such as cerebrospinal fluid. In some cases, this enables diagnoses without risky surgical procedures, for example through the analysis of cerebrospinal fluid (liquid biopsy).
Similarly positive results have been observed with an AI tool called “Mia.” It is capable of identifying even the smallest signs of breast cancer at an early stage. Practical tests showed that many of these symptoms had previously gone undetected by human doctors. To date, the tool has analyzed more than 10,000 mammograms and identified eleven cases that radiologists had not previously classified as suspicious. Thanks to early detection, patients were able to begin treatment sooner and significantly improve their chances of a full recovery.
