Artificial Intelligence in healthcare is no longer a distant promise. It is already part of the reality in many Brazilian hospitals. Proof of this is that, according to a survey conducted by Anahp and ABSS, 62.5% of institutions use AI in some form, mainly in service chatbots but also in strategic areas such as information security, clinical decision support, and medical image analysis. More than half of the hospitals that invested in this technology have already seen practical results in their daily operations.
This movement shows how Artificial Intelligence is transforming the way we care for health, bringing faster diagnoses, personalized treatments, and more efficient management processes. But alongside the advances, there are also risks and challenges that must be addressed with ethics, human oversight, and responsibility.
Keep reading to understand the impacts, benefits, and risks of Artificial Intelligence in healthcare, as well as how to apply it responsibly.
What is Artificial Intelligence in healthcare and why does it matter?
Artificial Intelligence in healthcare is the use of algorithms that learn from large volumes of medical data, identify patterns, and support clinical decisions. With AI, digital systems can analyze data and cross-reference information from exams, medical histories, and population databases to reveal invisible patterns with far greater speed.
These solutions already appear in different areas: from medical imaging analysis to clinical decision support and hospital management. This shows that AI in healthcare is no longer a promise for the future. It is already integrated into the daily routines of hospitals and professionals, paving the way for the advances we will explore below.
In South America, nearly two-thirds (64%) of healthcare professionals believe AI will benefit most clinical decisions, showing that the technology is no longer just a promise. It is a reality in physicians’ routines.
What are the advances of Artificial Intelligence in healthcare?
Faster and more accurate diagnoses
At Mayo Clinic, the world’s largest nonprofit integrated medical center and a leader in clinical research, the StateViewer system was developed to analyze FDG-PET brain scans. The technology can identify up to nine types of dementia, including Alzheimer’s, with 88% accuracy. Beyond accuracy, the real impact is time. Diagnoses that once took weeks can now be made in just days, accelerating the start of treatment.
Disease prevention and monitoring
Researchers at the DCC of UFMG, in partnership with USP and the University of Sheffield, created an AI technology that analyzes aerial and facade images such as water tanks and swimming pools to identify areas at high risk of Aedes aegypti infestation. This approach eliminates the need for in-person visits, speeding up the process and helping public health authorities focus preventive actions where they matter most.
Efficiency in hospital management
AI already helps hospitals automate administrative tasks such as scheduling, triage, and billing, freeing professionals’ time for direct patient care. Intelligent systems also support medical record management and clinical report generation, identifying patterns and risks with greater precision. The result is improved logistics, fewer errors, and increased efficiency in patient care.
Support for robotic surgeries and rehabilitation
The combination of AI and robotic surgery has already brought significant advances: greater precision, fewer complications, and shorter hospital stays. AI supports everything from planning to execution, assisting surgeons in critical decisions. Outside the operating room, AI also contributes to rehabilitation. Smart devices and AI-based platforms monitor patients’ progress in real time and adjust exercises and therapies as needed, accelerating recovery.
Challenges in the use of Artificial Intelligence in healthcare
While AI opens new possibilities for medicine, it also brings risks that must be carefully managed. The Pan American Health Organization (PAHO) advises that technology should be developed to maximize benefits while minimizing risks and avoiding pitfalls. The World Health Organization (WHO) also emphasizes that placing ethics and human rights at the center is essential for AI to truly create value in healthcare.
Human autonomy: Doctors and healthcare professionals must remain in charge of clinical decisions. AI supports diagnoses and treatments but cannot replace human judgment, which takes into account individual aspects beyond data.
Privacy and data protection: AI relies on large volumes of sensitive information. Ensuring anonymization, security, and transparency in data handling is essential to maintain patient and institutional trust.
Transparency and bias: Algorithms can reproduce biases and errors present in the datasets used for training. If not auditable and fair, they risk producing inaccurate diagnoses or excluding minority groups.
Limits in psychological and psychiatric care: In recent years, many people have turned to chatbots such as ChatGPT for venting or emotional guidance. However, research shows these systems do not meet clinical standards and may even worsen mental health conditions.
Continuous oversight: Without constant monitoring, AI errors can go unnoticed and replicate at scale. Independent validation, auditing mechanisms, and human review are essential at every stage.
How to ensure the responsible use of Artificial Intelligence in healthcare
Using AI safely in healthcare requires more than just adopting technological tools. It is important to create conditions where innovation and responsibility go hand in hand. In practice, some good practices help ensure this balance:
Continuous training: train professionals to interpret AI results critically and integrate them with clinical knowledge.
Auditable and transparent systems: maintain clear records of how algorithms reach conclusions, allowing for independent review.
Mandatory human oversight: AI can support but never replace medical decision-making. The human perspective remains irreplaceable.
Data governance: establish strong policies to protect sensitive information, ensuring anonymization, consent, and ethical use.
Equity in access: adopt solutions that do not exclude minority populations or underserved regions, promoting digital inclusion.
Pathways to applying Artificial Intelligence in healthcare safely
Adopting AI in healthcare requires more than choosing the right tool. It means aligning technology, people, and processes. Start by ensuring clarity about the objectives of using AI, invest in team training, and maintain human supervision at all stages.
Recent lessons show that integrating high-quality data and ensuring ethical use of information makes all the difference. During the Covid-19 pandemic, for example, strategic data use helped anticipate decisions, optimize resources, and save lives.
If your healthcare organization faces challenges that could be solved with AI, contact BIX. Let’s work together to turn data into solutions that make a real impact.
