AI -based diagnosis in the medical world: opportunities, risks, and readiness

Zoling – AI -based diagnosis in the medical world works by analyzing large amounts of medical data, such as radiological scanning results, electronic medical records, to laboratory records. With the support of the Machine Learning algorithm that continues to be trained from millions of case data, AI is able to recognize the pattern of disease quickly and accurately.

This technology has been applied in the detection of breast cancer, brain tumors, to retinal disorders with accuracy levels in several studies beyond the diagnosis of specialist doctors.

However, how safe AI -Based Diagnosis in the Medical World? Although efficient and precise, the AI ​​system cannot be separated from risk. The quality of the diagnosis is very dependent on the quality of the training data used.

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If the data is biased or not representative, then the results can be wrong. In addition, many AI systems are “black box”, which is difficult to explain how the system reaches a decision this is a big challenge in terms of medical transparency.

However, in the midst of the efficiency offered, basic questions arise: How safe is AI -based diagnosis? And is Indonesia ready to welcome the leap of this technology?

AI in the medical world: between innovation and worries

The use of AI in the world of health is not new. In some developed countries, AI -based systems have been used to read radiological results, detect tumors, to help planning surgical actions.

A study published in a journal Natural medicine (2023) shows that AI is able to diagnose lung cancer from CT-scan results with accuracy up to 94.4%beyond the performance of most radiologists.

The same thing was conveyed by researchers at the Karolinska Institute, Sweden, who tested AI in more than 80,000 mammogram results. As a result, the use of AI decreases the workload of radiologists up to 50% without sacrificing diagnosis accuracy.

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Application in Indonesia: The first step towards health digitalization

In Indonesia alone, AI -based diagnosis still faces a number of challenges. The absence of specific national regulations regulates the use of AI in the health sector makes aspects of legal responsibility become gray.

What if there is a diagnostic error? Who should be responsible for developers, service providers, or medical personnel who rely on the system?

In addition, infrastructure readiness is a separate obstacle. Many hospitals and puskesmas in the regions do not yet have technological devices that support AI integration. The internet access that is not evenly distributed also makes the application of this technology cannot be done widely. In fact, if used optimally, AI has a great potential to accelerate the distribution of health services and help overcome the shortage of specialist doctors in remote areas.

Genose C19: AI -Based Local Innovation

During the Pandemic period, Gadjah Mada University (UGM) stole the attention by launching Genose C19AI-based COVID-19 detection device. Only by exhaling into the tool, users can find out the results in Less than 3 minutes. The level of accuracy of this tool even reaches 97%According to the official release of UGM in 2021.

In a press statement delivered by the Head of the Genose Team, Prof. Kuwat Triyana, it was stated that the AI ​​system in Genose was trained using thousands of positive and negative patient breathing data Covid-19. As a result, this tool can be a solution to fast, cheap, and portable detection.

Pilot Project AI in several hospitals

Some major hospitals in Indonesia have begun to adopt this technology in the form of pilot projects, especially in the fields of radiology and dermatology. For example, Cipto Mangunkusumo Hospital (RSCM) and several local healthtech startups have developed AI -based systems for medical image analysis and patient track record.

With a collaboration between the government, medical personnel, and technological developers, AI -based diagnoses in the Indonesian medical world can be an effective future solution – it still prioritizes accuracy, transparency, and protection of patient rights.

Telemedicine’s Awakening and Medical Chatbot

Spike is used Telemedicine During Pandemic, it triggered AI’s adoption more broadly. Based on data from the Katadata Insight Center (2022), applications such as Halodoc, Alodokterand other online consulting services have increased user up to 900% Between 2019 and 2021.

Some applications are now starting to integrate AI -based chatbots for initial consultations. Users only need to enter the perceived symptoms, then the system will provide initial advice on the possibility of disease and steps that can be taken before further consultation with a doctor.

Real benefits, but the risk cannot be ignored

Although AI technology in health offers many advantages, experts remind that the risk potential is also real.

Dr. Fitri Rahmawati, bioetic expert from the University of Indonesia, in her interview with TEMPO.CO emphasized, “AI can indeed detect abnormalities quickly, but can also bring up phenomena overdiagnosis where patients are diagnosed with abnormalities that are actually harmless. “

In addition, another risk that often occurs is Automation Bias namely the tendency of a doctor or patient to trust the results of AI without considering other clinical factors. This can cause a diagnosis that should be correct actually corrected to be wrong because it follows the engine’s advice.

Regulation is unclear: Who is responsible for AI’s mistakes? “

Up to mid -2025, Indonesia does not yet have a special legal umbrella that regulates the use of AI in medical practice. Some aspects such as the limit of responsibility in the event of a diagnostic error, patient data protection, until the algorithm transparency is still a debate.

However, the Ministry of Health has given a statement that AI may not replace the doctor’s rolebut only functions as a tool. In Circular Letter Number HK.02.02/2024 dated 30 December 2024, the Ministry of Health emphasized the importance of human involvement in the clinical decision making process.

Digital access and literacy gaps are a challenge

According to research conducted by the Indonesian Siber Research Institute (LRSI), one of the main obstacles to AI adoption in Indonesia is internet access inequality and low digital literacy Among health workers.

“If in a large hospital it might be ready, but in the area, the signal is often broken. How do you want to run the AI ​​system?” said Dr. Rizal Wahyudi, Head of Puskesmas in West Lampung.

In addition, the lack of quality local medical data also hamper the development of AI systems that are truly accurate and relevant for the Indonesian context.

Conclusion: AI as a companion, not a substitute

Although artificial intelligence offers extraordinary potential in increasing the efficiency and accuracy of diagnosis, AI is not a single solution. This system still requires medical assistance, mature regulations, and adequate supporting infrastructure.

Health technology expert from LIPI, Dr. Bima Kurniawan, states that the future of the medical world is collaborative. “AI does not replace doctors, but strengthen their capacity. It is precisely the danger if we rely on technology without understanding and supervision.”

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