Integration of AI into the healthcare system is innovating the ways in which healthcare providers do business and offering new opportunities to advance both patient safety and quality of care. Generally speaking, AI refers to computers capable of making smart decisions independently. For the purpose of this article, we describe AI as a technology that uncovers large volumes of insights from healthcare data, revealing hidden patterns, identifying risks, and improving communication in the medical world.¹

It can aid doctors with more accurate diagnoses, and many new drug developments, along with personalized treatment regimes and patient care management, have already been assisted by AI. It is also used within the EHR system to identify and reduce patient safety risks. As many experts point out, challenges remain for the integration of AI into health care. Individual-level issues may include those affecting awareness of AI, education about AI, and trusting AI. Larger scale issues involve those of regulation and policy, the potential harm from AI in making mistakes. Other technical issues involve usability of systems, performance of tasks, and data privacy and security.¹

Applications of AI in healthcare

AI is one of the mega-emerging technologies in healthcare and has promised to improve patient safety by aiding the early identification and prediction of serious conditions like sepsis, pressure ulcers, postpartum haemorrhage, adverse drug reactions, and deterioration of patients. Inappropriate design, development, and clinical use of AI will be harmful. For instance, an AI system in broad use, supposed to detect sepsis, identified just 7 per cent of 2,552 cases of sepsis that resulted in delaying the antibiotic treatment and also missed catching 1,709 patients detected by the hospital through other means.² 

Medical diagnosis

With medical diagnosis, AI is in its early days of being fully utilized, but progress is being made. For example, a study done in the UK showcased that AI reduced false positives and false negatives in the diagnosis of breast cancer by interpreting mammograms better. In South Korea, AI detected breast cancer with higher sensitivity than radiologists. The majority of the time, AI has proven to be valuable in skin cancer, diabetic eye disease, and heart-related problems. It even outperformed radiologists in diagnosing pneumonia from chest X-rays. Besides, AI methods such as random forests have already been successful in the diagnosis of appendicitis, and similar techniques can be applied for the detection of viral infections such as COVID-19.³

It improves accuracy cuts costs and saves time compared to traditional diagnostic techniques. This technique reduces human errors, hence making the diagnosis quicker and more accurate. In the future, AI may also support medical doctors in decision-making by providing real-time analysis. New ways in which AI can be used for the diagnosis and treatment of ailments by analyzing medical images such as X-rays, CT scans, and MRIs are continuously being discovered by researchers. This could also involve a variety of tasks: the detection of abnormalities, fractures, and tumours, together with precise measurements, are all made possible by AI with machine learning that allows a much faster and more accurate diagnosis.³

Surgery

AI has only recently seen success within the medical domain in tasks related to images, for example: Heart disease prediction from retinal images, skin lesion classification, and detection of breast cancer from mammograms. However, its complete potential in surgery is yet to be realized because of unique challenges in this field. While images represent static data, surgery includes dynamic data from the patient, devices, and team, not to mention the urgency for real-time decisions with clinical acumen. Most especially, training AI in surgery requires quite an amount of labelled data, which is difficult to get. Each of these adds more complexity to integrating AI into surgery.⁴

Surgical data science is a new discipline with the goal of improving both the quality and added value of surgical care through data acquisition, integration, and analysis, always supported by AI. AI has the potential to play a key role in every phase of the surgical procedure, from decision support to context-aware assistance, up to advanced robotic surgery.⁴

Enhancing accuracy in healthcare with AI

One important benefit of AI will come in predictability, finding diseases early by analyzing past data and spotting trends or risk factors, which is especially vital for the treatment of diseases such as cancer. AI also brings about personalized medicine through the analysis of a patient's unique traits and history for unique treatment plans, a departure from healthcare's one-size-fits-all approach. Challenges to using AI in medical imaging would also include data privacy concerns, biases in AI algorithms, and investment in technology and training. Equally, there is a need for clear guidelines and ethical standards to ensure a sense of responsibility in the use of AI in healthcare.⁵

Significant strides have been taken in the use of AI toward precision medicine. For example, a team of researchers created an AI system using deep learning, and convolutional neural networks to interpret kidney tissue samples. This system identified 92% of glomeruli filtering units within the kidneys in close to 1,800 samples at a false-positive rate of 10.4%. Performant independent of the variability in scanning methods, ways of tissue preparation, and staining techniques. This artificial intelligence is promising for the improvement of routine diagnostic procedures of normal and pathologic tissues.⁵

Challenges and limitations of AI in healthcare

Data collection 

Most AI applications in healthcare are restricted because large datasets are not available since patient records are mostly confidential. Data sharing improves performance in AI models, but this has proven quite challenging due to a variety of concerns related to privacy issues and barriers within the organization. There are also security-related issues to consider, such as health records breaches. Health records can be breached, and there have been controversies where AI companies use patient data without explicit consent, an example being Google's DeepMind. There are regulations, much like in Europe, which help shield data but also constrain the actual data that can be used to train AI. Innovation and privacy go hand in glove. Better encryption and technologies, including federated learning, will help strike a balance between innovation and privacy. Finally, quality and structured data is still an issue to be resolved.⁶

Algorithms development

Data collection biases, which mainly involve under-representation, distort AI results through poor predictions. These biases are usually counteracted with various methods such as multi-ethnic training sets; how effective AI will be in combating bias remains to be observed. Some of the issues around bias include overfitting, where the algorithm learns to make irrelevant associations and leakage, a condition where incorrect predictions take place. The problem with the "black box" aspect of AI not being able to explain its decisions raises a growing issue in trusting it for healthcare. With this issue at hand, while noting that full transparency for many of the accepted medical practices does not exist, research continues to be done to make AI more understandable.⁶

Ethical concern

Ethical considerations regarding AI have plagued conceptions since their origin, mainly questions of accountability. That AI systems are a "black box," as relates to healthcare, means that their inner mechanisms are not well understood, and hence when mistakes occur, nobody knows who is responsible. Unlike kiosks and self-service checkouts, the effects brought about by medical AI mistakes are serious, yet nobody quite knows who is responsible: the doctor or the developer. For example, China and Hong Kong prohibit AI from making decisions involving ethical judgments in healthcare. Furthermore, the lack of standardized guidelines only increases the mystery, although some efforts by the FDA and NHS are already in motion to create a set of criteria for the safe and effective application of AI. A public discussion is needed to create a universal ethic.⁶

Future of AI in patient safety

AI in health also brings a lot of questions about fairness, accountability, and transparency. Biases can be born from unequal datasets, and there may be "automation bias" overreliance on AI to the detriment of human judgment. Patient data security and privacy represent another hazard given the major data volumes required by AI. The solution also involves ethical governance, model explainability, and enhancing the skills of healthcare professionals. Regulations differ around the world; Europe has taken a more conservative position. For this reason, education in AI should become part of the medical training process, with a focus on responsible use. It will be necessary to choose the appropriate AI algorithms for clinical applications, after which a series of agreements and approval by regulatory bodies would be an assurance of the safety of patients.⁶

Conclusion

AI could go a long way in improving patient safety by enhancing diagnosis to reduce errors, thus allowing for treatments tailored to the individual. It can analyze large volumes of data for early disease detection and provide more informed decisions about health. But data privacy, algorithmic biases, and the "black box" problem are challenges that indicate careful and responsible integration.

With AI still changing the face of healthcare, the establishment of clear ethical guidelines to address such challenges, ensuring transparency and comprehensive training for healthcare professionals would be quite important. By paving this path, AI can be put to work in the best possible way to improve patient outcomes and transform the healthcare system. This requires various stakeholders to come together, whose collaborative effort ensures AI is implemented with a sense of responsibility and ethics for the ultimate good of the patients and healthcare providers.