Artificial Intelligence in Clinical Diagnosis

Kulkarni, Prathit A.; Singh, Hardeep

doi:10.1001/jama.2023.11440

Cited by 33 publications

(16 citation statements)

References 9 publications

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“…Moreover, DL methods may improve diagnostic accuracy by eliminating subjectivity, and may provide diagnoses in a few seconds [53]. Various studies have been published on this topic so far, however most algorithms are lacking adequate validation and generalizability and are currently limited to research purposes [58][59][60].…”

Section: Formulation Of Clinical Diagnosesmentioning

confidence: 99%

“…However, the solution may be round the corner. In fact, AI-powered technologies may not only be of aid in providing summaries of large medical records; filtering and drafting medical notes and e-mails; generating laboratory and prescription orders; cataloguing diseases according to their ICD and scheduling appointments; they may do so at a greater speed than humans and with greater accuracy [59,97]. This would enable physicians to have more time actively interacting with their patients, while reducing working hours, and ultimately reducing their risk of burnout [98].…”

Section: Role In Reducing the Burden Linked To Administrative Workmentioning

confidence: 99%

“…LLMs specifically trained on medical guidelines and on the most recent literature are being developed to overcome such pitfalls [113,114]. Moreover, at the present moment AI technology is still not able to account for certain real-world aspects of making a clinical diagnosis or solving a medical case such as sorting through the myriad of symptoms and information a patient may report and deducing the most salient, as well as understanding nuances linked to the patient's emotional wellbeing [59]. Such drawbacks of AI may have more serious repercussions on patients' health if clinicians who use these technologies in their clinical practice are not aware of their limitations, and as such do not verify their veracity [5].…”

Section: Limitations and Challenges Of Aimentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse than Humans?

Cetera,

Tozzi,

Chiappa

et al. 2024

Preprint

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Artificial intelligence (AI) is experiencing advances and integration in all medical specializations, and this creates excitement but also concerns. This narrative review aims at a critical assessment of the state of the art of AI in the field of endometriosis and adenomyosis. By enabling automation, AI may speed up some routine tasks, decreasing gynecologists’ risk of burnout, as well as enabling them to spend more time interacting with their patients, increasing their efficiency and patients’ perception of being taken care of. Surgery may also benefit from AI, especially through its integration with robotic surgery systems. This may improve the detection of anatomical structures and enhance surgical outcomes by combining intra-operative findings with pre-operative imaging. Not only, AI promises to improve the quality of care by facilitating clinical research and through the introduction of decision support tools that can enhance diagnostic assessment and predict treatment effectiveness and side effects, as well as reproductive prognosis and cancer risk. However, concerns exist regarding the fact that good quality data used in tool development and compliance with data sharing guidelines are crucial. Also, professionals are worried AI may render certain specialists obsolete. This said, AI is more likely to become a well-liked team member rather than a usurper.

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Section: Formulation Of Clinical Diagnosesmentioning

confidence: 99%

Section: Role In Reducing the Burden Linked To Administrative Workmentioning

confidence: 99%

Section: Limitations and Challenges Of Aimentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse than Humans?

Cetera,

Tozzi,

Chiappa

et al. 2024

Preprint

View full text Add to dashboard Cite

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“… 2 Rather than being an ordinary additional tool, it is a complex broad concept that has the potential to reshape the structure and establishment of modern medicine and drastically empower both pediatricians and patients/parents while bringing some significant clinical, technical, ethical, and legal challenges as expected from any advancement. 3 , 4 …”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Pediatrics: Learning to Walk Together

Demirbaş,

Yıldız,

Saygılı

et al. 2024

Turkish Archives of Pediatrics

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In this era of rapidly advancing technology, artificial intelligence (AI) has emerged as a transformative force, even being called the Fourth Industrial Revolution, along with gene editing and robotics. While it has undoubtedly become an increasingly important part of our daily lives, it must be recognized that it is not an additional tool, but rather a complex concept that poses a variety of challenges. AI, with considerable potential, has found its place in both medical care and clinical research. Within the vast field of pediatrics, it stands out as a particularly promising advancement. As pediatricians, we are indeed witnessing the impactful integration of AI-based applications into our daily clinical practice and research efforts. These tools are being used for simple to more complex tasks such as diagnosing clinically challenging conditions, predicting disease outcomes, creating treatment plans, educating both patients and healthcare professionals, and generating accurate medical records or scientific papers. In conclusion, the multifaceted applications of AI in pediatrics will increase efficiency and improve the quality of healthcare and research. However, there are certain risks and threats accompanying this advancement including the biases that may contribute to health disparities and, inaccuracies. Therefore, it is crucial to recognize and address the technical, ethical, and legal challenges as well as explore the benefits in both clinical and research fields.

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“…Optimists see value in an instantaneous review of a voluminous medical record, machine learning interpretation of diagnostic scans, and analysis of diverse data ranging from published literature to patient breathing patterns. Skeptics, on the other hand, point out that definitive diagnosis often involves a careful and nuanced history and bedside physical examination, neither of which come naturally to a machine, and neither of which are adequately quantified by human clinicians to be digitally digested . What is rarely acknowledged, however, is that the ability of AI tools (such as LLMs) to assist with medical diagnosis is not merely an answer to be discovered—it is an outcome to be influenced through policy.…”

mentioning

confidence: 99%