References for Theme: AI and Medicine
- Altemeyer, Boris
- Atallah, Sam
- Buck, Bryan; Morrow, John
- DeGrave, Alex J; Janizek, Joseph D; Lee, Su-In
- Diebolt, Vincent; Azancot, Isaac; Boissel, François-Henri; Adenot, Isabelle; Balague, Christine; Barthélémy, Philippe; Boubenna, Nacer; Coulonjou, Hélène; Fernandez, Xosé; Habran, Enguerrand; Lethiec, Françoise; Longin, Juliette; Metzinger, Anne; Merlière, Yvon; Pham, Emmanuel; Philip, Pierre; Roche, Thomas; Saurin, William; Tirel, Anny; Voisin, Emmanuelle; Marchal, Thierry
- Ellahham, Samer; Ellahham, Nour; Simsekler, Mecit Can Emre
- "Application of Artificial Intelligence in the Health Care Safety Context: Opportunities and Challenges" (2019)
(p.2) In health care, AI is defined as the mimicking of human cognitive functions by computers. AI has been inspiredby the functioning of biological neurons and includes the basics of sensing, recognition, and object recognition to enable machines to perform as good as or even better than humans. However, with the inherent lack of articulation and generation of insights, AI cannot replace physicians in health care.With no universally applicable rules in health care, AI must be supplemented with physician judgment in many instances. An extensive correlation of history and clinical findings is needed for the diagnosis or monitoring of any disease state. The physician–patient relationship is guided by associative and lateral thinking and can...
- "Application of Artificial Intelligence in the Health Care Safety Context: Opportunities and Challenges" (2019)
- Froomkin, A Michael; Kerr, Ian; Pineau, Joelle
- "When AIs outperform doctors: confronting the challenges of a tort-induced over-reliance on machine learning" (2019)
(p.92) If the medical industry seeks to substitute ML for the work of a medical specialty, such as radiology, we would expect that in the short term radiologists' salaries might drop, blunting the economic pressure to eliminate them. But, as we have argued above, in the longer run, demand could shrink to near zero; meanwhile, those medical students whose choice of specialty is influenced by salary will avoid that specialty.
- "When AIs outperform doctors: confronting the challenges of a tort-induced over-reliance on machine learning" (2019)
- Frownfelter, John
- Goldfarb, Avi; Taska, Bledi; Teodoridis, Florenta
- Goldhahn, Jörg; Rampton, Vanessa; Spinas, Giatgen A
- Granter, Scott R; Beck, Andrew H; Papke, David J
- Gulliford, Fred; Dixon, Amy Parker
- Hindin, David
- Ismail, Azra; Kumar, Neha
- "AI in Global Health: The View from the Front Lines" (2021)
(p.13) In the push for more data, the role of the humans and the work they do are routinely marginalized, even as they provide critical linkages to make the data/AI infrastructures work. HCI and related disciplines have invested consistently in making work visible. This body of work is wide-ranging, from Suchman’s and Gray and Suri’s investigation of invisible work to Sambasivan and Smyth’s description of the “human infrastructure of ICTD” Though not integrated into the digital economy quite yet, there are similar risks to extracting (invisible) labor from the FHWs who are already overburdened on account of responsibilities touching diverse, overlapping...
- "AI in Global Health: The View from the Front Lines" (2021)
- Kanevsky, Jonathan; Corban, Jason; Gaster, Richard; Kanevsky, Ari; Lin, Samuel; Gilardino, Mirko
- Lundberg, Scott M; Nair, Bala; Vavilala, Monica S; Horibe, Mayumi; Eisses, Michael J; Adams, Trevor; Liston, David E; Low, Daniel King-Wai; Newman, Shu-Fang; Kim, Jerry; Lee, Su-In
- Mak, Kit-Kay; Pichika, Mallikarjuna Rao
- Mak, Martin L; Al-Shaqsi, Sultan Z; Phillips, John
- Mina, Ashraf
- "Big data and artificial intelligence in future patient management. How is it all started? Where are we at now? And Quo Tendimus?" (2020)
(p.5) Applying ML concept in computational pathology, which is defined as an approach to diagnosis that incorporates multiple sources of data (e. g. pathology, radiology, clinical, molecular and laboratory operations), presents clinically actionable knowledge to doctors, investigators and patients [31]. The work culture will play a significant role to be more inclusive to allow different experts from all relent areas to work cohesively together including, IT application specialists, medical and scientific experts, data scientists, hardware and software engineers, biostatisticians, and algorithm designers.
- "Big data and artificial intelligence in future patient management. How is it all started? Where are we at now? And Quo Tendimus?" (2020)
- Mukherjee, Siddhartha
- "AI versus MD: What happens when diagnosis is automated?" (2017)
“I think that if you work as a radiologist you are like Wile E. Coyote in the cartoon,” Hinton told me. “You’re already over the edge of the cliff, but you haven’t yet looked down. There’s no ground underneath.” Deep-learning systems for breast and heart imaging have already been developed commercially. “It’s just completely obvious that in five years deep learning is going to do better than radiologists,” he went on. “It might be ten years. I said this at a hospital. It did not go down too well.”
- Ostberg, Nicolai P; Zafar, Mohammad A; Elefteriades, John A
- Pepito, Joseph Andrew; Locsin, Rozzano
- "Can nurses remain relevant in a technologically advanced future?" (2019)
(p.109) Technological breakthroughs occur at an ever-increasing rate thereby revolutionizing human health and wellness care. Technological advancements have drastically changed the structure and organization of the healthcare industry. McKinsey Global Institute estimates that 800 million workers worldwide could be replaced by robots by the year 2030. There is already a robotic revolution happening in healthcare wherein robots have made tasks and procedures more efficient and safer. Locsin and Ito has addressed the threat to nursing practice with human nurses being replaced by humanoid robots. Routine nursing care dictated solely by prescribed procedures and accomplishment of nursing tasks would be best performed...
- "Can nurses remain relevant in a technologically advanced future?" (2019)
- Ramesh, A N; Kambhampati, C; Monson, J R T; Drew, P J
- Robert, Nancy
- Tanabe, Shihori
- Topol, Eric J
- "High-performance medicine: the convergence of human and artificial intelligence" (2019)
(p.49) Besides the productivity and workflow gains that can be derived from AI-assisted image interpretation and clinician support, there is potential to reduce the workforce for many types of back-office, administrative jobs such as coding and billing, scheduling of operating rooms and clinic appointments, and staffing. At Geisinger Health in Pennsylvania, over 100,000 patients have undergone exome sequencing; the results are provided via an AI chatbot (Clear Genetics), which is well-received by most patients and reduces the need for genetic counselors. This demonstrates how a health system can leverage AI tools to provide complex information without having to rely on expansion...
- "High-performance medicine: the convergence of human and artificial intelligence" (2019)
- Tran, Bach Xuan; Vu, Giang Thu; Ha, Giang Hai; Vuong, Quan-Hoang; Ho, Manh-Tung; Vuong, Thu-Trang; La, Viet-Phuong; Ho, Manh-Toan; Nghiem, Kien-Cuong P; Nguyen, Huong Lan Thi; Latkin, Carl A; Tam, Wilson W S; Cheung, Ngai-Man; Nguyen, Hong-Kong T; Ho, Cyrus S H; Ho, Roger C M
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